scenario questions

A company uses AWS Glue to run ETL jobs that prepare data for machine learning. The source data in S3 has a schema that evolves over time (new columns are added occasionally). The Glue job schema is defined as a fixed schema in the job script. After an update to the source data, the Glue job fails with an error about mismatched schemas. How should the data engineer modify the data preparation process to handle schema evolution?

A company is building a CI/CD pipeline for ML models using AWS CodePipeline and SageMaker. The pipeline should include steps to automatically retrain, evaluate, and deploy models. Which THREE components are essential for this pipeline? (Choose three.)

A team needs to deploy a model that has compliance requirements to log all inference requests and responses for auditing. The model will be served using a real-time endpoint. How can they achieve this without custom code?

A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset for training. The dataset contains a column with missing values. Which built-in transform in Data Wrangler can be used to handle missing values by replacing them with the mean of the column?

A machine learning engineer is using Amazon SageMaker Data Wrangler to prepare a dataset with a categorical feature that has over 5,000 distinct values (high cardinality). The engineer needs to transform this feature into a form suitable for a gradient boosting model while preserving as much information as possible. Which transform should be applied?

A team used the above config to create an endpoint. However, the endpoint fails to invoke because of a "ModelError". What is the most likely cause?

An MLOps team is designing a SageMaker Pipeline to automate model retraining. The pipeline must: (1) run training only if new training data is available, (2) register the model in SageMaker Model Registry only if evaluation metrics exceed a threshold, (3) deploy the approved model to a staging endpoint automatically. Which THREE steps should they include? (Choose THREE.)

A fraud detection model is being trained on imbalanced data. The team wants to ensure the model's precision is optimized. Which objective metric should be used in automatic model tuning?

A company uses SageMaker endpoint with production variants for canary deployments. The team wants to gradually shift traffic from the old model variant (variant A) to the new model variant (variant B) over a period of 10 minutes. After the shift, if the new variant's error rate increases by more than 5%, they want to roll back automatically. Which solution meets these requirements with minimal manual intervention?

A data scientist wants to compare the performance of two model versions (V1 and V2) in production by splitting traffic between them. They want to gradually increase the percentage of traffic to the new version while monitoring metrics. Which SageMaker feature enables this?

A team is training a PyTorch model using SageMaker. They have a custom training script that requires specific Python packages not included in the SageMaker default PyTorch container. Which approach should they use?

A data scientist needs to deploy a single ML model that will serve real-time predictions with low latency (under 10 ms) for a high-traffic web application. The model fits in memory and requires GPU acceleration. Which SageMaker inference option is MOST suitable?

A team is building a machine learning model for natural language processing using SageMaker BlazingText. The data preparation step must format the training data correctly. What format does BlazingText require for supervised text classification?

During a blue/green deployment of a SageMaker endpoint, the team notices that traffic is not being fully shifted to the new variant after the update. The endpoint has two variants with equal initial weights (50% each). The team wants to shift 100% traffic to the new variant. What is the most likely cause?

A machine learning team is preparing numerical features for a linear regression model. Feature 'A' ranges from 0 to 1000, feature 'B' ranges from 0 to 1, and feature 'C' ranges from -10000 to 10000. The team wants to ensure that feature scales do not affect the model's coefficients and that the features are bounded between 0 and 1. Which transformation should they apply?

A company wants to implement a retraining pipeline that automatically triggers when SageMaker Model Monitor detects data drift. The retraining job should use the latest approved pipeline version in SageMaker Pipelines. Which approach meets these requirements?

A company uses AWS Glue ETL jobs to transform data for machine learning. They have a dataset with a column 'income' that is heavily right-skewed. Which transformation should be applied to make the distribution more Gaussian-like?

A data scientist is using SageMaker Autopilot to automatically build a binary classification model on a balanced dataset. They want to understand the relationship between the input features and the model predictions. Which feature in SageMaker Autopilot should they use?

A machine learning engineer deploys a model to an Amazon SageMaker endpoint with data capture enabled. The endpoint uses a production variant with initial instance count of 2. After a week, they notice that the captured data is not being sent to the specified Amazon S3 bucket. The IAM role used by the endpoint has the following policy attached. What is the MOST likely reason for the failure?

A data scientist is using Amazon SageMaker Feature Store to manage features for a fraud detection model. The model needs to be updated hourly with new features from streaming data, and historical features must be available for training. Which THREE actions are necessary to set up this pipeline? (Choose THREE.)

A company has a model that receives low traffic but needs to handle sudden spikes. Which deployment option is most cost-effective?

A data engineer needs to ingest streaming clickstream data from a website into an S3 data lake for ML training, with the ability to run real-time aggregations before storage. Which combination of AWS services meets these requirements?

A machine learning team is training a large natural language processing model on Amazon SageMaker using the SageMaker Hugging Face container. The training job runs on multiple instances and uses Managed Spot Training to reduce costs. However, the job frequently gets interrupted by Spot interruptions, causing long training times. What should the team do to mitigate this issue?

A data engineer is processing a large dataset in Amazon S3 with AWS Glue ETL. The dataset contains timestamps in multiple time zones. The engineer needs to create a feature for hour-of-day consistent across all records. Which approach ensures correctness?

A data scientist notices that a SageMaker endpoint is returning HTTP 5XX errors under high load. The endpoint uses a single ml.m5.large instance. The team wants to reduce these errors without changing the instance type. What is the most cost-effective step?

A company uses SageMaker Model Monitor for data quality. They notice that monitoring jobs are failing intermittently with constraint violations. Upon review, they see that some features have different data types in production compared to the baseline (e.g., string instead of integer). Which type of drift is this?

A company uses SageMaker Clarify to detect bias during training. They want to ensure that the trained model does not rely on a sensitive attribute like gender. Which Clarify feature should they configure?

Refer to the exhibit. A SageMaker Pipeline fails with 'Invalid output reference' at the TrainingStep. What is the most likely cause?

A team is training a PyTorch model using SageMaker with a custom training script. They want to track hyperparameters and metrics across multiple experiments. Which service should they use?

A machine learning engineer is deploying a custom PyTorch model to a SageMaker endpoint for real-time inference. The model requires GPU acceleration. The engineer wants to minimize latency and cost. Which THREE actions should the engineer take? (Select THREE.)

A data scientist wants to track the lineage of models, datasets, and training jobs in SageMaker. Which SageMaker feature should they use to capture these relationships as artifacts and actions?

A machine learning engineer sees the above error in Amazon CloudWatch Logs for a SageMaker endpoint. What is the most likely cause?

A data engineer is preparing a dataset for a classification model. The dataset contains duplicate rows. Which TWO approaches are appropriate to handle duplicates in AWS? (Choose 2.)

A data scientist needs to label a large dataset of product images for a classification model. They want to reduce labeling costs by prioritizing uncertain samples. Which Amazon SageMaker Ground Truth feature should they use?

A data science team wants to automate the retraining of a model when data drift is detected. Which TWO AWS services should they use in combination to achieve this? (Choose TWO)

A team wants to deploy a new model using a canary deployment strategy on SageMaker. Which TWO configurations are necessary? (Choose two.)

A data scientist needs to split a time-series dataset into training and testing sets while avoiding data leakage from future values. Which splitting technique should the data scientist use?

A machine learning team is preparing a dataset for a text classification model. The dataset contains customer reviews. The team wants to convert the text into numerical features while reducing the impact of common words and capturing word importance. Which THREE techniques should the team apply? (Select THREE.)

A company is adopting Amazon SageMaker Pipelines to automate their ML workflow. They want to choose three key benefits that SageMaker Pipelines provides over traditional manual scripts and ad-hoc steps. Which THREE benefits are correct?

A company is using SageMaker Model Registry to manage model versions. They want to automatically deploy the latest approved model to production after retraining. Which approach is best?

A data scientist needs to ensure that the same train/test split is used across multiple experiments for reproducibility in SageMaker. Which approach should they take?

A company is using an AWS Step Functions state machine to orchestrate a multi-step ML deployment. The workflow includes: training a model, evaluating it, registering the model, and deploying to a staging endpoint. They need to implement an approval gate before deploying to production. Which THREE components are necessary to achieve this? (Choose three.)

A company stores its model training data in Amazon S3. To meet compliance requirements, all data in transit between the S3 bucket and SageMaker must be encrypted. What should the company enforce?

A company is running multiple SageMaker endpoints for different models, each serving a separate business unit. The total cost is growing rapidly. The ML engineering team wants to reduce costs without sacrificing performance or isolation. They are considering either consolidating models into a Multi-Model Endpoint (MME) or onto a Multi-Container Endpoint (MCE). The models vary in size from 100 MB to 5 GB, and traffic patterns are unpredictable. Which recommendation is MOST appropriate?

A company uses SageMaker Autopilot to build a binary classification model. The generated leaderboard shows an ensemble model as the best candidate. The team needs a model that can be deployed for real-time inference with latency < 10ms. What should they do?

In SageMaker Data Wrangler, you have a flow that imports data from Amazon S3 and needs to join it with a table from Amazon Redshift. The data volumes are large (hundreds of GB). Which approach is most efficient within Data Wrangler?

A machine learning team is building a fraud detection model. They have a dataset with a categorical feature 'merchant_id' that has over 10,000 unique values. Which feature engineering technique should they apply to 'merchant_id' to reduce dimensionality while retaining predictive power?

A company wants to automate remediation when a SageMaker endpoint's latency exceeds a threshold for more than 5 minutes. The team needs to be notified and a Lambda function should be invoked to scale up the endpoint. Which combination of services should be used?

A data scientist is preparing text data for a sentiment analysis model. The dataset contains customer reviews with a high proportion of misspellings, slang, and emojis. The scientist wants to preprocess the text to reduce noise while preserving semantic meaning. Which combination of preprocessing steps is MOST effective?

A data scientist is preparing a large dataset (50 GB) for training a TensorFlow model on SageMaker. The dataset consists of many small CSV files. Training is slow due to I/O bottlenecks. Which data preparation strategy most effectively accelerates training?

A data scientist is using SageMaker Autopilot for a regression problem. They want to see which data preprocessing steps Autopilot applied. Which TWO sources can they use to find this information?

A data scientist is using SageMaker built-in Image Classification algorithm on a dataset with 1000 classes. The training is very slow. They want to speed it up without sacrificing accuracy. Which instance type and training configuration is MOST appropriate?

A data scientist wants to version and manage trained models, require approval before deployment, and enable cross-account deployment. Which SageMaker feature provides these capabilities?

A company uses SageMaker Pipelines to orchestrate their ML workflow. They notice that if a pipeline step fails due to a transient error (e.g., a brief network issue), the entire pipeline fails and they must manually rerun from the beginning. They want to automatically retry failed steps a few times before failing. What should they do?

A team is training a large language model on SageMaker using PyTorch with data parallelism. The model is too large to fit on a single GPU. Which distributed training strategy should they use to split the model across multiple GPUs?

A team is fine-tuning a foundation model using LoRA. They want to reduce memory usage during training. Which technique should they combine LoRA with to further reduce memory?

A data engineer needs to prepare a large dataset for machine learning. The data is stored in an Amazon RDS MySQL database and needs to be transformed and moved to an S3 bucket in Parquet format for use with SageMaker. Which AWS service is most suitable for this extraction, transformation, and loading (ETL) task?

A machine learning engineer wants to set up a retraining pipeline that triggers when model quality degrades. Which TWO components are essential for this automated retraining pipeline? (Select TWO)

A company uses Amazon SageMaker Pipelines for automated retraining. The pipeline includes a processing step that runs a Python script. The script uses the boto3 library to call an AWS service, but the calls are being throttled. What is the MOST effective way to address this within the pipeline?

A machine learning engineer wants to monitor a deployed model for data drift. Which SageMaker feature should they use to automatically detect drift in the input data distribution compared to the training data baseline?

A team uses SageMaker Pipelines for CI/CD. The training step fails due to insufficient memory. How to fix without rewriting code?

A data science team needs to deploy a frequently updated PyTorch model for real-time inference. The model is retrained weekly and versioned using SageMaker Model Registry. Which deployment strategy minimizes downtime and allows easy rollback?

A company uses an Amazon SageMaker endpoint for real-time inference. The security team requires that all traffic between the endpoint and the client application be encrypted in transit. Which configuration ensures this?

A company is deploying a machine learning model using SageMaker hosting. They need to support multiple versions of the model for A/B testing. Which TWO actions are required to set up the A/B test? (Choose two.)

A company is using Amazon SageMaker Ground Truth to create a labeled dataset for object detection in images. The team wants to minimize labeling costs while maintaining high accuracy. Which feature should they use to achieve this?

A data scientist wants to use SageMaker Clarify to analyze bias during training of a binary classification model. Which TWO types of bias metrics can SageMaker Clarify compute? (Select TWO.)

A healthcare company is deploying a model for predicting patient outcomes. The model must be deployed across multiple AWS accounts to meet compliance requirements. Each account has its own Amazon SageMaker endpoint. The company wants to centralize monitoring of model performance without exposing data across accounts. Which solution should the company use?

A machine learning engineer is setting up automated retraining for a model using SageMaker Pipelines. The pipeline should trigger when a data drift alert is received from Model Monitor. Which event source should the engineer use to initiate the pipeline?

An ML engineer needs to orchestrate a multi-step workflow that includes data preprocessing on Spark, model training on SageMaker, and deployment to a production endpoint. They require tight integration with other AWS services and the ability to add custom logic. Which AWS service should they use alongside SageMaker?

A company is training a deep learning model for object detection using SageMaker. The training is very slow and the GPU memory is insufficient for the batch size. The team wants to scale across multiple GPUs efficiently. Which THREE actions should they take? (Choose THREE.)

An ML engineer is using Amazon SageMaker Feature Store to manage features for a fraud detection model. The team needs to retrieve point-in-time feature values for training, ensuring no future data leaks. Which THREE configurations are required? (Choose 3)

A data scientist is using SageMaker to train a custom PyTorch model for image classification. They want to use SageMaker Debugger to detect training issues. Which TWO built-in rules are most relevant for detecting common training problems? (Select TWO.)

A machine learning engineer is using Amazon SageMaker Debugger to monitor a training job for a deep neural network. They receive a rule alert indicating 'exploding gradients'. Which action should they take to address this issue?

A data scientist is training a deep learning model using SageMaker and wants to use distributed training across multiple GPUs to reduce training time. Which TWO actions should the scientist take to configure distributed training? (Select TWO.)

A machine learning engineer is configuring auto-scaling for a SageMaker real-time endpoint. The endpoint is expected to have steady traffic during business hours and low traffic at night. The engineer wants to minimize costs by scaling in during low traffic, but the model container has a long start-up time (about 5 minutes). Which scaling policy should the engineer use to prevent request drops during sudden traffic spikes?

A company is preparing a large dataset for a SageMaker built-in XGBoost model. The dataset has missing values in both numeric and categorical features, and some categorical features have high cardinality. Which THREE data preparation steps should the company take to optimize model performance? (Choose three.)

A machine learning engineer needs to optimize a trained TensorFlow model for deployment on edge devices with limited compute. Which SageMaker feature should they use to compile the model for target hardware?

A team is preparing text data for a natural language processing (NLP) model. They have a corpus of customer reviews. Which THREE preprocessing steps are essential to reduce noise and improve model performance?

A machine learning engineer is using Amazon SageMaker Data Wrangler to create a data preparation pipeline. The pipeline includes multiple transforms such as handling missing values, scaling, and encoding. The engineer wants to export the prepared data directly to a feature group in Amazon SageMaker Feature Store for reuse in training and inference. Which export option should the engineer choose?

A company stores training data in Amazon S3 and uses Amazon SageMaker for model training. They need to ensure data is encrypted at rest. Which THREE encryption options are supported by SageMaker for data stored in S3? (Choose THREE.)

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

A machine learning engineer deploys a multi-model endpoint using SageMaker. They need to track which model version was used for each inference request for compliance purposes. Which service should they integrate to capture this lineage?

A data engineer needs to assess the quality of a dataset containing customer information. The dataset has missing values, outliers, and duplicate records. Which TWO AWS services can be used to perform data quality assessment? (Select TWO.)

A machine learning engineer is deploying a model using AWS Lambda for inference. The model is a small scikit-learn classifier with a size of 50 MB. The Lambda function is invoked by an API Gateway REST API. The engineer notices that cold starts are causing high latency. Which action would most effectively reduce cold start latency without increasing costs significantly?

A data engineer needs to provide the data science team with access to various data sources for machine learning. The team uses Amazon SageMaker Studio. Which TWO data sources can be accessed directly from SageMaker Studio notebooks without additional infrastructure? (Choose two.)

A machine learning team needs to deploy a model that was built using scikit-learn. They want to use SageMaker for hosting. Which approach should they take?

A startup wants to deploy a model that has variable traffic patterns, with some periods of no traffic and occasional spikes. They want to pay only for what they use and do not want to manage instances. Which SageMaker inference option should they choose?

A data scientist is performing feature selection for a high-dimensional dataset with 10,000 features. The goal is to identify a small subset of features that are most predictive of the target variable. The scientist wants to use a method that is computationally efficient and can handle feature interactions implicitly. Which feature selection method is most appropriate?

A company wants to automatically trigger model retraining when SageMaker Model Monitor detects data drift. Which TWO services should they integrate to achieve this automation? (Choose two.)

A data scientist is using SageMaker Experiments to track multiple training runs. They want to compare different hyperparameter configurations and visualize the impact on model accuracy. What should they use to track hyperparameters?

A machine learning engineer at a retail company is monitoring a production model that predicts inventory demand. The model's prediction accuracy has dropped significantly over the past week. The engineer checks the model's input data and notices a new product category was introduced with a different distribution. Which concept is most likely causing the performance degradation?

A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset for a binary classification model. The scientist wants to detect potential bias in the data before training. The dataset includes a sensitive attribute 'gender'. Which TWO actions should the scientist take in Data Wrangler to analyze and mitigate bias? (Select TWO.)

An ML team trained a model using SageMaker and stored the model artifacts in S3 with server-side encryption using AWS KMS (SSE-KMS). They need to deploy the model to a SageMaker endpoint that uses a different KMS key for inference data encryption. What must they do to ensure the endpoint can decrypt the model artifacts?

A data scientist is training a binary classification model using a dataset that has a severe class imbalance (90% negative, 10% positive). Which technique should be used to address the imbalance during model training?

A team is using Amazon SageMaker for feature engineering. They have a dataset with a column 'TransactionDate' in string format (e.g., '2023-01-15 10:30:00'). They need to create features: year, month, day, hour, and day_of_week. What is the most efficient way to do this in a SageMaker processing job?

An ML team is using SageMaker Automatic Model Tuning to optimize hyperparameters for a neural network. They want to prioritize exploration of the hyperparameter space early in the tuning process. Which strategy should they choose?

A company wants to deploy a model on SageMaker serverless inference. Which TWO of the following are limitations of serverless endpoints compared to real-time endpoints? (Choose two.)

A team is preparing text data for sentiment analysis. They have a large corpus of customer reviews. They want to convert the text into numerical features using a technique that captures word importance relative to the whole corpus. Which feature extraction method should they use?

A company is building a real-time fraud detection system. They need to store features with historical context for model training and also support low-latency lookups for inference. Which THREE configurations should they set up in Amazon SageMaker Feature Store? (Select THREE.)

A machine learning team is using SageMaker Studio for model development. They need to restrict all internet access from Studio notebooks and ensure that all data stays within a VPC. Which configuration should they use?

A machine learning team needs to ensure that all model training and inference jobs within SageMaker Studio run in a private network without internet access. The team also requires that inter-container traffic within the same training job be encrypted. Which configurations should they combine?

A company uses Amazon SageMaker Ground Truth to label a dataset for object detection. To reduce labeling costs, they want to use active learning. Which configuration should they set up in Ground Truth?

A company is using SageMaker Autopilot to automatically build a regression model on a dataset. They want to understand which features are most important for the model's predictions. Which feature of Autopilot can provide this insight?

A company uses Amazon SageMaker Ground Truth to create a labeled dataset. They want to monitor the accuracy of human labelers during the labeling process. Which metric should they track?

A data science team deploys a PyTorch model on Amazon SageMaker for real-time inference. The model requires GPU for low latency. Which instance type is MOST cost-effective while meeting the GPU requirement?

A data engineer needs to perform feature selection on a dataset with 500 numeric features to train a regression model. The engineer wants to remove features that are redundant or have low predictive power. Which TWO techniques should the engineer consider? (Select TWO.)

A machine learning engineer wants to encrypt model artifacts stored in Amazon S3. The artifacts are created and used by SageMaker training jobs and endpoints. What is the simplest way to ensure encryption at rest?

A data scientist is working on a binary classification problem and wants to use AWS Glue for data preparation. The dataset has missing values in several numeric columns. Which imputation strategy is MOST appropriate for the scientist to apply in AWS Glue ETL?

An organization needs to prepare data for a churn prediction model. They observe missing values in 15% of the records for a numerical feature 'usage_minutes'. The data is not missing at random (NMAR). Which imputation strategy is MOST robust?

A data science team uses SageMaker to train and deploy models. They need to track model lineage, including datasets, training jobs, and model versions, to ensure reproducibility. Which THREE actions should they take? (Select THREE)

A data engineer is using Amazon SageMaker Processing to run a data preprocessing script on a dataset with 500 million rows. The script runs out of memory on a single ml.r5.24xlarge instance. The engineer needs to modify the processing job to handle the dataset size. Which approach is most cost-effective and scalable?

A data science team wants to track the lineage of models, including datasets, training jobs, and endpoints, for reproducibility and audit. They need a solution that captures relationships between artifacts automatically during training and deployment. Which service should they use?

A data scientist wants to track hyperparameters, metrics, and artifacts for multiple training runs in SageMaker. They need to compare runs and identify the best performing model. Which SageMaker feature should they use?

Refer to the exhibit. A SageMaker Processing job configured as above fails with a timeout error. The input data is 100 GB of CSV files. The processing script performs standard data cleaning operations. What is the most likely cause?

During data preparation for a regression model, a data scientist notices that two features have a Pearson correlation coefficient of 0.95. The scientist is concerned about multicollinearity. Which action should be taken to address this issue?

A company is using AWS Glue to prepare data for a machine learning pipeline. The source data is in an Amazon S3 bucket in CSV format. The data scientist wants to convert the data to Parquet format and partition it by date. Which AWS Glue feature should be used to optimize the data for query performance and reduce storage costs?

A company needs to perform time-series forecasting on historical sales data. Which SageMaker built-in algorithm is BEST suited for this task?

An ML team has deployed a model to a SageMaker real-time endpoint and wants to set up automated monitoring for model quality. Which TWO elements are required to configure SageMaker Model Monitor for model quality? (Select TWO.)

A team uses SageMaker Model Monitor to track data quality. They notice that the monitor's constraint violations are increasing but the model performance remains good. What should they do?

A team is deploying a machine learning model for real-time fraud detection. The model must have inference latency under 10 ms and handle up to 1000 requests per second. The model is a gradient boosting model using XGBoost. Which SageMaker hosting configuration is MOST cost-effective while meeting the requirements?

An ML team wants to monitor the cost of their SageMaker endpoints. They have observed that some endpoints are underutilized. Which AWS offering can help them reduce costs by committing to a consistent amount of usage in exchange for a lower price?

An ML team uses AWS Step Functions to orchestrate a retraining pipeline triggered by EventBridge when new training data arrives. The pipeline includes a SageMaker training job and a model evaluation. If evaluation fails, the team wants to send an alert. How should they implement this?

An ML engineer monitors a SageMaker endpoint for data drift. They set up SageMaker Model Monitor to compare inference data against a baseline created from the training dataset. The monitoring schedule runs daily and reports violations. Which monitoring type should be configured to detect if the distribution of a numerical feature in real-time inference data differs significantly from the training distribution?

A company needs to deploy a large language model (LLM) on SageMaker with the Triton Inference Server to maximize GPU utilization and reduce latency. They have an NVIDIA A100 GPU. Which SageMaker inference option supports Triton?

A machine learning team at a retail company has deployed a product recommendation model using Amazon SageMaker. The model is updated weekly with new data. Recently, the team noticed that the model's accuracy on a holdout evaluation set has been declining over the past month. The data pipeline that feeds the training job has not changed. The team suspects data drift. They have SageMaker Model Monitor enabled on the inference endpoint and have set up Amazon CloudWatch metrics for feature distribution distances. Upon reviewing the CloudWatch dashboards, they see that the feature distribution distance metric for the most important feature 'product_category' has increased significantly. However, the team is unsure if this is the root cause. Which remediation step should the team take FIRST?

A data science team uses SageMaker Pipelines for automated training. They need to conditionally register a model only if evaluation metrics exceed a threshold. Which pipeline step type should they use after the evaluation step?

A machine learning engineer is preparing a dataset for a binary classification model. The dataset has 10,000 rows and 200 features, with 5% positive class. The engineer suspects class imbalance may affect model performance. Which TWO actions should the engineer take to mitigate imbalance? (Choose 2.)

An ML engineer is using Amazon SageMaker Automatic Model Tuning (AMT) to optimize hyperparameters for a gradient boosting model. The tuning job is taking a long time and has completed many training jobs. The engineer wants to stop training jobs that are unlikely to improve the objective metric. What should they configure?

A company deploys a model for credit risk assessment on a SageMaker endpoint. To comply with internal policies, they must ensure that the endpoint only allows inference requests from within a specific VPC and that the data is encrypted at rest. Which configuration meets these requirements?

A company trains a model daily using Amazon SageMaker and uses the model for real-time inference. They want to detect data drift between the training data and the inference data to decide when to retrain. Which AWS service should they use for this purpose?

A machine learning engineer wants to store, share, and manage features for multiple ML models across an organization. The features need to be accessible for both real-time inference (low-latency) and batch training. Which AWS service should the engineer use?

During a SageMaker training job, the loss stops decreasing and the validation accuracy plateaus early. SageMaker Debugger rules are enabled. Which rule is MOST likely to identify this issue?

Which SageMaker built-in algorithm is designed for time series forecasting?

A team is deploying a model that requires GPU acceleration for inference. They are using an Amazon SageMaker real-time endpoint. The model is a large language model (LLM) that does not fit on a single GPU. Which configuration should they use to minimize latency while fitting the model?

Which SageMaker feature allows you to automatically tune hyperparameters using Bayesian optimization?

A data engineer wants to transform a categorical feature with 1,000 possible values into numerical features for a linear model. Which feature engineering technique is most appropriate for this high-cardinality feature?

A company wants to ensure that only authorized users and services can invoke a SageMaker real-time endpoint. Which AWS service can be used to manage access control?

A company uses SageMaker Model Monitor to detect data drift in production. The monitoring job compares the current data distribution to a baseline. Which TWO types of drift can SageMaker Model Monitor detect? (Select TWO)

A company uses SageMaker to orchestrate a training pipeline with multiple steps including preprocessing, training, and evaluation. They want to ensure that each step can be reused and tracked. Which three SageMaker features support this? (Select THREE.)

A team deploys a PyTorch model on Amazon SageMaker for real-time inference. They notice that inference latency is higher than expected. They suspect the serialization format used for input data is inefficient. Which approach would MOST likely reduce latency?

A company wants to use SageMaker to deploy a model that requires GPU acceleration for inference but also needs to keep costs low when traffic is low. Which SageMaker feature should they use?

A machine learning engineer is using SageMaker Automatic Model Tuning to optimize hyperparameters for a regression model. The objective metric is RMSE. The training job is costly, and the engineer wants to find a good configuration quickly. Which tuning strategy should they use?

A machine learning team is using Amazon SageMaker to train a model. They notice that the training job is taking longer than expected and the logs show repeated warnings about 'loss not decreasing'. Which SageMaker feature should they use to diagnose and visualize the training process?

Refer to the exhibit. A team has configured data capture for a SageMaker endpoint. The endpoint is returning predictions but no captured data appears in the S3 bucket. What is the most likely cause?

An ML engineer is preparing a time-series dataset for a forecasting model that predicts daily sales for the next 30 days. The dataset contains 3 years of daily sales data. Which data splitting strategy should the engineer use to evaluate the model's performance on future data?

A company wants to secure access to a SageMaker real-time endpoint. Which TWO actions should be taken? (Select two.)

A company runs a batch inference job on 10 TB of image data stored in S3. Each image needs to be processed by a GPU-accelerated model. The job is not time-sensitive and cost is the primary concern. Which SageMaker option is MOST appropriate?

A team is developing a model to predict customer churn. The dataset has 10,000 samples with 20 features. The target variable is binary with 15% churn rate. The team wants to use logistic regression. Which data preprocessing step is MOST important to ensure proper convergence?

A company wants to use SageMaker to serve real-time predictions with a model that has a large memory footprint. They need to ensure the endpoint can handle traffic spikes. Which scaling policy should they use?

A team is training a PyTorch model using SageMaker and wants to use their own custom training container with a specific PyTorch version. Which approach should they use?

Refer to the exhibit. A data scientist creates a SageMaker Pipeline definition using the JSON shown. The pipeline runs successfully, but the scientist notices that the training step did not use the parameter 'TrainingInstanceCount' defined in Parameters. Why did this happen?

A data engineer is preparing a dataset for a k-means clustering algorithm. The features have different scales: age (18-100), income ($20k-$200k), and number of purchases (0-50). Without scaling, which feature will dominate the distance calculations?

A machine learning engineer is monitoring a production SageMaker endpoint using Amazon CloudWatch. They want to set up alarms for anomalous behavior. Which TWO CloudWatch metrics are MOST appropriate for detecting a sudden increase in request latency?

A company runs a real-time inference endpoint with an auto-scaling policy based on average CPU utilization. During a traffic spike, the endpoint scales out but takes several minutes to become healthy, causing increased latency. The endpoint uses a large instance type. Which change would MOST effectively reduce the time to scale out?

Refer to the exhibit. A Glue job runs successfully the first time but on subsequent runs with new data (added to the same input location), the job does not process the new data. What is the most likely cause?

A social media company is processing a real-time stream of user activity data from Amazon Kinesis Data Streams to train a machine learning model for content recommendation. The raw data includes user ID, timestamp, content ID, interaction type (like, share, comment), and device type. The data scientists need to aggregate features per user over a sliding window of 7 days, including counts of interaction types, unique content IDs engaged, and a moving average of interaction timestamps. The aggregated data will be used to update a user embedding model. The streaming data volume is approximately 500 records per second, and the company uses an AWS Glue streaming ETL job for transformation. However, the Glue job is failing frequently with high latency and checkpoint errors. The team needs a more robust solution to prepare the streaming data features. Which approach should the team take?

A company uses Amazon SageMaker to train and deploy machine learning models. The security team requires that all data in transit between the training job and S3 be encrypted, and that no data traverses the public internet. Which configuration should the company use?

A machine learning engineer is deploying a model using SageMaker and needs to ensure that the endpoint can automatically scale based on traffic patterns. Which TWO actions should the engineer take? (Choose two.)

A machine learning team is building a CI/CD pipeline to train and deploy models using Amazon SageMaker. They want to ensure that the deployment step only proceeds if the model evaluation metrics exceed a certain threshold. Which THREE components should the team include in the pipeline? (Choose THREE.)

An ML engineer needs to create a feature store that supports both low-latency online inference and large-scale offline training. The features are updated hourly from a streaming source. Which Amazon SageMaker Feature Store configuration should the engineer use?

A team trains a model using SageMaker and wants to ensure that the training job cannot access the internet, but needs to access a private S3 bucket in the same VPC. Which configuration should they use?

A company uses SageMaker Model Monitor's feature attribution drift monitoring with SHAP. They receive an alert that the average SHAP value for a particular feature has increased significantly compared to the baseline. The feature's input distribution has not changed. What does this likely indicate?

A machine learning team is building a feature store using Amazon SageMaker Feature Store. They need to store features that support both real-time inference (low latency) and historical training. Which configuration should they choose?

A company notices that the prediction distribution of their deployed model has shifted significantly from the training data distribution, but the input data distribution remains unchanged. Which type of drift is occurring, and what is the MOST likely cause?

A company has a trained machine learning model that needs to be deployed as a real-time inference endpoint on Amazon SageMaker. The endpoint must automatically scale based on incoming traffic. Which SageMaker feature should be used?

A company wants to detect anomalies in login events from a large user base, focusing on unusual patterns that may indicate compromised accounts. Which SageMaker built-in algorithm is most suitable for this task?

A data scientist is using Amazon SageMaker Ground Truth to create a labeled dataset for object detection. The team wants to reduce labeling costs by automatically labeling easy examples and only sending uncertain examples to human annotators. Which TWO features should the scientist use? (Select TWO.)

A company is deploying a deep learning model for real-time inference using Amazon SageMaker. The model is a CPU-intensive XGBoost model that performs well with CPU. However, the team wants to minimize latency further by using hardware acceleration. They are considering Amazon Elastic Inference (EI) or moving to a GPU instance. The model is not optimized for GPU, so significant code changes would be required. Which approach is the MOST cost-effective way to reduce latency without changing the model code?

A company wants to use a pre-trained NLP model from SageMaker JumpStart for sentiment analysis. Which step is required to make predictions?

A company wants to version and track ML models, with an approval workflow for promoting models from staging to production. Which SageMaker feature should they use?

A machine learning engineer runs a training job and notices the loss is NaN after a few steps. Which SageMaker Debugger rule can help identify this issue?

A financial institution uses SageMaker to train and deploy models. They need to track every experiment, model version, and deployment step for audit purposes. Which SageMaker feature should they use to capture the full lineage of artifacts, actions, and contexts?

A team is deploying a real-time inference endpoint in SageMaker. The model requires access to an S3 bucket containing customer data, which is encrypted with SSE-KMS. The team needs to ensure that the endpoint can decrypt the data. Which IAM role configuration is necessary?

A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset that contains a column 'CustomerID' with high cardinality (over 10,000 unique values). The column will be used as a feature in a model predicting customer churn. What is the recommended approach to handle this high-cardinality feature?

A data scientist wants to train a binary classification model using Amazon SageMaker with a built-in algorithm that performs well on tabular data. Which algorithm should they choose?

A company is deploying a large NLP model on SageMaker for real-time inference. They want to reduce inference latency and cost by optimizing the model for the target hardware. The model is trained in PyTorch. Which SageMaker feature should they use to compile the model for best performance on the chosen instance?

A company wants to build a machine learning model to predict house prices based on features like square footage, number of bedrooms, and location. The target variable is a continuous numeric value. Which Amazon SageMaker built-in algorithm is most appropriate for this task?

A machine learning engineer uses Amazon SageMaker Data Wrangler to preprocess a dataset. After applying a transform, the engineer wants to export the data to a feature group in Amazon SageMaker Feature Store for reuse in training and inference. Which export option should they choose?

A data engineer is building a feature engineering pipeline in AWS Glue ETL to process streaming data from Amazon Kinesis. The data includes a nested JSON structure with arrays. The engineer needs to flatten the nested structures into a tabular format for machine learning. Which THREE approaches are valid for this task? (Choose 3.)

A data scientist is preparing text data for a sentiment analysis model using Amazon SageMaker. Which two data preprocessing techniques are commonly used when working with text data for natural language processing? (Choose two.)

A team is training a large model on SageMaker using the SageMaker distributed training library with model parallelism. They need to choose the most cost-effective instance type. Which instance family offers the best balance of performance and cost for large model training?

A company is deploying a ML model for real-time fraud detection using SageMaker. The model must process requests within 50 ms and scale to handle up to 10,000 requests per second during peak hours. The data includes PII, so all traffic must stay within a VPC. The team has configured the SageMaker endpoint with a VPC and an internet gateway for model downloads. During a load test, the endpoint fails to achieve the required throughput. Which change would most likely resolve the issue?

A data scientist wants to track feature definitions, share them across teams, and serve features for both training and real-time inference. Which AWS service provides these capabilities?

A machine learning engineer is performing feature selection for a regression model with 200 features. The dataset has 10,000 samples. The engineer wants to remove irrelevant features while keeping those that have a strong non-linear relationship with the target. Which feature selection method is best suited for this requirement?

Refer to the exhibit. A data scientist ran a training job using a custom algorithm container. The job failed with the error shown. What is the most likely cause?

A team is using AWS Step Functions to orchestrate a machine learning workflow that includes data preprocessing, training, and model evaluation. The team wants to run the workflow whenever new data arrives in an S3 bucket. Which approach should they use to trigger the Step Functions workflow?

A data engineer is building a data pipeline for a machine learning model that requires both structured and unstructured data. The structured data (customer demographics) is in Amazon RDS, and the unstructured data (customer support chat logs) is in Amazon S3 as JSON files. The engineer needs to combine these datasets into a single training dataset stored in S3 in Parquet format. They must also perform feature engineering such as text vectorization on the chat logs. The pipeline should be serverless and cost-effective. Which approach should they use?

A machine learning engineer is preparing a training job on SageMaker with a custom Docker container. Which TWO actions are required to use the container with SageMaker? (Choose TWO.)

A data scientist is building a binary classification model on a highly imbalanced dataset where the positive class represents only 1% of the data. The scientist needs to train the model using Amazon SageMaker's built-in XGBoost algorithm. Which strategy should be used to address the class imbalance?

An ML team uses AWS Step Functions to orchestrate a multi-step inference pipeline: data preprocessing, model inference, and postprocessing. The pipeline runs on demand for single records. The team notices that the pipeline occasionally fails due to timeouts in the preprocessing step. They want to implement retries with exponential backoff and a maximum retry count of 3 for that step. How should they configure this?

A financial services company deploys multiple models on a single Amazon SageMaker endpoint using a multi-model endpoint (MME). The models are stored in Amazon S3. Each model is approximately 500 MB and is loaded on demand. Users report high latency for cold-start scenarios. What should the company do to reduce cold-start latency?

A data scientist is preparing a dataset for a linear regression model. The features have different scales: one feature ranges from 0 to 1000, another from 0 to 1, and a third from -5 to 5. The scientist wants to ensure that all features contribute equally to the model. Which TWO scaling techniques should the scientist consider? (Select TWO.)

Which THREE steps are part of the typical workflow when using SageMaker built-in algorithms?

A data scientist wants to monitor a deployed model for performance degradation. Which TWO metrics from Amazon CloudWatch should they use to detect issues? (Select two.)

An engineer runs: aws sagemaker describe-endpoint --endpoint-name my-endpoint and receives the exhibit output. The engineer wants to update the endpoint to use a new model version stored in ECR with tag ':2'. Which step is necessary to perform the update?

A team wants to evaluate a binary classification model for credit risk. They need to understand the trade-off between false positives and false negatives. Which TWO metrics should they use? (Select TWO.)

A company is using SageMaker to train a model for image classification. They have a dataset of 10,000 images. They use SageMaker's built-in image classification algorithm with transfer learning. During training, they notice that the training job completes successfully but the model accuracy on the validation set is very low (~30%). They suspect the model is underfitting. Which action is most likely to improve accuracy?

A machine learning engineer needs to handle missing values in a dataset containing numerical features. The missingness is completely at random (MCAR). Which imputation strategy is most robust for downstream model performance?

A company uses SageMaker Pipelines to automate their ML workflow. They want to ensure that pipeline steps are not re-executed if the inputs and parameters have not changed since the last successful run. Which THREE features can help achieve this? (Choose three.)

A data scientist needs to split a dataset into training, validation, and test sets. The dataset has a categorical target variable with imbalanced class distribution. Which splitting technique ensures that each subset has a similar proportion of each class?

A data scientist is preparing a dataset for a binary classification model. The dataset has 10,000 samples, but the positive class represents only 2% of the data. The data scientist needs to train a model that will be evaluated on a hold-out test set that preserves the original class distribution. Which data preparation strategy is MOST appropriate?

A company uses SageMaker Pipelines to automate model retraining. The pipeline runs daily but sometimes fails due to data quality issues. What is the best design to handle this?

A SageMaker endpoint is failing with the exhibited error. What is the most likely cause of this error?

A company is deploying multiple models on a single endpoint to reduce costs. They need to update one model without affecting others. Which solution?

A machine learning engineer needs to automatically retrain a model whenever SageMaker Model Monitor detects data drift. Which combination of services should be used to trigger the retraining pipeline?

A company wants to use SageMaker Autopilot to automatically build a binary classification model. Which output does Autopilot provide to help understand model decisions?

A machine learning engineer is using AWS Glue ETL to transform a large dataset stored in Amazon S3. The transformation involves joining two tables on a high-cardinality column and aggregating results. The job is running slowly and the engineer needs to improve performance. Which optimization technique should the engineer apply?

A data scientist runs this pipeline but the Train step fails with "ResourceLimitExceeded". What is the most likely cause?

A company deploys a model using SageMaker and enables data capture for monitoring. After a week, they notice that the captured data is not being written to the specified S3 bucket. The endpoint is running and invocations are successful. What is the most likely cause?

Refer to the exhibit. An IAM policy is attached to a user to allow invoking a SageMaker endpoint. A developer tries to call the endpoint from a laptop with IP 203.0.113.5 and receives an access denied error. What is the most likely reason?

A data scientist is training a binary classification model using imbalanced data where the positive class is only 1% of the dataset. The scientist wants to maximize the recall for the positive class while maintaining reasonable precision. Which evaluation metric is most appropriate to tune during model selection?

An ML engineer is setting up monitoring for a SageMaker endpoint. Which THREE metrics should be monitored to detect performance issues? (Select THREE.)

A company is using Amazon SageMaker to host a real-time inference endpoint. They want to restrict access to the endpoint to only a specific VPC and require authentication using AWS IAM. Which TWO configuration steps should they take to achieve this? (Choose TWO.)

A team wants to orchestrate a multi-step ML workflow that includes data preprocessing, hyperparameter tuning, model training, evaluation, and conditional deployment to staging or production based on evaluation metrics. The workflow should run on a schedule and track lineage. Which service should they use?

A company is using SageMaker to train a large model using data parallelism with the SageMaker distributed data parallelism library. They notice that the training throughput is not scaling linearly with the number of GPUs. Which THREE factors could be causing this?

A machine learning engineer runs a SageMaker HyperparameterTuningJob with Bayesian optimization strategy. The job terminates earlier than the specified MaxNumberOfTrainingJobs. The engineer notices that the best objective metric value has not improved for several consecutive jobs. What is the most likely adjustment to make?

A company uses Amazon SageMaker to host a real-time inference endpoint for a fraud detection model. The endpoint is deployed with three instances of ml.m5.large. The model processes each request in about 200 ms. Lately, users report occasional timeouts (requests taking >5 seconds). The team suspects model drift or data skew. What is the MOST likely cause and solution?

A SageMaker endpoint is logging an error when processing inference requests that require database access. What is the most likely cause?

A data engineer is using AWS Glue to prepare a dataset for ML. The engineer wants to split the dataset into training and testing sets while preserving the distribution of the target variable. Which TWO methods achieve this goal? (Select TWO)

A company is fine-tuning a foundation model using RLHF (Reinforcement Learning from Human Feedback) on SageMaker. They want to reduce memory usage and training time. Which THREE techniques should they consider? (Select THREE.)

Which SageMaker built-in algorithm should be used for forecasting time series data with seasonal patterns?

A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset for classification. They want to detect potential bias in the data before training. Which SageMaker service should they use in conjunction with Data Wrangler to detect bias?

A machine learning engineer trains a binary classifier and obtains an accuracy of 95% on the test set. The dataset is imbalanced with 95% positive class. What is the most important metric to evaluate the model's performance?

A data science team needs to deploy a PyTorch model that performs real-time inference with sub-100ms latency. The model requires GPU acceleration, but the team wants to minimize cost by sharing GPU instances across multiple models. Which SageMaker hosting option should they choose?

A data science team detects that a deployed model's prediction accuracy is degrading over time due to concept drift. They need to implement a retraining strategy. Which THREE actions are recommended best practices for handling concept drift?

A data scientist uses SageMaker Automatic Model Tuning (AMT) with Bayesian optimization to tune an XGBoost model. The objective metric is validation:auc, but the tuning job converges to a plateau early. Which action is MOST effective to improve exploration?

A team uses SageMaker Pipelines with a Condition step to decide whether to register a model based on evaluation metrics. They want to also store the evaluation results for lineage tracking. Which step should they use to record the metrics?

A company's ML pipeline runs in multiple AWS accounts (dev, test, prod). They want to enforce that only approved models from a central Model Registry can be deployed to the production account. Which combination of services is MOST appropriate to implement this governance?

A team is deploying a model using SageMaker real-time endpoint with an ml.m5.large instance. They notice high latency under peak load. They want to reduce latency without increasing instance size. Which THREE actions could help? (Select THREE.)

A machine learning team is handling a text classification task with a dataset of 1 million documents. They need to convert text into numerical features. Which THREE techniques are commonly used for feature extraction from text? (Select THREE.)

A team wants to automatically retrain a model whenever data drift is detected on their SageMaker endpoint. Which AWS service should they use to invoke a retraining pipeline in response to a CloudWatch Alarm?

A data scientist needs to ingest streaming customer clickstream data from a website into an S3 data lake for ML training. The data must be delivered within 1 minute of ingestion, and JSON records must be converted to Parquet. Which AWS service combination should be used?

A machine learning engineer wants to reduce training costs by using excess EC2 capacity. Which instance purchasing option should they choose for SageMaker training jobs?

A company is deploying a SageMaker endpoint and must meet strict security requirements: no public internet access, all inter-container traffic must be encrypted, and all data at rest must be encrypted with a customer-managed KMS key. Which THREE configurations should they apply? (Choose THREE.)

A data scientist needs to train a binary classification model on a large tabular dataset stored in Amazon S3. The team wants to minimize training time and cost while using a built-in SageMaker algorithm. Which algorithm should they use?

A data scientist is training a large transformer model using SageMaker's model parallelism library. The training job is failing with an out-of-memory (OOM) error. Which two actions can help resolve the OOM error? (Choose two.)

A data engineer is designing a feature engineering pipeline using Amazon SageMaker Feature Store. The team needs to support both real-time inference (millisecond latency) and batch training jobs that require access to historical feature values at specific points in time. Which configuration should the engineer choose?

A data scientist wants to train a model on SageMaker using a custom PyTorch script, then register the best model in the SageMaker Model Registry. The training job is part of a SageMaker Pipeline. Which pipeline step should be used to register the model?

A company wants to enable cross-account access to a SageMaker model endpoint. The model is in Account A, and Account B needs to invoke it. Which TWO steps are required? (Select TWO)

A team is building a recommendation system and wants to store and serve features for online and offline models. The features include user statistics (updated daily) and movie metadata (static). The team needs low-latency inference for real-time recommendations and wants to reuse features across multiple models. Which AWS service should the team use to store, manage, and serve these features?

A machine learning engineer needs to split a time-series dataset for a forecasting model. The data spans 3 years of daily sales. Which splitting strategy should they use to avoid look-ahead bias?

You are preparing a time-series dataset for a forecasting model. Which three steps are critical to prevent data leakage during preprocessing? (Choose three.)

A dataset contains a numerical feature with extreme outliers. The outliers are genuine (not errors), and the ML model is a linear regression which is sensitive to outliers. Which data transformation should be applied to reduce the impact of outliers while preserving the data?

A team is building a time-series forecasting model for daily sales data. They want to evaluate model performance using cross-validation while respecting the temporal order of the data. Which data splitting strategy should they use?

A team is using Amazon SageMaker Data Wrangler to prepare a large dataset. They need to detect potential bias in the data before training. Which capability of Data Wrangler should they use?

A machine learning engineer is using SageMaker Debugger to monitor training jobs. They want to capture tensors every 100 steps but only for the first 500 steps. Which configuration should they set in the Debugger hook?

A company is using SageMaker Pipelines to orchestrate their ML workflow. They have a Condition step that checks if a model's accuracy exceeds 0.9. If true, they want to register the model in the model registry; otherwise, they want to run a retraining step. Which step type should they use for the decision?

A company wants to deploy 50 small models (each ~100 MB) for real-time inference. They need to minimize hosting costs while maintaining low latency. Which SageMaker hosting option is most cost-effective?

An ML team uses Amazon SageMaker Data Wrangler to prepare a dataset for a binary classification model. They suspect the dataset might contain bias against a certain demographic group. They want to detect and visualize potential bias before training the model. Which feature of SageMaker should they use?

A team is training a large language model using SageMaker with multiple GPUs. They need to reduce training time by splitting the model across devices due to memory constraints. Which distributed training strategy should they use?

An ML engineer needs to convert a raw dataset from CSV to Parquet format in a serverless manner for cost efficiency. Which AWS service can be used to perform this conversion without managing servers?

A company has trained a custom model using PyTorch on Amazon SageMaker. The model achieves high accuracy, but the inference latency on a real-time endpoint is above the required 100ms SLA. The model is a large neural network with many layers. The company wants to reduce latency without significantly impacting accuracy. Which approach should the machine learning engineer take?

A company needs to give a data science team in another AWS account access to deploy a model from a shared model registry. Which approach should they use to grant cross-account access?

A data scientist needs to convert categorical variables to numerical format for a linear regression model. The dataset contains a 'Country' column with 50 unique values. Which transformation should the engineer use to avoid introducing ordinal relationships?

A team is collaborating on a machine learning project and needs to ensure that data used for training is consistent across experiments. The team wants to version datasets, track data lineage, and be able to reproduce past experiments. The team uses SageMaker for model training. Which combination of services and features should the team use?

A company uses Amazon SageMaker Data Wrangler to prepare data for ML. The dataset contains a timestamp column and sensor readings from IoT devices. The data scientist needs to create features such as moving averages and rolling statistics over time windows. Which Data Wrangler transformation type should be selected?

A data scientist is preparing text data for natural language processing (NLP). The corpus contains many rare words and typos. To reduce dimensionality and improve generalization, they decide to apply stemming and remove stop words. However, after training, the model performs poorly on domain-specific terms. What is the most likely cause?

A data scientist is building a text classification model using a pre-trained BERT model from the Hugging Face library on SageMaker. The scientist wants to fine-tune the model on a custom dataset. Which TWO steps are necessary to set up the fine-tuning job? (Select TWO.)

A company is using a SageMaker notebook instance to develop models. The security team requires that all data in the notebook be encrypted at rest and in transit, and that internet access be restricted. Which configuration meets these requirements?

A data science team has trained a PyTorch model for real-time inference and needs to deploy it on AWS with GPU acceleration while minimizing cold-start latency. Which SageMaker inference option should they choose?

A team wants to use a custom PyTorch training script in SageMaker. They need to install additional Python packages not included in the base PyTorch container. Which approach should they take?

A data scientist is working with a dataset that contains missing values in several numeric features. The data scientist wants to impute the missing values with the median of each feature. Which Amazon SageMaker Data Wrangler transformation should be used?

Refer to the exhibit. A data engineer runs an AWS Glue ETL job with the following script portion. The job fails with an error: 'An error occurred while calling o113.pyWriteDynamicFrame. No such file or directory'. What is the most likely cause?

A machine learning engineer is deploying a model using a SageMaker endpoint and needs to ensure that the model artifacts are encrypted at rest using a customer-managed KMS key. Which configuration should they set?

A team is deploying a TensorFlow model on a SageMaker real-time endpoint with automatic scaling. They set the scaling policy to target an average CPU utilization of 50%. However, during traffic spikes, the endpoint experiences high latency and 503 errors. The instance type is ml.c5.large. What should the team do to resolve this while minimizing cost?

A company deploys a machine learning model as a SageMaker real-time endpoint. They need to implement a mechanism to automatically roll back to the previous model version if performance degrades after a deployment. Which approach should they use?

A machine learning team uses SageMaker Pipelines and wants to automatically retrain a model when data drift is detected. They have set up Model Monitor to publish drift violations to CloudWatch. Which approach provides a COMPLETE serverless retraining pipeline triggered by drift detection?

After deploying a model to a SageMaker endpoint, the operations team notices high inference latency. They suspect it is due to insufficient instance capacity. Which first step should they take to diagnose the issue?

A machine learning engineer is using SageMaker to train a model with the built-in LightGBM algorithm. The engineer wants to use early stopping to prevent overfitting. The training job is configured with a validation dataset. Which hyperparameter should be set to enable early stopping?

A company wants to automate its machine learning pipeline using AWS CodePipeline and Amazon SageMaker. The pipeline should train a model, evaluate it, and if the evaluation passes, register the model in the SageMaker Model Registry. Which service should the company use to orchestrate the training and evaluation steps?

A financial services company must deploy a SageMaker endpoint that only accepts traffic from within a VPC and encrypts all data at rest and in transit using customer-managed KMS keys. They also need to prevent inter-container traffic from being visible to other users. Which combination of settings fulfills these requirements?

A team receives alerts that their SageMaker endpoint latency has increased significantly. They check CloudWatch metrics and see Invocations rising, but ModelLatency remains stable. Which metric should they investigate to find the source of the increased latency?

A company uses AWS Glue ETL jobs to process data from multiple sources and store it in a centralized S3 data lake for ML. They want to ensure that schema changes in source tables are automatically updated in the Glue Data Catalog without manual intervention. Which feature should they enable?

A team is fine-tuning a foundation model using LoRA in SageMaker. They want to reduce memory usage during training. Which instance type is optimized for cost-effective fine-tuning with LoRA?

A data scientist is preprocessing time series data for a fraud detection model. The data includes transaction timestamps, amounts, and merchant IDs. The model should predict fraud within seconds of a transaction. The data scientist wants to avoid data leakage by not using future information to predict past events. Which data preparation practice should be implemented?

A company uses SageMaker Clarify to detect bias in their training data. They find that the model has a high disparate impact for a protected attribute. What should they do to mitigate this bias during training?

A company's SageMaker real-time endpoint is experiencing high latency under load. The CloudWatch metrics show that the ModelLatency is acceptable, but the OverheadLatency is spiking. What is the most likely cause?

A data scientist has trained a binary classification model for fraud detection. The dataset is highly imbalanced (99% non-fraud, 1% fraud). After evaluation, the model shows an accuracy of 99%, but the recall for fraud cases is only 10%. Which metric should the data scientist prioritize to improve the model's performance for fraud detection?

A team uses SageMaker Clarify to monitor bias drift on a deployed model. They have defined a baseline with training data and set up a monitoring schedule. After one month, they receive a violation report indicating that the post-training metrics have deviated from the baseline. What does this violation indicate?

A team is using Amazon SageMaker Processing for data preprocessing. They have a Parquet dataset in Amazon S3. Which configuration will provide the most efficient reading of the dataset during processing?

A financial services company is deploying a real-time fraud detection model using Amazon SageMaker. The model is a gradient boosting model (XGBoost) trained on historical transaction data. The inference endpoint uses an ml.m5.2xlarge instance with a single variant. Recently, the company has experienced a 3x increase in transaction volume during peak hours, causing inference latency to exceed the 200ms SLA. The data science team has already optimized the model by reducing the number of trees and feature set, but the latency remains high during spikes. The team considers using SageMaker's built-in scaling policies. They currently have a single endpoint with one production variant. The team wants to maintain low latency without over-provisioning resources. They have ruled out model changes. Which approach should the team take?

A machine learning engineer wants to automatically track hyperparameters, metrics, and artifacts for multiple training runs. Which SageMaker feature should they use?

A financial services company deploys a fraud detection model on a SageMaker real-time endpoint. The inference logic includes a pre-processing step that requires access to a DynamoDB table for user metadata. The model container is a custom Docker image. How should the team grant the endpoint access to DynamoDB?

A data scientist is performing text preprocessing for a sentiment analysis model. The dataset contains many stop words and rare words. Which combination of preprocessing steps will reduce dimensionality and improve model performance?

An organization uses SageMaker Studio and needs to restrict Studio's internet access while allowing users to install custom packages from a private PyPI mirror hosted in a VPC. Which networking configuration should they use?

An organization needs to ensure that all data used for inference on a SageMaker endpoint is encrypted at rest. The endpoint uses a SageMaker-provided container. Which configuration should be applied?

A data engineer is setting up a Glue ETL job to process a large dataset stored in Amazon S3. The job needs to read data in Parquet format, apply a filter, and write the results back to S3 in Parquet. The engineer wants to minimize the cost and runtime. Which optimization technique is MOST effective?

A company is running a SageMaker endpoint serving multiple models. They need to monitor for data drift and model quality. Which THREE actions are necessary? (Choose three.)

A financial services company trains multiple models on SageMaker and needs to track hyperparameters, metrics, and artifacts for each experiment. Which SageMaker feature should they use to organize and compare experiments?

A company operates an e-commerce platform that uses a machine learning model to recommend products to users. The model is deployed on an Amazon SageMaker endpoint with automatic scaling enabled based on average CPU utilization. The model was trained on historical data and is updated weekly. Recently, the platform experienced a flash sale event that caused a sudden spike in traffic. During the event, the endpoint's latency increased dramatically, and many requests timed out. After the event, the team reviews the CloudWatch metrics and notices that the CPU utilization never exceeded 70%, and the scaling policy was triggered but instances took several minutes to become available. The team wants to prevent similar issues in future flash sales. Which course of action would be MOST effective?

A SageMaker Processing job fails with 'Access Denied' when listing objects in an S3 bucket, despite the IAM policy shown in the exhibit. What is the most likely cause?

An ML team wants to deploy a model that was trained using XGBoost in SageMaker. They want to use the built-in XGBoost algorithm container for inference. Which inference option requires the least custom code?

A machine learning engineer is using Amazon SageMaker Data Wrangler to prepare a dataset for a regression model. After applying a StandardScaler to numeric features, the target variable has a mean of 50 and standard deviation of 20. Which additional step should the engineer take to reduce model bias?

A financial services company is deploying a credit risk model using SageMaker. They require that the model always uses the latest approved version from the Model Registry. They also need to maintain a detailed audit trail of all model version transitions (e.g., from PendingApproval to Approved). The deployment should be fully automated and must roll back immediately if the new model's error rate exceeds the old model's error rate by more than 2% during a canary deployment. Which solution meets these requirements with the least custom code?

A company wants to serve a large ensemble of models using NVIDIA Triton Inference Server on SageMaker for high throughput GPU inference. Which SageMaker inference option supports this?

A data engineer is using Amazon SageMaker Data Wrangler to create a data preparation flow for a dataset with 500 columns, many of which are highly correlated. The goal is to reduce dimensionality while preserving interpretability. Which built-in transform in Data Wrangler should be applied?

A machine learning engineer is using SageMaker Automatic Model Tuning (AMT) to optimize hyperparameters for a random forest model. The engineer notices that the tuning job is taking too long and many hyperparameter combinations are being evaluated but not improving the objective metric. Which action should the engineer take to make the tuning more efficient?

A team uses SageMaker Pipelines to train and register a model. They want to conditionally run a hyperparameter tuning step only if the data quality check passes. Which pipeline step type should they use to branch the execution?

A company uses SageMaker Model Monitor to track feature attribution drift with SHAP. They notice that the SHAP values have changed significantly for a feature, while the model performance remains stable. What is the MOST likely interpretation?

Which SageMaker built-in algorithm is specifically designed for time series forecasting?

A company has a SageMaker real-time endpoint that serves predictions. They want to set up automated monitoring and remediation for when the number of 5XX errors exceeds a threshold. Which TWO steps should they take? (Choose TWO.)

A team is building a regression model on a dataset with missing values in multiple features. They decide to use a k-Nearest Neighbors (k-NN) imputer. The dataset has 100,000 rows and 50 features. Which step should the team take to ensure the imputation is efficient and accurate?

A company needs to deploy a new model version to a SageMaker real-time endpoint. They want to route 5% of traffic to the new version initially to monitor for errors before full rollout. Which deployment strategy should they use?

A machine learning engineer observes that a SageMaker training job fails with the error shown in the exhibit. What is the most likely cause of the failure?

A machine learning engineer is building a pipeline to ingest data from multiple sources into an Amazon S3 data lake. The data includes both batch files and real-time streams. The engineer needs to catalog the data for discovery and querying. Which TWO AWS services should the engineer use to ingest and catalog the data? (Select TWO.)

A data scientist is using Amazon SageMaker Data Wrangler to create a data flow for a machine learning project. The source data is in Amazon S3 and contains PII (personally identifiable information) such as email addresses and credit card numbers. The data scientist needs to prepare the data for training while ensuring compliance with data privacy regulations. Which THREE actions should the data scientist take? (Select THREE.)

A company requires that all SageMaker notebook instances be created within a private VPC without internet access. Which configuration step is mandatory?

Refer to the exhibit. A SageMaker training job failed. Based on the error message, which action should the engineer take?

A company is fine-tuning a large language model using LoRA on SageMaker. They want to reduce GPU memory usage during training. Which configuration change would help?

A machine learning engineer is building a pipeline to preprocess text data for a sentiment analysis model. The data consists of customer reviews. The engineer wants to convert the text into numerical features while preserving the semantic meaning of words. Which technique should be used?

A data scientist trains a binary classification model using SageMaker and obtains an AUC of 0.95 on the test set. However, the precision-recall curve shows low precision for high recall thresholds. The business requires a model that performs well on the minority class. Which metric should the team primarily optimize during hyperparameter tuning?

A team is using SageMaker to train a distributed model with data parallelism. They notice that the training loss is not decreasing as expected and suspect a bug in the data loading pipeline. Which SageMaker Debugger feature can help them inspect the data distributions during training?

A data science team deployed a model on Amazon SageMaker and enabled Model Monitor to detect data drift. After a week, they receive alerts indicating that the distribution of a key feature has shifted significantly. However, the model's accuracy on the recent production data remains high. Which action should the team take next?

A data science team deploys a regression model using Amazon SageMaker. After one week, the model's prediction accuracy drops significantly. The team needs to detect this degradation automatically and trigger retraining. Which AWS service should they use to monitor the model's performance over time and set up alerts?

Refer to the exhibit. A data engineer deploys this Glue job via CloudFormation. When running, the job fails with a timeout after 2 hours. The job processes a large dataset and expected to take 3 hours. Which change would resolve the issue?

A data scientist notices that a production model's accuracy has degraded over the past week. The training data distribution remains unchanged, but the relationship between features and the target has shifted. Which type of drift is occurring, and which monitoring approach should be used?

A company needs to detect bias in a pre-trained model before deployment. They want to compute metrics like disparate impact and equal opportunity difference. Which AWS service should they use?

A data engineer needs to prepare a large dataset (10 TB) stored in Amazon S3 for a training job on SageMaker. The data is in CSV format, but the training algorithm expects Parquet for performance. The engineer must transform the data with minimal cost and without writing custom code. Which service should be used?

A data scientist needs to create a feature group in Amazon SageMaker Feature Store for real-time recommendations. Which TWO configurations are required? (Select TWO.)

A data scientist is training a binary classifier on a highly imbalanced dataset (1:100 class ratio). The dataset contains 500,000 rows and 30 features. The data is stored in S3 in Parquet format. The data scientist wants to use SageMaker's built-in XGBoost algorithm. Which data preparation technique should the data scientist apply to best address the class imbalance without causing data leakage?

A machine learning team deploys a fraud detection model on a SageMaker endpoint. The model's predictions are used in real-time. The team wants to monitor for data drift by comparing incoming data distributions against a baseline created from the training data. Which SageMaker capability should they use?

An ML team uses SageMaker Pipelines to automate model retraining. They want to skip redundant training steps when input data has not changed. Which feature should they enable?

A company wants to restrict access to a SageMaker notebook instance so that only a specific IAM role can open the notebook via JupyterLab. The notebook instance is associated with a lifecycle configuration that installs custom packages. What is the correct way to enforce access control?

A company plans to deploy a large foundation model using SageMaker JumpStart. They are concerned about costs because the model will be used intermittently. Which deployment option is MOST cost-effective for intermittent traffic?

Refer to the exhibit. The training job failed. What is the MOST likely cause?

A company uses Amazon SageMaker to train a custom XGBoost model. The training job runs on a single ml.m5.large instance and takes 2 hours. To reduce training time without changing the algorithm, what should the data scientist do?

A data scientist is evaluating data quality for a machine learning project. The dataset has missing values, outliers, and inconsistent formatting. Which TWO steps should the data scientist perform during the data preparation phase? (Choose 2.)

A machine learning engineer wants to reduce costs for a SageMaker real-time endpoint that experiences predictable traffic patterns with low traffic at night and high traffic during business hours. Which approach is most cost-effective while maintaining availability?

A team is deploying a model using SageMaker Pipelines. They have defined a pipeline with steps: preprocessing, training, evaluation, and conditional registration. The evaluation step produces a JSON file with metrics. If accuracy > 0.9, the model is registered; else, the pipeline fails. Which TWO statements about this pipeline are correct? (Choose TWO.)

A data scientist is training a deep learning model on SageMaker and notices that the training loss oscillates and does not converge. They want to debug this issue. Which SageMaker feature can they use to monitor and analyze the training process?

A team has a large number of models that need to be deployed for batch inference weekly. They want to minimize cost and management overhead. Which approach is MOST efficient?

A company wants to deploy a foundation model from SageMaker JumpStart with the lowest possible inference cost, given that latency requirements are flexible. They have a mix of traffic volumes. Which approach should they take?

A company wants to deploy a machine learning model using infrastructure as code to ensure reproducibility. They need to define the SageMaker Studio domain, user profiles, and the endpoint configuration. Which tool should they use?

An ML engineer creates a SageMaker inference pipeline with two containers: a preprocessor and a predictor. The preprocessor is a lightweight Python script that transforms input data. How should the engineer structure the endpoints to ensure both containers run sequentially?

Which SageMaker feature compiles a trained model into an optimized binary for a specific hardware target (e.g., Intel, ARM, NVIDIA, or edge devices) to improve inference performance?

A team is training a large language model using PyTorch on SageMaker. They need to reduce training time. The model has 10 billion parameters. Which distributed training strategy should they use?

A company uses SageMaker Model Registry to manage model versions. They want to automate the approval of models that pass automated evaluation, but require manual approval for others. Which Model Registry feature supports this workflow?

A company deploys a model for fraud detection. They want to monitor if the model's predictions become less accurate over time due to changes in the underlying data distribution, but they do not have immediate access to ground truth labels. Which type of drift should they monitor as a proxy?

A company needs to serve real-time predictions from a large ensemble of three deep learning models, each requiring different inference environments (PyTorch, TensorFlow, MXNet). Which SageMaker endpoint type supports running multiple inference containers together?

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

An organization stores raw data in Amazon S3 as CSV files. They need to perform serverless data transformation and convert the data to Parquet format for efficient ML training. Which AWS service is most appropriate?

A machine learning engineer wants to reduce costs for hyperparameter tuning jobs that run for several hours. The jobs are fault-tolerant and can be interrupted. Which TWO actions should they take? (Select TWO.)

A team is building a fraud detection model using SageMaker and wants to detect anomalies in user login events. Which SageMaker built-in algorithm is specifically designed for anomaly detection in event-based data?

A data scientist is preparing text data for sentiment analysis. They need to convert the text into numerical features while reducing the impact of common words. Which feature extraction method should they use?

A machine learning engineer is designing a SageMaker Pipeline that includes a training step, a processing step for evaluation, and a condition step to decide whether to register the model. The pipeline should support caching to avoid redundant runs when inputs haven't changed. Which three steps must have caching enabled? (Select THREE.)

A financial services company operates a real-time inference endpoint for a fraud detection model on Amazon SageMaker. The model was trained on historical transaction data from 2023. Over the past month, the model's precision has dropped from 92% to 78%, while recall remains high at 95%. The data science team suspects data drift and has already enabled SageMaker Model Monitor with data capture and a baseline from the training data. The latest monitoring report indicates no statistically significant drift in any of the input features. The team also verified that the inference code and model artifact have not changed. Despite the stable feature distributions, the model is misclassifying an increasing number of legitimate transactions as fraudulent (false positives). The business is concerned about the impact on customer experience. What is the best course of action?

Refer to the exhibit. The data scientist wants to update the endpoint to use a new model version without downtime. Which approach should they use?

A team wants to secure SageMaker endpoints for a healthcare application. They must ensure data is encrypted at rest and in transit, and that the endpoint can only be accessed from within a VPC. Which THREE steps should they take? (Select THREE)

A data scientist is using Amazon SageMaker Data Wrangler to create a data preparation flow. After completing the flow, the scientist wants to export the processed data to a feature group in Amazon SageMaker Feature Store for reuse in multiple training jobs. Which export option should the scientist choose in Data Wrangler?

During data quality assessment, a data scientist discovers that a numeric feature has many missing values. The feature is expected to have a skewed distribution. The scientist wants to impute missing values in a way that preserves the distribution shape and does not introduce bias toward the center. Which imputation strategy is MOST appropriate?

A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset. Which TWO features of Data Wrangler can be used to handle imbalanced classification problems? (Choose two.)

A data scientist is using SageMaker Autopilot to automatically build a binary classification model. The dataset is imbalanced. Which action will Autopilot take by default to address class imbalance?

A company uses Amazon SageMaker to train and deploy machine learning models. They need to run batch predictions on 10 TB of data stored in Amazon S3 every night. The model is a PyTorch neural network that fits in GPU memory. The predictions are not time-sensitive, but the job must complete within 8 hours. Which approach would be the MOST cost-effective?

A company ingests streaming transaction data from multiple sources using Amazon Kinesis Data Streams. The data must be transformed (e.g., JSON parsing, data type conversions) and then stored in Amazon S3 for ML training. The transformation logic may change over time. Which approach provides the greatest flexibility and ease of maintenance?

A company uses SageMaker Processing jobs to clean customer transaction data. The processing script runs on a single ml.m5.large instance and takes 30 minutes to process 50 GB of data in CSV format. To reduce processing time, the company wants to process 200 GB of data within 1 hour. Which combination of changes should the company make?

A company wants to use SageMaker to fine-tune a foundation model for a text generation task using RLHF (Reinforcement Learning from Human Feedback). Which THREE components are required in the RLHF pipeline?

A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset. They need to identify potential bias in the data before training. Which SageMaker feature should they use?

A company uses SageMaker Ground Truth to label a dataset for object detection. They set up a labeling job with a private workforce. After labeling, they export the dataset and train a model using SageMaker's built-in object detection algorithm. The model achieves high accuracy on the test set but low accuracy on a small holdout set that was manually labeled by an expert. What might be the issue?

A data scientist needs to run a hyperparameter tuning job for a PyTorch model using SageMaker. They want to use Hyperband for efficient resource allocation. Which tuning strategy should they select in the HyperparameterTuner?

A data science team deploys a real-time inference endpoint on Amazon SageMaker. They want to monitor for data drift in the input features over time. Which AWS service should they use to capture and analyze the input data distribution?

A data science team needs to deploy a PyTorch model for real-time inference with low latency. The model requires GPU acceleration. Which SageMaker endpoint configuration should they use?

An ML team uses SageMaker Model Registry to manage model versions. They want to automatically deploy a model to a staging endpoint when a new version is approved. Which AWS service can orchestrate this?

A practitioner is using SageMaker Automatic Model Tuning with Hyperband strategy. They want to stop underperforming trials early to save compute. Which Hyperband parameter controls the aggressiveness of early stopping?

A data scientist discovers that a dataset for binary classification contains 95% negative samples and 5% positive samples. Which technique is MOST appropriate to address the class imbalance?

An organization is deploying a large language model on SageMaker and needs to optimize inference costs while maintaining low latency. Which three strategies should they consider? (Select THREE.)

An ML team is using Amazon SageMaker Feature Store to serve features for both real-time inference and batch training. They need to ensure that training data uses feature values as they were at the time of each event. Which type of query should they use?

A data scientist needs to prepare a dataset for a binary classification model. The dataset has 10,000 samples with 100 features, and the target class is highly imbalanced (5% positive). Which combination of techniques should the data scientist use to address class imbalance and prepare the data for training?

A company deploys a real-time inference endpoint with auto-scaling using a target tracking policy based on average Invocations per instance. They notice that during a traffic spike, the endpoint scales out too late, causing increased latency. They want to scale proactively before the spike. Which strategy should they implement?

A SageMaker training job has been running for several hours but shows no progress. The job is using a custom Docker container. The engineer suspects a bug in the training script. Which tool is BEST to debug the training job without stopping it?

A company wants to deploy a PyTorch model on SageMaker for real-time inference. Which two steps are required? (Select TWO.)

A data science team wants to automate the retraining of a model whenever SageMaker Model Monitor detects a significant drift in data quality. They need the least amount of custom code. Which approach should they use?

A machine learning team is preparing data for a binary classification model. The target variable has a severe class imbalance (95% negative, 5% positive). The team wants to train a model that maximizes recall on the positive class while keeping training time manageable. Which approach should they use?

A company is building a sentiment analysis model for customer reviews. The text data contains many typos, abbreviations, and informal language. Which text preprocessing step would be most beneficial to support the ML model?

A company is using SageMaker Debugger to monitor a training job for a deep learning model. They want to detect when gradients become extremely large, which may cause training instability. Which built-in rule should they use?

A data engineer is using Amazon Athena to query a partitioned dataset stored in S3. Which THREE actions are necessary to ensure the queries can access the data and run efficiently?

A machine learning engineer is using Amazon SageMaker Experiments to track multiple training runs. They want to compare the performance of different hyperparameter configurations visually. Which SageMaker tool provides an interactive interface to compare experiments?

A company has deployed a real-time inference endpoint using SageMaker. The endpoint latency is within acceptable limits, but the team notices that the Invocations metric shows occasional spikes. They want to investigate the source of the spikes. Which CloudWatch metric should they examine to isolate the time spent in SageMaker overhead versus model inference?

An ML engineer needs to deploy a model as an AWS Lambda function for serverless inference. The model is a scikit-learn pipeline serialized as a pickle file. What is the best way to include the model in the Lambda deployment?

A team is fine-tuning a foundation model using reinforcement learning from human feedback (RLHF) on SageMaker. They have a dataset of human preferences. Which SageMaker capability is most suitable for the reward model training step?

A team uses MLflow on SageMaker for experiment tracking. They want to automatically deploy the best-performing model from an MLflow run to a SageMaker endpoint for real-time inference. What is the MOST efficient way to achieve this?

A data scientist needs to annotate a large dataset of images for an object detection model. The team wants to minimize manual labeling effort and cost. Which Amazon SageMaker feature should they use?

A data scientist is training an XGBoost model on a large tabular dataset using SageMaker. The training job is taking too long. The scientist wants to reduce training time while maintaining model quality. Which action should the scientist take?

A company is setting up a data pipeline to ingest streaming clickstream data from their website for real-time analytics and machine learning. The data must be reliably ingested, transformed, and stored in Amazon S3 for batch processing. Which combination of AWS services should be used?

A team is fine-tuning a Hugging Face transformer model on SageMaker. They need to use a custom training script with the Hugging Face Estimator. Which SageMaker feature does this represent?

A machine learning engineer needs to deploy a model that requires less than 100 ms inference latency for real-time predictions. The model is a small PyTorch model that fits in a single GPU. Which SageMaker inference option is MOST cost-effective for this scenario?

A machine learning team notices an increase in 5XXError count for a SageMaker endpoint. They want to set up automated remediation. Which THREE actions should they take? (Select THREE)

A company uses Amazon Kinesis Data Streams to ingest real-time user interactions and wants to store the data in Amazon S3 for historical analysis. They need to transform the data (e.g., add timestamps, filter records) before storage. Which approach is MOST cost-effective?

A data scientist needs to version and manage multiple models for a team of five. The team frequently experiments with different algorithms and hyperparameters. They need a centralized registry to store, deploy, and compare model versions. Which AWS service should the data scientist use?

Your team manages a SageMaker real-time endpoint for a financial services application that requires low latency for fraud detection. The model is a 1 GB XGBoost model. The endpoint is deployed on two ml.m5.xlarge instances with target tracking auto-scaling based on average CPU utilization at 70%. During peak hours, the endpoint receives a sudden burst of traffic that increases from 500 requests per second to 2000 requests per second within 30 seconds. Many requests start failing with 503 errors. The CPU utilization metric shows that the instances are at 90% before the scaling policy launches new instances. However, by the time the new instances are added (approximately 3 minutes), the burst has subsided. You need to prevent these failures during future bursts while keeping costs reasonable. Which action would be MOST effective?

A data scientist suspects that a deep learning model is overfitting. They enable SageMaker Debugger and want to detect overfitting automatically. Which built-in rule should they use?

A machine learning engineer is monitoring a deployed model for data drift. The input features are a mix of categorical and numerical columns. The baseline is from the training data. Which SageMaker Model Monitor feature should they enable to detect changes in the distribution of each feature over time?

A company wants to monitor their machine learning model for bias over time. Which THREE AWS services or features can they use to achieve this? (Choose THREE.)

A data scientist is training a linear regression model on a dataset with 10 features. After training, the model shows high training accuracy but poor test accuracy. Which of the following is the most likely cause?

A company wants to use SageMaker built-in algorithms for a time series forecasting task. Which TWO algorithms are appropriate for this task? (Choose TWO.)

A company uses SageMaker Model Registry to manage model versions. They want to enforce that only models with an 'Approved' status can be deployed to production endpoints. How can they enforce this?

A machine learning team deploys a model for loan approval. They want to monitor data drift on the real-time endpoint using SageMaker Model Monitor. Which set of actions should they take to set up data quality monitoring?

A company uses SageMaker Studio and wants to restrict studio user access to only the VPC. They also need to encrypt the data exchanged between the Studio app and the kernel gateway. Which configuration should they apply?

A company wants to test a new ML model in production with minimal risk before shifting full traffic. They have an existing real-time endpoint serving model version A. They need to route 5% of live traffic to model version B and monitor performance for 24 hours. Which TWO steps should they take? (Choose TWO.)

Which SageMaker built-in algorithm is best suited for detecting anomalous login attempts based on IP addresses and user behavior?

A company needs to deploy a model that processes large payloads (up to 1 GB) asynchronously. The results should be written to S3, and the team needs SNS notifications upon completion. Which SageMaker inference option is MOST suitable?

A data engineer needs to ingest streaming data from IoT devices into Amazon S3 for machine learning. The data arrives continuously and must be available for querying within minutes. Which service should be used to collect and deliver the streaming data to S3?

A company runs an online retail business and wants to build a product recommendation system. They have a dataset of customer purchases stored in Amazon S3 as CSV files. The dataset includes columns: 'customer_id', 'product_id', 'purchase_date', 'quantity', 'price', and 'category'. The data science team plans to use Amazon SageMaker to train a factorization machines model. During data exploration, they discover that the 'category' column has 1,200 unique values, and many categories appear only a few times. The 'product_id' column has 50,000 unique values. They want to include both features in the model. The team is concerned about the high cardinality of these features. Which approach should they take to prepare these features for the factorization machines model?

A company ingests daily log data into an S3 bucket. They need to update the existing ML training dataset with new data without reprocessing the entire history. Which two strategies should they adopt? (Choose two.)

An ML engineer is debugging a training job that is consistently failing due to an out-of-memory error. The engineer is using SageMaker's built-in XGBoost algorithm. Which Debugger rule can help identify the issue?

A team deploys a machine learning model using a SageMaker endpoint with an ML.T4 instance. After a week, they notice that the endpoint's CPU utilization is consistently below 10% and latency is low. However, the endpoint is incurring high costs. Which action should the team take to reduce costs while maintaining the ability to serve traffic?

A machine learning model is deployed on SageMaker and its predictions are used in a production application. The model's accuracy has degraded over time. What is the most likely cause?

A company's SageMaker endpoint is experiencing increased latency during peak hours. The endpoint uses a single ml.m5.large instance. The deployment is critical and must maintain low latency. Which action is MOST effective to reduce latency without sacrificing cost efficiency?

A data scientist is training a model on text data from customer reviews. The dataset contains a mix of English and Spanish reviews. The scientist wants to convert the text into numerical features for a classification model. Which approach is MOST appropriate for this multilingual dataset?

A team is optimizing a deep learning model for deployment on SageMaker using SageMaker Neo. Which THREE of the following are valid optimization techniques that Neo can apply? (Choose THREE.)

A machine learning engineer has configured a SageMaker Model Monitor schedule for data quality monitoring as shown in the exhibit. The schedule is set to run hourly. However, the engineer notices that the monitoring jobs are not producing output in the specified S3 bucket. What is the most likely cause?

A data scientist is using SageMaker to train a model and wants to reduce training costs without sacrificing performance. Which TWO actions should the scientist take? (Select TWO.)

An ML engineer needs to monitor the operational health of a SageMaker endpoint, specifically the time taken for the container to process an inference request and the overhead added by SageMaker. Which two CloudWatch metrics should they examine?

A company wants to share a trained model across multiple AWS accounts for inference. The model is stored in a central account's S3 bucket and needs to be deployed in other accounts' SageMaker endpoints. What is the recommended approach?

Refer to the exhibit. A data scientist is trying to use AWS Glue to read data from the S3 bucket `ml-data-bucket`. The Glue job fails with an access denied error. What is the most likely cause?

A team is evaluating classification models for a medical diagnosis application. The cost of a false negative is much higher than the cost of a false positive. Which metric should be optimized during model selection?

A data scientist is using Amazon SageMaker to train a linear regression model. After training, the scientist notices that the training and validation errors are both low, but the model performs poorly on new test data. What is the MOST likely cause?

A healthcare startup is building a model to predict patient readmission within 30 days. The data is stored in Amazon Redshift and includes patient demographics, admission history, lab results, and medication records. The data scientist extracts a sample of 10,000 records to Amazon S3 as CSV files for initial prototyping. During exploratory data analysis, they find that the 'age' column has values like '150', '0', and negative numbers. The 'diagnosis_code' column contains codes like 'E11', 'E11.9', and 'e11' (inconsistent formatting). The 'readmitted' target column has 60% 'Yes' and 40% 'No'. The data scientist wants to use AWS Glue DataBrew for data cleaning. Which combination of steps should they use?

A machine learning team trains a model in SageMaker and wants to track every step — from dataset version to hyperparameters to final model artifact — for reproducibility and audit compliance. Which SageMaker feature should they use?

A data scientist trained a model using SageMaker and wants to automate the retraining process when new data becomes available. Which AWS service is best suited to trigger a SageMaker training job based on an S3 event?

A financial services company uses SageMaker Studio. They require that all Studio traffic remains within the corporate network and that user notebooks cannot access the internet. Which TWO configurations should they implement? (Select TWO.)

A team wants to use AWS Step Functions to orchestrate a retraining workflow that is triggered when new data arrives in an S3 bucket. They also need to monitor model drift. Which event-driven approach should they use?

A data scientist is using SageMaker Experiments to track multiple training runs. They want to compare the F1 scores across runs. Which component should they use to log the F1 score?

A company is fine-tuning a large language model using reinforcement learning from human feedback (RLHF). Which THREE components are typically required?

A machine learning team is preparing a dataset for training a deep learning model. They notice that some features have very different scales: one feature ranges from 0 to 1, another from 0 to 100,000, and a third is a binary indicator (0/1). The model uses gradient descent. Which scaling method should be applied to ALL features to ensure stable and efficient training?

A company wants to secure data in transit between the client and SageMaker endpoint, and between containers in the same endpoint. Which THREE configurations should they apply? (Choose three.)

A company deploys a deep learning model to a real-time SageMaker endpoint. After deployment, users report high inference latency. Which action is the MOST effective first step to reduce latency?

A company has a dataset of 2 billion records stored as text files in Amazon S3. The data is partitioned by year and month. The data science team wants to read only the last 6 months of data for model training using SageMaker. To minimize data scanned and reduce costs, which approach should the team use?

A company uses SageMaker Model Monitor to detect drift. They want to receive notifications when drift is detected. Which TWO services can be used together to send notifications? (Choose TWO.)

A machine learning team wants to detect concept drift in a production model. Which TWO actions should they take? (Choose TWO)

A company is building a binary classifier for credit default prediction. The dataset is highly imbalanced (98% no default). They want to maximize recall for the minority class while maintaining reasonable precision. Which metric should be optimized during hyperparameter tuning?

A data scientist is performing feature selection for a linear regression model and wants to remove features that are highly correlated with each other to reduce multicollinearity. Which technique is BEST suited for this purpose?

A team uses SageMaker Pipelines to train and evaluate a model. They want to run the training step only if the data quality check passes, otherwise skip. Which TWO pipeline step types are required? (Select TWO.)

A company is building a real-time inference pipeline for an ML model. The raw data arrives in JSON format via Amazon Kinesis Data Streams. Before invoking the SageMaker endpoint, the data must be preprocessed to match the training data format. Which THREE steps should be included in the preprocessing function? (Select THREE)

A team uses SageMaker Clarify to monitor bias drift in production. They schedule weekly analysis. After a month, Clarify reports a significant increase in a bias metric. What should the team do first?

A financial services firm is training a fraud detection model using SageMaker. The dataset is highly imbalanced (0.1% fraudulent transactions). The model currently achieves 99.9% accuracy but only catches 5% of fraud cases. Which metric should the team prioritize to evaluate model performance?

A data engineer is ingesting streaming clickstream data from a website into Amazon S3 for ML training. The data arrives at a rate of 10,000 events per second, and the team needs near-real-time availability with minimal transformation. Which AWS service should the engineer use to ingest the data into S3 with the LEAST operational overhead?

An ML team is building a churn prediction model using customer data stored in Amazon S3. The data includes a high-cardinality categorical feature (customer_id) and several numerical features with missing values. The team wants to use Amazon SageMaker Data Wrangler for data preparation and then export the transformed data to Amazon SageMaker Feature Store. Which THREE steps should the team take?

A company has deployed a machine learning model on Amazon SageMaker and wants to automatically detect when the distribution of input features deviates significantly from the training data distribution. Which SageMaker feature should they use?

A company uses SageMaker Pipelines to train and register models. They want to automate the deployment of approved models from the model registry to a staging endpoint. Which service should they use to orchestrate the deployment workflow?

A media company uses SageMaker to host a real-time video recommendation model. The model is deployed on a single ml.c5.xlarge endpoint. During a major live event, traffic surges to 10 times the normal load, and the endpoint becomes unresponsive, causing high latency and errors. The team had set up an Application Auto Scaling target tracking policy based on CPU utilization with a target of 70%. However, scaling did not trigger quickly enough. After the event, the team reviews CloudWatch metrics and notices that CPU utilization never exceeded 70% during the surge, but memory utilization peaked at 95%. The model is memory-bound. The team wants to ensure the endpoint scales automatically before performance degrades during future events. What should the team do?

A company is training a deep learning model on Amazon SageMaker using a dataset stored in Amazon S3. The training job is taking a long time due to I/O bottlenecks. The data is in JSON lines format. Which data preparation step combined with SageMaker's best practices would most effectively reduce training time?

A company wants to use SageMaker Clarify to analyze bias in their training data and model predictions. Which TWO types of bias can Clarify detect? (Choose TWO.)

A data science team is using Amazon SageMaker to train and deploy a binary classification model. They want to continuously monitor the model for data drift in production. Which combination of AWS services and SageMaker features should they use to implement automated drift detection with minimal operational overhead?

A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset. The dataset contains a column with date strings in the format 'YYYY-MM-DD'. The data scientist wants to extract the year, month, and day as separate features. Which Data Wrangler transform should be used?

A company deploys a model for fraud detection. They need to monitor for bias after deployment, specifically whether the model's false positive rate changes across demographic groups over time. Which SageMaker feature should they use?

A company is preparing a dataset with a categorical feature that has over 1000 unique values. They need to create features for a random forest model. Which feature engineering approach is most scalable and effective in AWS for high-cardinality categories?

A data scientist is using SageMaker Automatic Model Tuning to optimize hyperparameters for an XGBoost model. They want to maximize AUC. Which search strategy is MOST appropriate for efficient exploration?

A company is training a large computer vision model using SageMaker. The training dataset is 500 GB and the model has 1 billion parameters. The team needs to minimize training time. Which distributed training strategy should they use?

A company needs to ensure that their SageMaker Studio environment is only accessible from within their corporate network and that all data processed in Studio remains encrypted. Which configuration should they use?

A company is building a fraud detection model on credit card transactions. The dataset contains a column 'merchant_id' with 50,000 unique values, many with low frequency. The team wants to avoid overfitting while preserving predictive signal. Which feature engineering approach is most appropriate?

A company is building a recommender system using implicit feedback (clicks) and explicit feedback (ratings). They plan to use Amazon SageMaker to train a model. The data includes user ID, item ID, timestamp, and rating (if any). Which TWO data preparation steps should the team perform? (Choose TWO.)

A company is building a machine learning model on customer transaction data stored in Amazon S3. The data includes columns with missing values in the 'age' field. The data scientist wants to impute missing values with the median age across all customers. Which approach is MOST efficient for preparing the data at scale?

Which TWO actions are recommended best practices for securing an Amazon SageMaker notebook instance? (Select TWO.)

A company is training a large NLP model on SageMaker and wants to reduce costs by using Spot Instances. Which TWO configurations should they implement to handle Spot interruptions gracefully?

A company wants to deploy a new model using a canary deployment strategy on SageMaker. Which two actions should they take? (Select TWO.)

A team has a SageMaker Pipeline that trains a model and registers it in the Model Registry. They want to automate the deployment of the approved model to a staging environment. Which event-driven approach should they use?

A financial services company is developing a real-time fraud detection model using XGBoost on SageMaker. They have millions of transactions daily and train a model weekly on 6 months of historical data. The training dataset is 500 GB in CSV format stored in S3. The training job uses an ml.p3.16xlarge instance with 8 GPUs, but training takes over 12 hours, which is too long for the weekly cadence. The data scientist notices that GPU utilization averages only 15% during training. The training script uses the SageMaker XGBoost container with default hyperparameters. Which combination of actions would MOST likely reduce training time? (Choose the best answer.)

A retail company is preparing a dataset for a machine learning model to predict customer churn. The dataset includes customer_id, signup_date, last_purchase_date, total_purchases, average_order_value, and churn_label. The data scientist notices that the 'total_purchases' column has missing values for 15% of the records. The company wants to use AWS Glue for data preparation. Which approach should the data scientist take to handle the missing values while minimizing bias and preserving data integrity?

A company is using SageMaker Automatic Model Tuning to optimize a regression model. They want to minimize the root mean squared error (RMSE). The tuner has completed 20 jobs, and the RMSE has plateaued. Which action should the data scientist take to potentially improve the results?

A data scientist wants to fine-tune a Llama 2 7B model using SageMaker for a text summarization task. The dataset is 10 GB. The budget is limited, so cost efficiency is important. Which THREE steps should the data scientist take? (Choose THREE.)

A data team is preparing data for a machine learning pipeline. Which TWO practices are best for ensuring data quality and reproducibility? (Choose two.)

A company wants to reduce costs for a real-time inference endpoint that experiences predictable traffic spikes during business hours and low traffic at night. Which auto-scaling policy is MOST cost-effective while maintaining performance?

A machine learning team is preparing a dataset for a regression model. The dataset contains numerical features that are on different scales (e.g., age 0-100, income 0-1,000,000). The team plans to use Amazon SageMaker to train a linear regression model. Which THREE data preparation steps should the team take to ensure the model performs well? (Select THREE.)

A company uses Amazon Rekognition to moderate user-generated images. They want to set up a monitoring system that alerts the team if the number of inappropriate images flagged by the model exceeds a threshold. Which combination of AWS services should they use?

A data scientist is using SageMaker Experiments to track multiple training runs for a PyTorch model. They want to compare metrics across runs and identify the best hyperparameters. Which TWO capabilities should they use? (Choose TWO.)

A data scientist is preparing a dataset for a multi-class classification problem. The dataset contains a categorical feature with 50,000 unique values (high cardinality). The scientist wants to reduce dimensionality while preserving predictive information. Which TWO approaches are appropriate? (Choose 2)

A data science team uses SageMaker notebooks to develop models. They want to automate the process of training and registering models whenever new data arrives in an S3 bucket. The team has limited DevOps experience and needs a solution that requires minimal maintenance. Which approach should the team use?

A financial services company uses a custom container on Amazon SageMaker to serve a fraud detection model. The model's inference latency has recently increased, causing timeouts for some requests. The team reviews the SageMaker logs and finds that the container is consuming more memory than allocated. What should the team do to maintain service quality while ensuring cost-effectiveness?

A company uses SageMaker Pipelines for model training and wants to incorporate model evaluation before deployment into production. Which THREE components are essential? (Choose three.)

A machine learning engineer is setting up a retraining pipeline that triggers when concept drift is detected. They plan to use CloudWatch Alarms to monitor the model's accuracy metric. When drift is detected, they want to automatically start a SageMaker training job. Which architecture should they use?

A team wants to automatically retrain a model when new labeled data arrives. Which SageMaker feature can orchestrate this workflow?

A team uses SageMaker ML Lineage Tracking to capture the metadata of their ML workflow. They want to query the lineage to see which model version was trained from a specific dataset. Which Lineage Tracking entity represents the dataset?

A machine learning engineer is preparing a dataset for a binary classification model. The dataset has 10,000 samples with a 1:100 class imbalance. The engineer needs to balance the classes before training. Which technique would create a balanced dataset without discarding majority class samples and without generating synthetic data?

A data scientist is building a time-series forecasting model for daily sales data. The data spans two years. To evaluate the model's performance, the data scientist needs to simulate a realistic rolling forecast scenario. Which data splitting strategy should be used?

A machine learning engineer needs to standardize features to have zero mean and unit variance before training a support vector machine. Which scaling method should they apply?

A company has a SageMaker endpoint that uses a trained model to classify images. The endpoint is experiencing high latency and the team suspects it is due to the model size. Which action can the team take to reduce latency without significantly impacting accuracy?

A hospital deploys a model to predict patient readmission risk. To comply with regulations, they must ensure that the model's predictions do not show bias against any demographic group over time. Which service should they use for ongoing monitoring?

A company uses Amazon SageMaker Pipelines to automate its ML workflow. The pipeline includes a training step and a model evaluation step. If the evaluation step fails, the pipeline should stop and notify the team. How should the company configure the pipeline?

A data scientist is using SageMaker Data Wrangler to prepare features for a classification model. Which TWO statements about feature engineering in Data Wrangler are correct?

A data scientist is using SageMaker Data Wrangler to prepare a large dataset. The data contains duplicate rows, which could bias the model. Which built-in step in Data Wrangler can automatically detect and remove duplicates?

A financial services company is developing a fraud detection model using Amazon SageMaker. They have a dataset with 10 million transactions, each with 300 features. The dataset is highly imbalanced (0.1% fraud). They have performed feature engineering and now need to split the data for training, validation, and test sets. The data is stored in CSV files in Amazon S3. They plan to use SageMaker's built-in XGBoost algorithm. To ensure proper evaluation and avoid data leakage, which data splitting strategy should they use?

A team is using SageMaker Pipelines to automate a training workflow. They need to ensure that if a step fails, the pipeline can resume from the failed step without reprocessing prior steps. Which TWO configurations are necessary? (Choose TWO.)

A machine learning team is building a model to predict customer churn. They have historical data that includes customer activity logs, each with a timestamp. The team wants to ensure that the training data does not contain any data leakage from the future. Which approach should they take when preparing the training and validation datasets?

A company stores its raw IoT sensor data in Amazon S3. The data is in CSV format and contains timestamps, sensor IDs, and readings. A data engineer needs to catalog this data for discoverability and querying by other team members. Which AWS service should they use to create a searchable metadata catalog?

A company is using SageMaker to train a neural network for image classification. The training job is taking too long. The team wants to reduce training time without sacrificing model accuracy. Which approach should they recommend?

A model deployed on SageMaker is returning inaccurate predictions for certain customer segments. The team suspects data drift. Which SageMaker feature should they use to continuously monitor input data distribution?

A team wants to apply a custom container for inference on SageMaker. The container needs to implement a web server that responds to API requests. Which protocol and port must the container listen on to be compatible with SageMaker hosting?

A startup wants to deploy a containerized ML application that includes both a model inference server and a preprocessing component in the same endpoint. Which SageMaker endpoint type supports running multiple containers?

An ML team is running multiple SageMaker endpoints for various models. The monthly cost is higher than expected. Which TWO actions would help reduce costs without negatively impacting performance?

A machine learning engineer is using SageMaker Processing to run a scikit-learn preprocessing script. The script reads a CSV file from S3, applies a StandardScaler, and writes the output. The job fails with a 'MemoryError'. Which change should the engineer make to the data preparation process?

A data engineer is using Amazon SageMaker Data Wrangler to prepare a dataset. The dataset contains a column 'review_date' with timestamps. The engineer wants to extract the day of the week as a new feature. How should this transformation be performed in Data Wrangler?

A company uses Amazon SageMaker to deploy a real-time inference endpoint. They notice increased latency in predictions during peak hours. Which should they investigate first to address the issue?

A company is using an Amazon SageMaker pipeline for automated retraining. The pipeline fails intermittently due to transient errors in the training job. Which steps should the team take to ensure the pipeline completes successfully? (Choose THREE.)

A company uses SageMaker to train a model. They want to ensure that training data is encrypted at rest and in transit, and that only authorized users can access the training artifacts. Which three steps should they take? (Choose three.)

A retail company is building a machine learning model to predict customer churn. The data engineering team has extracted customer transaction data from Amazon Aurora and stored it as CSV files in Amazon S3. The data includes customer IDs, transaction amounts, timestamps, and product categories. A data scientist discovers that the dataset contains several missing values in the 'transaction_amount' column for about 15% of the records. The data scientist also notices that the 'customer_id' column has some duplicate entries. The team wants to prepare the data for training a churn model using Amazon SageMaker. The data is approximately 50 GB in size. What should the data scientist do to handle the missing values and duplicates efficiently while preparing the data for training?

A data scientist trained a logistic regression model on a dataset with 100 features. After training, the training accuracy is 0.99 but validation accuracy is 0.75. Which action is MOST likely to reduce overfitting?

A data scientist is preparing a large dataset for training a machine learning model. The dataset contains missing values in several columns. Which approach is the MOST efficient for handling missing values in a large dataset using AWS services?

A team deploys a model on a SageMaker real-time endpoint using an ml.m5.xlarge instance. The model has high latency due to a large neural network. The team wants to reduce latency without changing the model code. Which option should they use?

A team is using SageMaker Automatic Model Tuning to optimize hyperparameters for an XGBoost model. They want to find the best configuration as quickly as possible, with a maximum of 50 training jobs. Which TWO strategies should they choose? (Choose TWO.)

A data engineer needs to ingest streaming clickstream data from a website into an S3 data lake for ML training. The data arrives continuously and must be written to S3 in near real-time. Which AWS service is best suited for this task?

A machine learning team deploys a custom container image for an Amazon SageMaker training job. The container needs to access an S3 bucket that contains sensitive data. The team wants to follow the principle of least privilege. How should the team grant access?

A company is deploying a large model (10GB) for real-time inference. The inference latency is too high. What optimization technique can help?

A company is deploying a multi-model endpoint using SageMaker to serve multiple models from a single endpoint. They notice that one model consumes excessive memory and impacts others. What is the BEST practice to isolate resource usage?

A data scientist wants to train a binary classification model using Amazon SageMaker. The dataset has 10,000 rows and 50 features. Which SageMaker built-in algorithm is MOST appropriate for this task?

A company is deploying a real-time inference endpoint for a natural language processing model using Amazon SageMaker. The model is a fine-tuned BERT variant. The endpoint has been running for two weeks with acceptable latency (average 200 ms). However, over the past 24 hours, the latency has increased to an average of 800 ms, and the number of simultaneous requests has doubled. The team expects traffic to continue to grow. The current endpoint configuration uses a single ml.m5.large instance. The model is loaded into memory once, and the inference framework is PyTorch. The team needs to maintain latency under 500 ms. Which course of action should the team take to address the latency increase while minimizing cost?

A data scientist wants to automate retraining of a model weekly and deploy the new model automatically after passing validation. Which AWS service combination is best?

A team wants to track and compare multiple machine learning experiments, including hyperparameters, metrics, and artifacts. They are using Amazon SageMaker. Which AWS service or feature should they use to achieve this?

A machine learning team is deploying a fraud detection model using SageMaker. They use the SageMaker Model Registry to track model versions. They want to automatically deploy the latest approved model to a production endpoint whenever a new model version is approved. The team uses a CI/CD pipeline with AWS CodePipeline. The pipeline currently includes a source stage (S3), a build stage (CodeBuild), and a deploy stage (manual approval). They want to automate the deployment of approved models. Which solution will meet these requirements with the least operational overhead?

A company uses Amazon SageMaker to deploy a model for real-time inference. They want to perform A/B testing between two model versions. Which TWO actions should the company take to set up A/B testing? (Choose TWO.)

A financial services company needs to deploy a SageMaker endpoint that processes sensitive customer data. The security policy requires that all data in transit between the endpoint and the application must be encrypted, and that the endpoint cannot be accessed from the public internet. Additionally, model containers must not be able to initiate outbound internet requests. Which combination of settings meets these requirements?

A company wants to maintain multiple versions of a trained model in a central repository and track metadata such as training metrics, hyperparameters, and approval status. Which SageMaker feature should they use?

A data engineer is designing a pipeline to process customer reviews for sentiment analysis. The text data contains punctuation, common words like 'the' and 'and', and emojis. Which sequence of preprocessing steps should they apply in Amazon SageMaker Data Wrangler?

A company trained a model using SageMaker and wants to deploy it with low latency for real-time inference. Which SageMaker feature is MOST suitable?

A team wants to deploy a single SageMaker real-time endpoint that serves both a PyTorch model for NLP and a TensorFlow model for image classification. Each model requires a different inference container. Which two features can they use together to achieve this? (Select TWO.)

A company is building a real-time fraud detection system using Amazon Kinesis Data Streams. The data must be joined with a reference table (e.g., customer profile) that is stored in Amazon DynamoDB and updated frequently. The enriched data will be used for ML predictions. Which THREE AWS services should the company use to build this streaming pipeline? (Select THREE.)

A model has high training accuracy but low validation accuracy. Which action is least likely to reduce overfitting?

A company uses SageMaker Model Registry to manage model versions. They have a cross-account deployment requirement: models approved in the development account must be deployed to a production account. Which approach is the MOST secure and recommended?

A company is using Amazon SageMaker to train a model on sensitive customer data. The security team requires that all data be encrypted in transit and at rest, and that the training job does not have internet access. Which configuration should the team use to meet these requirements?

A machine learning engineer wants to deploy a pre-trained foundation model for text summarization using SageMaker JumpStart. Which of the following is a primary cost consideration when deploying such a model?

A data engineer is building a feature store using Amazon SageMaker Feature Store. The team needs to store features that are updated frequently and require low-latency retrieval for real-time inference. Which type of store should the engineer use?

A security team requires that all data used by a SageMaker training job be encrypted at rest using a customer-managed KMS key. The data is stored in an S3 bucket that is already encrypted with SSE-KMS. What additional configuration is needed on the SageMaker training job?

A data scientist is preparing a dataset for a multiclass classification problem. The dataset has a categorical feature with 50 unique values (medium cardinality) and a target variable with 5 classes. The scientist wants to encode the categorical feature in a way that captures the relationship with the target while keeping the number of output features manageable. Which TWO encoding methods should the scientist consider? (Select TWO.)

A company uses SageMaker Ground Truth to create a labeled dataset, then trains a model using SageMaker Training. They want to automate the pipeline so that whenever a labeling job is completed, it triggers the training job. Which architecture meets this requirement with minimal latency?

A healthcare company deploys a model to predict patient readmission risk. The model was trained on historical data and is now showing signs of concept drift. The team needs to implement a monitoring solution that can detect drift and automatically retrain the model when drift is detected. Which THREE steps should the team take to build this solution? (Choose THREE.)

A team wants to use SageMaker Clarify to monitor bias in their production model predictions. They have configured a bias drift monitor. What does SageMaker Clarify compare to detect bias drift?

A data science team wants to deploy a real-time inference endpoint on Amazon SageMaker for a model that requires low latency (under 100 ms). The model is a small ensemble of three tree-based models, each about 50 MB. The team expects around 1000 requests per minute, with occasional spikes to 5000 requests per minute. Which instance type and deployment strategy would be MOST cost-effective while meeting the latency requirement?

A machine learning engineer is using SageMaker Data Wrangler to perform data validation. Which step should be added to the pipeline to ensure data quality before training?

A company has deployed a model to a SageMaker endpoint. The security team wants to ensure that all traffic between the endpoint and the client application is encrypted and that the endpoint is not accessible from the internet. Which TWO actions should the company take? (Choose TWO.)

A data scientist is using SageMaker built-in XGBoost algorithm for a regression problem. Which metric is most appropriate as the objective metric for hyperparameter tuning?

An ML engineer needs to split a dataset into training, validation, and test sets. The dataset has a time-based column that should not be leaked. Which split method is most appropriate?

A data engineer is designing an ETL pipeline using AWS Glue to transform raw data from S3 into a curated set for ML training. The data contains personally identifiable information (PII) that must be masked before being used by data scientists. Which TWO actions should the engineer take? (Choose TWO.)

A data scientist is training a binary classification model using Amazon SageMaker. The dataset has a severe class imbalance (95% negative, 5% positive). The model achieves 99% accuracy but fails to identify positive cases correctly. Which action should the data scientist take to improve the model's ability to detect positive cases?

A company deploys a model using SageMaker real-time endpoint with auto scaling. They observe that during a traffic spike, the endpoint quickly scales up to 10 instances, but after the spike, it takes a long time to scale down, leading to high costs. The scaling policy is based on a simple average CPU utilization threshold. Which adjustment would optimize the scaling down behavior?

An MLOps engineer is setting up a SageMaker endpoint for a model that performs inference on large images. The model is containerized and expects input in a specific format. The team wants to preprocess the images (resize and normalize) before passing them to the model. What is the most efficient way to implement this?

A company deploys a model in a different AWS account for production. They want to allow the production account to invoke the model endpoint from a SageMaker notebook in the same account, while keeping the model in the original account. Which configuration is required?

A company uses SageMaker Autopilot to build a regression model predicting house prices. After the experiment completes, the company wants to understand why the model makes certain predictions. Which TWO SageMaker features can provide this explainability? (Choose TWO.)

A data engineer is using AWS Glue to run an ETL job that joins two large datasets and writes the output to S3 for ML training. The job is failing due to out-of-memory errors. Which THREE actions can help resolve this issue? (Select THREE.)

A company is using SageMaker to train a model for image classification. The training dataset contains 100,000 labeled images. The team wants to use a pre-trained model to reduce training time. Which SageMaker feature should they use?

An e-commerce company uses Amazon SageMaker to train a model that predicts click-through rates. The training data includes a timestamp column 'click_time' and a categorical feature 'device_type' (8 values). They notice that the model's performance degrades over time because the data distribution shifts. They want to ensure the training data represents the most recent behavior. The data is stored in a daily partitioned S3 bucket (e.g., s3://bucket/data/2024-01-01/). The total dataset size is 500 GB. Which approach should they take to prepare the training data while minimizing bias and cost?

An ML team is using SageMaker Model Registry to manage model versions. After training a new model version, they register it with an 'Approved' status. The CI/CD pipeline automatically deploys the latest approved model to a staging endpoint. However, the pipeline fails with an error: 'Cannot deploy model because the model version is not approved.' The model version is clearly approved in the registry. What is the most likely cause?

A company uses SageMaker Neo to compile a trained model for deployment on edge devices. What is the primary benefit of using Neo?

A data scientist is using SageMaker built-in linear learner algorithm for a regression problem. The dataset has 10 features, some have missing values, and the target variable is right-skewed. The data scientist wants to handle missing values and transform the target variable to improve model performance. Which data preparation steps should the data scientist take?

A company wants to track the lineage of their ML models for reproducibility and auditability. Which THREE services or features should they use together to achieve this? (Choose THREE.)

A data science team needs to deploy a trained PyTorch model for real-time inference with sub-100ms latency. The model fits on a single GPU. Which SageMaker inference option is MOST cost-effective while meeting the latency requirement?

A machine learning team is building a model using a dataset that contains a mix of numerical and categorical features. The categorical features have high cardinality (e.g., zip code with thousands of unique values). The team wants to use Amazon SageMaker for training. Which technique should the team use to encode the high-cardinality categorical features effectively?

A company wants to reduce costs for a SageMaker real-time endpoint that has variable traffic. Which feature allows the endpoint to automatically adjust instance count based on demand?

A company wants to use SageMaker to deploy a model that requires GPU acceleration for inference but wants to minimize costs by using a smaller attached GPU. Which options can they use? (Select TWO.)

An ML engineer is fine-tuning a foundation model using RLHF on SageMaker. Which THREE components are essential for this workflow? (Select THREE.)

A data scientist is training a deep learning model on Amazon SageMaker and notices that the training loss decreases but the validation loss starts increasing after a certain number of epochs. The model is likely overfitting. Which SageMaker feature can they use to detect and diagnose this issue during training?

A company is building a fraud detection model on an imbalanced dataset (99% legitimate, 1% fraudulent). To improve recall on the minority class, they want to resample data. Which combination of techniques should they use?

A data engineer needs to integrate a new streaming data source into an existing ML pipeline. The data arrives as JSON records and must be transformed to Parquet format, partitioned by date, and stored in Amazon S3. The engineer also needs to catalog the data for querying with Amazon Athena. Which service should be used to perform the transformation and cataloging?

A financial services company needs to enforce that only approved model versions are deployed to production. They use SageMaker Model Registry to track versions, with an approval workflow. Which action must they take in the model registry to ensure only approved models can be deployed?

A machine learning engineer needs to prepare a dataset with a target variable that has severe class imbalance (1:1000). The dataset has 100,000 rows and 200 features. Which approach should the engineer use to address the class imbalance before training a classification model?

A team wants to fine-tune a pre-trained Hugging Face transformer model for text classification using SageMaker. They have a custom training script. Which SageMaker estimator should they use?

A machine learning engineer is preparing a dataset for a binary classification model. The dataset has a severe class imbalance (95% class A, 5% class B). The engineer wants to use Amazon SageMaker to train the model. Which data preparation technique should the engineer apply to the training dataset to address the imbalance and improve model performance?

A data scientist is preparing a large dataset for training a binary classification model. The dataset has a severe class imbalance (95% negative, 5% positive). Which data preparation technique should the scientist use to address this imbalance without losing too much data?

A financial services company needs to build a fraud detection model using historical transaction data. The dataset has a timestamp column, and the model must be evaluated on its ability to detect fraud in future unseen transactions. The data is imbalanced (fraud is rare). Which TWO data splitting strategies should the engineer use for model validation? (Select TWO.)

A financial services company is building a fraud detection model using historical transaction data stored in Amazon S3. The data includes features such as transaction amount, merchant category, time of day, and user location. The data scientist observes that the 'merchant_category' column is a text attribute with over 200 unique values. Additionally, the 'transaction_amount' column has a long-tail distribution with extreme outliers. The dataset is 200 GB in size, and the company wants to use Amazon SageMaker for model training. The data scientist needs to engineer features that capture the high-cardinality category and reduce the impact of outliers. What is the MOST efficient and effective approach to prepare this data?

A marketing company is preparing a dataset to train a logistic regression model to predict whether a customer will click on an online ad. The dataset includes 1 million records with features: customer_age (numeric), income (numeric), education_level (ordinal: high school, bachelor, master, PhD), and ad_category (categorical: 50 unique values). The data is stored in a CSV file in Amazon S3. The data scientist plans to use Amazon SageMaker's built-in linear learner algorithm. The data scientist needs to preprocess the data before training. What is the correct sequence of data preparation steps that should be applied to this dataset to ensure optimal model performance?

A team is deploying a deep learning model on a SageMaker real-time endpoint. The model has high memory requirements, and the team wants to minimize instance cost while ensuring the endpoint can handle up to 10 concurrent requests. They plan to use a single ml.p3.2xlarge instance (8 vCPUs, 61 GB memory). Which SageMaker endpoint configuration will allow the endpoint to handle 10 concurrent requests without errors?

A machine learning team has a model that needs to serve predictions with very low latency (under 10 ms) for a real-time web application. The model is a small ensemble of three neural networks that fits in memory. Which SageMaker inference option is MOST appropriate?

A company deployed a machine learning model on an Amazon SageMaker real-time endpoint. Over several weeks, they notice that inference latency has been gradually increasing, especially during peak business hours. The model and instance type have remained unchanged. What is the most likely cause of the increased latency?

A data science team deploys a regression model to Amazon SageMaker for real-time inference. After one month, the model's prediction errors increase significantly, but data distributions remain unchanged. Which monitoring approach is MOST suitable for detecting this issue?

A team needs to deploy a new model version to production while minimizing risk. They want to route 5% of live traffic to the new model and 95% to the current model, and then gradually increase the new model's traffic. Which SageMaker deployment pattern should they use?

A machine learning engineer is preparing a dataset that contains both numerical and categorical features. The categorical features have high cardinality (e.g., zip code with thousands of unique values). Which technique is most appropriate for encoding these high-cardinality categorical features?

An ML team is developing a regression model using Amazon SageMaker. They have a 100 GB CSV dataset stored in Amazon S3. The data is contained in a single large file. They launch a SageMaker training job with an ml.p3.8xlarge instance using a custom Docker container. The training script loads the data using pandas' read_csv from S3 directly. The team observes that the training job takes over 24 hours, and CloudWatch metrics show: GPU utilization is consistently above 90%, but CPU utilization is below 30%. Network I/O is moderate, and disk I/O is low. The team has already tried switching to a larger instance type (ml.p3.16xlarge) with no significant improvement. They need to reduce training time. Which action is MOST likely to achieve this?

A company uses Amazon SageMaker Ground Truth to label images for object detection. They want to minimize labeling costs while maintaining high accuracy. Which feature should they enable?

Refer to the exhibit. The Glue job reads a CSV file and attempts to write to a Parquet table. What is the most likely cause of this error?

A data scientist is using Amazon SageMaker Processing to run a feature engineering job. The job requires installing additional Python libraries not included in the default SageMaker containers. Which approach should the data scientist use to include these libraries?

A data science team is using Amazon SageMaker Pipelines to orchestrate a multi-step workflow that includes data preprocessing, training, and model evaluation. They want to reuse the preprocessed data across multiple pipeline executions without re-running the preprocessing step if the source data hasn't changed. What should they configure?

A data scientist is training a large model on SageMaker and wants to reduce training time by using multiple GPUs. The model is small enough to fit on a single GPU but training is slow. Which SageMaker feature should be used?

A data science team at a financial services company is deploying a real-time fraud detection model using Amazon SageMaker. The model is a gradient boosting classifier trained on historical transaction data. The model is deployed to a SageMaker endpoint with an ML.M5.LARGE instance for real-time inference. After deployment, the team observes that the endpoint's latency spikes to over 2 seconds during peak hours (10:00-12:00 and 14:00-16:00), causing timeouts for client applications. The average latency during off-peak hours is 200 ms. The team has enabled auto-scaling with a target average CPU utilization of 70%, but the endpoint still experiences high latency during peak hours. The instance count never scales beyond 2 instances during peaks. The model size is 500 MB, and each request includes 200 features. The team needs to reduce latency to under 500 ms at the 99th percentile during peak hours without increasing costs beyond the current budget. Which course of action should the team take?

A company uses SageMaker training jobs that need to access data in an S3 bucket in a different AWS account. The bucket uses a bucket policy that allows access only from a specific VPC. How should they configure the training job?

A data scientist is using SageMaker built-in XGBoost algorithm for a binary classification task. Which objective metric is MOST appropriate for SageMaker Automatic Model Tuning to maximize?

A machine learning engineer is training a deep learning model on SageMaker and notices that the training loss decreases rapidly in the first few epochs but then plateaus. The validation loss starts increasing after 10 epochs. Which action should the engineer take to improve generalization?

A company operates an IoT platform that ingests sensor data from thousands of devices. Data is streamed via Amazon Kinesis Data Streams and stored in an S3 bucket using a Kinesis Firehose delivery stream, which writes data in 5-minute windows. The data is then used to train a machine learning model for anomaly detection. Recently, the data science team noticed that the training dataset is always missing the last 5 minutes of events from the end of each day. The S3 objects show that the last delivery stream buffer window is incomplete. The data engineer checked the Kinesis Firehose metrics and found no delivery errors or data loss, but the 'IncomingBytes' and 'IncomingRecords' metrics show consistent data for all periods. The S3 bucket has Lifecycle policies that do not delete objects. The team suspects the issue is related to the data preparation pipeline. Which course of action would correctly resolve the missing data problem?

An ML engineer needs to compile a trained TensorFlow model to run efficiently on a target edge device with an ARM CPU. Which AWS service should they use?

A data scientist is working with a dataset that contains missing values in several numerical columns. The missing data is not completely at random (MNAR). The scientist wants to minimize bias in the imputed values. Which imputation strategy is most appropriate?

A company uses SageMaker JumpStart to deploy a foundation model for a summarization task. They want to minimize costs while still meeting a latency requirement of under 2 seconds. Which option should they consider?

A team is fine-tuning a foundation model using LoRA for a text summarization task. They want to reduce memory footprint during training. Which technique should they combine with LoRA?

A financial services company ingests transaction data from multiple sources into an S3 data lake. They want to use AWS Glue to catalog this data and make it queryable by Amazon Athena. The data schema changes frequently as new sources are added. Which AWS Glue feature should they enable to automatically detect and update the schema?

An ML team uses SageMaker to deploy a model for real-time inference. They want to monitor and improve cost efficiency. Which THREE actions should they take? (Select THREE.)

A data science team deploys a machine learning model to a SageMaker endpoint for real-time inference. They need to monitor the model for feature distribution drift over time to ensure the model's predictions remain accurate. Which AWS service should they use?

A machine learning engineer needs to split a dataset for binary classification where the positive class represents only 2% of the data. Which data splitting strategy ensures that both training and test sets maintain the same class proportion as the original dataset?

A data scientist is training a binary classification model using Amazon SageMaker. The dataset is highly imbalanced (95% negative class, 5% positive class). The model is evaluated on a held-out test set, and the F1 score is 0.12. The data scientist wants to improve the F1 score. Which two actions should the data scientist take? (Choose two.)

A company is using SageMaker Pipelines to automate a multi-step ML workflow. The pipeline includes data preprocessing, training, and model evaluation. The team wants to ensure that if the evaluation step fails, the pipeline stops and sends an alert to the operations team. Which SageMaker Pipelines feature should they use?

A data science team uses SageMaker to train models. They need to track the lineage of each model, including the dataset used, training job, and hyperparameters. Which TWO SageMaker features can they use together? (Select TWO.)

Which THREE steps should be taken to optimize a large-scale distributed training job on SageMaker? (Choose 3.)

A data science team uses SageMaker Pipelines to orchestrate their ML workflow. They noticed that even when source data hasn't changed, the pipeline re-runs all steps, wasting compute time. What should they enable to avoid redundant runs?

A company is building a time series forecasting model using SageMaker DeepAR. The raw data is a CSV with columns: timestamp, item_id, and value. What is the correct data format required for DeepAR training?

A data scientist is preparing a dataset for a machine learning model that predicts customer churn. The dataset contains a column 'CustomerID' that is a unique identifier. What should the data scientist do with this column before training the model?

Refer to the exhibit. A SageMaker Processing job fails with the following error log. Which change during data preparation would resolve the issue?

A data scientist is using SageMaker Experiments to track multiple training runs. They want to compare runs based on the objective metric and visualize performance. Which THREE steps should they perform? (Choose THREE.)

A company wants to deploy a machine learning model that was trained on-premises using TensorFlow. The model is a TensorFlow SavedModel. The company uses AWS and wants to minimize operational overhead. Which deployment option meets these requirements?

A company is deploying a machine learning model using Amazon SageMaker. The model is a large deep learning model that requires GPU for inference. The company expects unpredictable traffic patterns with occasional bursts. They want to minimize cost while ensuring low latency during bursts. Which TWO actions should they take? (Select TWO.)

A machine learning engineer deploys a new model version to a SageMaker endpoint with production variants. They want to gradually shift traffic from the old model to the new model, monitoring for errors, and automatically roll back if the error rate exceeds 5%. Which deployment pattern should they use?

A data scientist wants to quickly build a binary classification model without writing any code. Which SageMaker feature is MOST suitable?

A team uses SageMaker Pipelines to retrain a model nightly. They want to skip the training step if the new data is unchanged (same checksum as previous run) to save cost and time. Which pipeline configuration achieves this?

A team wants to ensure that their SageMaker training jobs cannot access the internet for security reasons. However, they need to download a public PyTorch package for training. Which TWO steps should they take? (Choose TWO.)

A financial services company must deploy a SageMaker endpoint that processes sensitive customer data. They require that all traffic between the endpoint and the model containers be encrypted, and that the endpoint cannot be accessed from outside a specific VPC. Which combination of settings should they use?

A machine learning engineer is using SageMaker Debugger to detect if a neural network has dead ReLU units during training. Which built-in rule should they enable?

A machine learning engineer is building a regression model to predict house prices. The feature 'square_footage' has values ranging from 500 to 10,000, while 'num_bedrooms' ranges from 1 to 10. Which preprocessing step is most critical before training a model that uses gradient descent?

A company has 200 small PyTorch models that are each used infrequently but need to be available for real-time inference. To minimize costs, they want to host all models on a single endpoint. Which SageMaker feature should they use?

An organization wants to automate ML retraining using an event-driven architecture. Which THREE services should they combine? (Select THREE.)

A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset. The dataset contains a column with missing values, a column with outliers, and a column with text data. The scientist wants to use built-in transforms to handle these issues. Which THREE transforms are available in Data Wrangler for these tasks? (Select THREE.)

A machine learning engineer is building a real-time fraud detection pipeline using Amazon Kinesis Data Streams. The data must be prepared (e.g., feature engineering, normalization) before being fed into a SageMaker endpoint. Which TWO steps should the engineer implement to ensure low-latency data preparation?

A company wants to deploy a trained XGBoost model for batch inference on a large dataset stored in S3. The inference job should be cost-effective and does not require real-time responses. Which SageMaker inference option should they use?

A company is building a recommendation system and has trained a matrix factorization model using SageMaker. They want to evaluate the model's performance using precision at k (P@k) and recall at k (R@k). They have a test set of user-item interactions. The data scientist implements a custom evaluation script that computes these metrics, but the precision values are consistently zero. What is the most likely cause?

A machine learning engineer is troubleshooting a model that is producing unexpectedly low accuracy in production. The engineer examines the model's training data and finds that the distribution of the target variable in production is significantly different from the training set. What type of drift is the model experiencing?

An e-commerce company uses Amazon SageMaker to deploy a real-time inference endpoint for product recommendations. The endpoint receives bursty traffic, with occasional spikes. The company wants to minimize cost while ensuring that latency remains under 100 ms. Which approach should the company take?

A company is fine-tuning a large language model using LoRA with a Hugging Face estimator in SageMaker. They want to reduce memory usage during training. Which instance type is most cost-effective for this workload?

A data scientist is preparing a dataset with a categorical feature that has 20 levels. The target variable is continuous. Which THREE encoding methods are appropriate for this scenario? (Select THREE.)

A media company uses SageMaker endpoints to serve a model that predicts video engagement. They have two production variants: Variant A (ml.c5.large) for regular traffic and Variant B (ml.c5.xlarge) for burst traffic. They use weighted routing (90% to A, 10% to B). Recently, during peak hours, Variant A's latency increase causes many requests to time out. The metrics show that both variants are under similar CPU load, but the number of concurrent requests to Variant A is very high. The team wants to ensure that burst traffic is handled properly without manual intervention. What should they do?

A company has a SageMaker endpoint that was deployed successfully and is in service. However, when the team sends test inferences using the InvokeEndpoint API, they receive a 500 internal server error. The endpoint logs in CloudWatch show a stack trace indicating 'OutOfMemoryError: Java heap space'. The model is a large XGBoost model (2 GB) and the endpoint is using an ml.m5.large instance with 8 GB of memory. What is the MOST likely cause and solution?

A company uses AWS Glue to run ETL jobs that prepare data for machine learning. The data is stored in Amazon S3 in Parquet format. A data engineer notices that the Glue job is running slowly and consuming a lot of resources. What is the MOST cost-effective way to improve the performance of the Glue job?

A company deploys a SageMaker model using AWS KMS for encryption at rest. They have a compliance requirement to rotate the KMS key every year without causing downtime for the inference endpoint. Which approach should they take?

Which TWO data storage options are commonly used by Amazon SageMaker Feature Store for offline and online storage?

A data science team is building a model to predict fraudulent transactions. The dataset has 1 million legitimate transactions and only 1,000 fraudulent ones. They plan to use Amazon SageMaker to train a model. Which data preparation technique should they apply to address the severe class imbalance before training?

A data scientist is trying to create a SageMaker endpoint configuration with 6 instances of ml.c5.large for a production variant. The creation fails with the error shown in the exhibit. Which action should the data scientist take to resolve this issue?

A machine learning team is setting up Model Monitor for a deployed model. Which THREE factors should they consider when configuring the monitoring schedule? (Select three.)

A machine learning engineer observes that model performance on a SageMaker endpoint has degraded over the past week. Ground truth labels are available with a 2-day delay. The engineer wants to automatically trigger a retraining pipeline when prediction quality drops below an acceptable threshold. Which approach is most appropriate?

A data scientist is performing feature engineering for a dataset with both numerical and categorical features. The data scientist wants to apply transformations that preserve the interpretability of the features. Which TWO transformations should the data scientist use? (Select TWO)

A team wants to monitor the number of requests and latency of their SageMaker endpoint using a unified dashboard. Which AWS service should they use to create a custom dashboard with these metrics?

A data scientist is using Amazon SageMaker Data Wrangler to create a feature engineering pipeline for a dataset with both numeric and categorical features. The scientist wants to apply transformations that are appropriate for a linear model. Which THREE transformations should the scientist apply? (Choose THREE.)

A team uses SageMaker real-time endpoints for inference. They want to deploy a new model version and compare its performance with the current version under live traffic without affecting user experience. Which method should they use?

A company uses Amazon SageMaker Ground Truth to create labeled datasets for object detection. The output must be in COCO format for downstream model training. How should the data preparation process be configured?

A data engineer needs to convert a JSON dataset to Parquet format for efficient querying with Amazon Athena. The JSON files are in an S3 bucket. Which service can perform this conversion with minimal coding?

A company uses SageMaker endpoints with auto-scaling. The endpoint is experiencing high latency during peak hours. The metrics show CPU utilization is low but memory is high. What is the most likely cause?

Which TWO actions are recommended best practices when preparing training data for a machine learning model in AWS? (Choose two.)

A team is using SageMaker Pipelines to automate retraining and deployment. They want to trigger the pipeline automatically when new training data is available in an S3 bucket. Which approach should they use?

A data scientist is preparing a dataset for a regression model that predicts house prices. The dataset includes a `neighborhood` feature with 500 distinct categories. The data scientist wants to encode this feature without increasing dimensionality too much and while capturing the target relationship. Which encoding technique should be used?

A company wants to deploy a single model that processes images from a production line. The images are uploaded to an S3 bucket every few minutes, and the inference results must be stored back to S3. The team wants to avoid paying for idle compute and prefers a fully managed, on-demand solution. Which SageMaker inference option should they use?

A data scientist is working on a time series forecasting problem. The dataset contains a column 'sales' with occasional negative values due to returns. The model expects non-negative input. Which data preparation step should be taken?

Refer to the exhibit. A user is unable to invoke a SageMaker endpoint. The IAM policy shown is attached to the user. Which permission is missing to allow invocation?

A company uses Amazon SageMaker Feature Store to store features for a real-time recommendation model. The feature data is updated continuously, and the model must use the most recent feature values for each user at inference time. Which type of Feature Store should the company use for serving features to the model?

A company uses Amazon SageMaker Data Wrangler to create a data flow for a classification model. The dataset contains a high-cardinality categorical feature 'product_id' with 50,000 unique values. The data scientist wants to reduce dimensionality while preserving predictive power. Which approach is most effective?

A financial services company deploys a credit risk model using an Amazon SageMaker endpoint with data capture enabled. The model uses a custom container. The compliance team requires that all inference requests and responses are logged to an S3 bucket with server-side encryption using AWS KMS. The IAM role for the endpoint has the following policy. What must be added to meet the compliance requirement?

A machine learning team needs to monitor a deployed model for both data drift and concept drift. Which TWO approaches should they implement? (Select TWO.)

An MLOps team is designing a CI/CD pipeline for deploying machine learning models to production on Amazon SageMaker. They want to ensure that the deployment process is automated and that models are automatically rolled back if performance degrades. Which of the following AWS services or features should they use to achieve this? (Choose THREE.)

A healthcare company is developing a predictive model to identify patients at risk of readmission within 30 days after discharge. The dataset contains electronic health record (EHR) data from multiple hospitals, stored as Parquet files in Amazon S3. The data includes patient demographics, diagnoses (ICD-10 codes), medications, lab results, and length of stay. A data scientist notices that the 'lab_result' column has a high number of null values (over 60%) because some tests are not applicable to all patients. Additionally, the 'diagnosis_code' column has over 10,000 unique ICD-10 codes. The company wants to build a model that complies with HIPAA and performs well. The data scientist must prepare the features efficiently using AWS services. Which combination of steps should the data scientist take? (Assume the company can use any AWS service.)

An ML engineer runs the CLI command shown in the exhibit. However, the training job fails immediately with an error: 'Unable to assume role'. What is the most likely cause?

A company is using Amazon SageMaker to deploy a model for real-time inference. The model requires access to a private S3 bucket that contains reference data. The company wants to ensure that the endpoint can access the S3 bucket without using a public internet connection. Which TWO actions should they take? (Select TWO.)

A team is deploying a model that requires low-latency inference for real-time predictions. They are using a SageMaker endpoint with a single instance. During testing, they observe high latency. Which change would most effectively reduce latency?

A data engineer is using Amazon SageMaker Ground Truth to create a labeled dataset for an object detection task. The dataset contains millions of images, and the labeling budget is limited. Which approach can reduce labeling costs while maintaining high model accuracy?

A company wants to deploy a PyTorch model on SageMaker using the NVIDIA Triton Inference Server for GPU acceleration. They have an existing Triton configuration. Which approach should they take?

A team uses AWS Auto Scaling for a SageMaker real-time endpoint. They notice that when scaling in, the latest instance is always terminated first, causing disruption to recent requests. How can they configure the scaling policy to terminate the oldest instance first?

A company needs to update a model in production without any downtime. They currently have a single real-time endpoint serving traffic. Which approach allows them to deploy a new model version and switch traffic gradually while being able to roll back quickly?

An ML team wants to use Amazon SageMaker Ground Truth to create a labeled dataset for a multi-class image classification task. They have a large set of unlabeled images and want to minimize labeling costs while maintaining high accuracy. Which Ground Truth feature should they enable?

Refer to the exhibit. A SageMaker execution role has the IAM policy shown. The team attempts to run a training job that writes results to 's3://my-bucket/training/output/model.tar.gz'. What will happen?

Which SageMaker feature automatically generates model cards, feature importance, and bias reports without requiring manual coding?

A data scientist is using Amazon SageMaker Data Wrangler for feature engineering on a large dataset stored in S3. The dataset has a column 'ProductCategory' with 1000+ unique values. To reduce dimensionality, they want to group categories that appear less than 1% of the time into an 'Other' category. Which Data Wrangler transform should they use?

A data scientist deploys a model and wants to monitor the endpoint's invocation latency. They notice that the CloudWatch metric 'ModelLatency' is high, but 'OverheadLatency' is low. Which statement correctly interprets these metrics?

A data engineer is preparing a large dataset of 10 TB for ML training on Amazon SageMaker. The data is stored in Amazon S3 as CSV files. To reduce training time and cost, the engineer wants to use a columnar format that is optimized for analytical queries. Which format should the engineer convert the data to?

A company deploys a real-time inference endpoint and wants to be alerted if the number of 4XX errors exceeds 10 per minute over a 5-minute period. Which steps should they take?

A data scientist is using SageMaker to train a linear regression model. After training, they evaluate the model on the test set and get an R² of 0.95. However, when they deploy the model to a SageMaker endpoint and run predictions on new data, the predictions are far off. What is the most likely cause?

A machine learning engineer notices that the latency of a SageMaker endpoint has increased over time. They need to identify which component (model inference vs. pre/post-processing) contributes most to the latency. Which CloudWatch metrics should they examine?

Refer to the exhibit. A team receives an error when running a SageMaker Model Monitor schedule for data quality. What should they do to resolve this issue?

A data scientist is preparing a dataset for a linear regression model. The dataset has a few missing values in a numerical feature with a normal distribution and no outliers. Which imputation method is most appropriate?

A company wants to deploy a machine learning model that makes real-time predictions for a mobile app. The model is a deep neural network with a large model size (500 MB). Which SageMaker endpoint configuration is most cost-effective while meeting low-latency requirements?

A machine learning engineer is deploying a PyTorch model for real-time inference on SageMaker. The model requires GPU for low-latency predictions. The deployment fails with the error: 'The primary container does not support the requested instance type.' The instance type is ml.p3.2xlarge. Which action should the engineer take to resolve the issue?

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

A data scientist is training a regression model in Amazon SageMaker. The dataset contains missing values in several features. The scientist wants to handle missing values as part of the training pipeline to ensure consistency between training and inference. Which approach should the scientist use?

A data scientist is training a model using SageMaker and wants to use spot instances to reduce costs. The training job is checkpointed every 5 minutes. However, the job gets interrupted frequently and never completes. What is the MOST likely cause?

A machine learning engineer is deploying a TensorFlow model for real-time inference. The model has high latency on CPU. Which TWO actions can reduce inference latency? (Choose two.)

A data scientist is using Amazon SageMaker Ground Truth to create a labeled dataset for an object detection model. The dataset contains 1 million images, and the team wants to reduce labeling cost by labeling only the most informative samples. Which feature of Ground Truth should they use?

A data scientist is running a SageMaker training job with a custom PyTorch image. The training script loads a large dataset into memory, and the job fails with an out-of-memory error after a few minutes. The instance type is ml.m5.xlarge (16 GB RAM). What should the data scientist do to resolve this issue without changing the instance type?

A company has deployed a SageMaker real-time endpoint for a model that predicts customer churn. The endpoint uses a single ml.m5.large instance. After deployment, the team notices that during peak hours, the endpoint returns 5xx errors for about 20% of requests. The endpoint has not been configured with any scaling policy. The team needs to resolve this issue with minimal cost increase. Which solution should the team implement?

An MLOps engineer is designing a CI/CD pipeline for deploying machine learning models to a production SageMaker endpoint. The pipeline should include automated testing, approval gates, and rollback capability. Which THREE components should be included in the pipeline? (Select THREE.)

A machine learning engineer is deploying a pre-trained NLP model on Amazon SageMaker for real-time inference. The model expects input sequences of variable length, and performance is critical. The engineer wants to minimize latency while handling the variable-length inputs efficiently. Which approach should the engineer choose?

A machine learning engineer is using SageMaker Autopilot for AutoML. Which TWO outputs does Autopilot produce?

An ML team at a financial services company has developed a fraud detection model using Amazon SageMaker. The model is currently deployed to a production endpoint with a single variant using the previous model version. The team wants to deploy a new model version with a canary deployment where 10% of traffic goes to the new version and 90% remains on the old version for 30 minutes before shifting all traffic to the new version if no issues are detected. Which step is essential to achieve this safe rollout?

A data scientist is working with a dataset containing customer demographics and purchase history. The dataset includes categorical variables with high cardinality (e.g., ZIP code, product ID). The data scientist wants to perform feature engineering to improve model performance. Which THREE feature engineering techniques should the data scientist consider? (Choose three.)

A company uses SageMaker Pipelines to automate their ML workflow. They notice that the pipeline reruns all steps even when the input data has not changed. Which feature should they enable to avoid unnecessary recomputation?

A company uses SageMaker endpoints for real-time inference. They want to automatically scale the number of instances based on the number of outstanding requests. Which auto-scaling policy type should they choose?

A data science team is deploying a PyTorch model for real-time inference with sub-second latency requirements. They need to minimize cost while handling variable traffic. Which TWO approaches should they consider? (Choose TWO.)

A machine learning team wants to monitor bias in a deployed model's predictions on an ongoing basis. Which AWS service should they use to schedule bias monitoring jobs and generate reports?

A team is fine-tuning a Hugging Face BERT model for text classification using SageMaker. They want to use the Hugging Face estimator for convenience. Which parameter must be set to use a custom training script?

A machine learning engineer is training a TensorFlow model using SageMaker with distributed training. They need to implement data parallelism across multiple GPUs. Which SageMaker feature should they use to distribute the training?

A data engineer needs to join two large datasets from Amazon S3: one containing customer demographics and another containing transaction history. The join key is `customer_id`. To minimize data shuffling and improve performance, the engineer decides to use Amazon SageMaker Processing with Spark. Which configuration should the engineer use?

A data scientist is using SageMaker Autopilot to automatically build a model. Which TWO aspects does Autopilot handle? (Choose TWO.)

A data scientist is preparing data for a regression model. The target variable has a skewed distribution. The scientist wants to apply a log transformation to make it closer to normal. Which step should be taken before applying log transformation?

A team is fine-tuning a large language model (LLM) using SageMaker and wants to reduce memory footprint during training. Which technique should they use?

Which TWO options are recommended best practices for monitoring model performance in production on SageMaker? (Choose 2.)

A data scientist is using SageMaker Automatic Model Tuning to find the best hyperparameters for a model. They want to reduce the total tuning time for a given number of training jobs. Which tuning strategy should they choose?

A data science team has trained a model using SageMaker and wants to deploy it to a production endpoint with automatic scaling based on request volume. Which SageMaker feature should they use to configure scaling?

A team uses MLflow on SageMaker for experiment tracking. They want to automate the retraining of a model when new training data arrives in an S3 bucket. Which combination of services should they use?

A company uses SageMaker Inference Recommender to select the optimal endpoint configuration. After running the recommender, they receive a recommendation for a specific instance type and initial instance count. What should they do next to optimize costs over time?

A data scientist creates a feature group as shown in the exhibit. When ingesting data with an 'age' column of integer values, the ingestion fails. What is the most likely cause?

A machine learning engineer is building a time-series forecasting model to predict daily sales for the next 30 days. The dataset spans two years of daily sales data. To evaluate model performance, the engineer needs to simulate a realistic forecasting scenario where the model is trained on past data and tested on future data without leakage. Which data splitting strategy should they use?

A company uses SageMaker Studio for collaborative ML development. The security team requires that all SageMaker Studio notebooks run within a VPC and cannot access the public internet. Which configuration should the administrator set?

A dataset for binary classification has a severe class imbalance (5% positive class). Which two data preparation techniques can help address this imbalance? (Choose two.)

A team is using SageMaker Clarify to detect bias drift in a deployed model's predictions. They run weekly bias monitoring jobs. The team wants to be notified when the bias metric for a sensitive feature exceeds a threshold. What is the most efficient method to achieve this?

An ML team uses SageMaker Pipelines to automate retraining. After a pipeline failure, they need to reprocess only the failed step without rerunning the entire pipeline. What should they do?

A data scientist is evaluating a binary classification model for loan default prediction. Which THREE metrics should they consider to thoroughly assess model performance, especially for imbalanced classes?

A company needs to secure a SageMaker notebook instance that contains sensitive data. Which THREE of the following are effective security measures? (Select THREE.)

A company collects sensor data from IoT devices. The data arrives with missing timestamps due to network issues. For anomaly detection, the engineer needs to create features that capture rolling statistics over fixed windows. Which data preprocessing step is essential before feature generation?

A startup is building a serverless inference API using AWS Lambda. They have a TensorFlow model that is 400 MB in size. They packaged the model and inference code into a Lambda function using a container image. When they test the function with a small input, it consistently times out after 3 seconds. The Lambda function has 512 MB of memory and a timeout of 30 seconds. The business requirement is that inference must complete in less than 5 seconds under normal conditions. What is the most likely cause of the slow performance, and which change should they make?

A data scientist is using SageMaker Automatic Model Tuning with Hyperband. They want to stop poorly performing trials early to save resources. Which strategy does Hyperband use?

Refer to the exhibit. A company configures a SageMaker Model Monitor Data Quality monitoring schedule as shown. The schedule runs every hour. However, the team notices that the monitoring job fails intermittently with an AccessDenied error when accessing the S3 bucket for output. The IAM role SageMakerMonitorRole has permissions to write to s3://my-bucket/monitor-output. What is the MOST likely cause of the failure?

An organization wants to schedule a retraining pipeline to run every Sunday night. Which AWS service should they use to trigger the pipeline on a schedule?

A data engineer is using AWS Glue to prepare a dataset for machine learning. The dataset has several columns with outliers. The engineer wants to detect and handle outliers in a scalable manner. Which TWO approaches should the engineer consider? (Select TWO.)

An e-commerce company uses a machine learning model to predict customer churn. They notice that the model's performance degrades after a major marketing campaign changes customer behavior. Which approach is MOST effective to detect and respond to this type of concept drift?

An e-commerce company uses a multi-model endpoint on Amazon SageMaker to serve several deep learning models. After a new model version is deployed, the endpoint starts returning 503 errors for some models. Monitoring shows that the endpoint's memory utilization is near 100%. What should the team do to resolve this issue while minimizing operational overhead?

A data science team uses SageMaker Studio with a VPC-only mode. They need to access a private S3 bucket in the same VPC to read training data. The SageMaker Studio domain is configured with VPC-only mode. Which configuration ensures the Studio notebook can access the S3 bucket without traversing the public internet?

A data scientist is preparing a dataset for a binary classification model to predict customer churn. The dataset contains a timestamp column 'signup_date' that is not relevant for the prediction. What is the most appropriate action to handle this column?

A machine learning engineer is setting up model quality monitoring for a binary classification model. They have ground truth labels available in Amazon S3. Which TWO steps are required to configure model quality monitoring? (Choose two.)

A team is monitoring a SageMaker endpoint and notices that the average latency (ModelLatency) is increasing over time, but the number of invocations is steady. They suspect that the model's inference code is becoming slower due to memory leaks. Which metric should they also examine to confirm this hypothesis?

A data scientist is training a large language model using SageMaker and wants to reduce training costs. The training job is expected to run for several days. Which TWO actions should the data scientist take to minimize costs? (Choose TWO.)

A data scientist uses SageMaker Experiments to track hyperparameters and metrics. Which component is used to organize related trials?

A company wants to implement cost monitoring and optimization for SageMaker endpoints. Which TWO actions should they take? (Select TWO)

A company is using Amazon SageMaker Data Wrangler to prepare a dataset for training. They have created a data flow with multiple transforms. Which TWO actions can they take to operationalize the data preparation pipeline for production? (Choose 2)

A machine learning engineer needs to deploy a TensorFlow model that requires a custom inference environment with specific system libraries. The model will be used in a real-time application with variable traffic. They want to minimize cold start latency. Which SageMaker hosting option should they choose?

A data scientist is preparing a dataset for binary classification using SageMaker. The dataset has 100 features and 10,000 rows, but the target variable is highly imbalanced (95% negative, 5% positive). Which technique should the data scientist apply during data preparation to address the imbalance?

An ML engineer wants to be notified when the average inference latency of a SageMaker endpoint exceeds 500 ms for 2 consecutive evaluation periods. Which AWS service combination should they use?

A model deployed on SageMaker uses custom inference code. The endpoint is showing intermittent 500 errors. CloudWatch logs reveal 'TimeoutError: Request timed out after 60 seconds'. The model takes on average 55 seconds to process. What is the most effective solution?

A team uses SageMaker Neo to compile a model for deployment on a target device. After compilation, they deploy the compiled model to a SageMaker endpoint using the Neo-optimized container. The endpoint fails to start with error "RuntimeError: Unable to load model". What could be the issue?

A company wants to use SageMaker Autopilot for a regression problem. They require an explainability report that shows feature importance globally. Which Autopilot feature should they enable?

A machine learning engineer needs to reduce costs when training a large model on SageMaker. They are willing to accept potential interruptions and have checkpointing enabled. Which instance purchasing option should they use?

A company wants to deploy a model using a serverless inference endpoint that can automatically scale to zero when not in use and has a configurable maximum concurrency. Which SageMaker inference option meets these requirements?

A company wants to automate the deployment of a SageMaker model into production whenever a new model version is approved in the Model Registry. Which service can be used to trigger the deployment pipeline?

A machine learning engineer is evaluating a binary classification model that predicts customer churn. The model achieves 95% accuracy, but the engineer suspects class imbalance is causing a misleading metric. Which THREE evaluation steps should the engineer perform to properly assess the model? (Choose THREE.)

A company is preparing data for a time-series forecasting model. The data is collected from IoT sensors at irregular intervals. Which TWO steps are necessary to prepare the data? (Choose 2.)

A company uses SageMaker Experiments to track training runs. They want to compare different hyperparameter configurations and identify the best run. Which SageMaker Experiments component should they use to organize related runs?

A company uses SageMaker Model Monitor for feature attribution drift monitoring with SHAP. Which THREE prerequisites must be in place before starting the monitoring schedule? (Select THREE)

A data science team uses Amazon SageMaker Model Monitor to detect data drift in production. They notice that the schema of incoming data (number of features) has changed compared to the training baseline. Which type of monitor is BEST suited to detect this issue?

An ML team is deploying a model using SageMaker. The model requires GPU inference and must be available in multiple AWS regions for low latency. The team has created a multi-model endpoint with GPU instances. After deployment, they notice high latency spikes when a new model is loaded. What is the most likely cause?

A financial services company must ensure that all data used by Amazon SageMaker training jobs is encrypted at rest. The company wants to use a customer-managed key (CMK) for the encryption. Which steps are necessary to achieve this? (Choose TWO.)

A team wants to automate the retraining and deployment of an ML model whenever new labeled data arrives in S3. The workflow includes data preprocessing, training, evaluation, and conditional deployment. Which AWS service is best suited for orchestrating this end-to-end pipeline?

A large enterprise has multiple SageMaker endpoints serving models for different business units. Each endpoint uses a separate instance type and scaling policy. The enterprise wants to implement a unified monitoring and logging solution to track endpoint health, latency, and errors across all endpoints. They also want to set up alerts when the error rate exceeds 5% over a 5-minute period. The solution must be centralized and use AWS-native services. Which solution should the team implement?

A data scientist is training an object detection model using SageMaker built-in Object Detection algorithm. They want to visualize the bounding boxes on validation images after training. Which approach should they use?

A data scientist wants to version control trained models and manage approvals for deployment. Which SageMaker feature should they use?

A company has a batch transform job in Amazon SageMaker that processes large datasets every night. Recently, the job has been failing sporadically with an out-of-memory error. The data size has not increased. What is the MOST likely cause?

A data scientist is using Amazon SageMaker Studio to develop a model. The training job is taking longer than expected. The data scientist suspects that the data is being downloaded from Amazon S3 each time the training starts. What is the BEST way to reduce data loading time?

A company is deploying a SageMaker real-time endpoint and needs to monitor inference latency. Which THREE metrics are available from SageMaker for this purpose? (Choose THREE.)

A data scientist is using Amazon SageMaker Data Wrangler for data preparation. Which two tasks can be performed using Data Wrangler's built-in transforms? (Choose two.)

A machine learning engineer wants to automatically trigger a retraining pipeline whenever new training data arrives in an S3 bucket. The pipeline uses SageMaker Pipelines. Which AWS service should be used to detect the S3 event and start the pipeline?

A data scientist wants to bring a custom PyTorch model to SageMaker. Which THREE methods are valid?

An ML engineer is designing a SageMaker Pipeline for model training and registration. They need to ensure that the pipeline can be re-run with different datasets without manual intervention, and that the steps are only re-executed if inputs have changed. Which THREE features should they configure? (Select THREE.)

Refer to the exhibit. A data scientist runs a SageMaker training job with the above configuration. The training completes but the model performance is poor. Which change to the hyperparameters is most likely to improve the model's AUC?

An ML engineer is fine-tuning a large language model using LoRA on SageMaker. The training is converging slowly, and GPU utilization is low. The engineer suspects the bottleneck is data loading. Which action should the engineer take to improve GPU utilization?

A data engineer is optimizing Amazon Athena queries on large datasets stored in S3 for machine learning data preparation. Which THREE practices improve query performance?

Refer to the exhibit. A user has the above IAM policy attached but cannot access files in SageMaker Studio. What additional permission is most likely needed?

A machine learning engineer needs to deploy a TensorFlow model to Amazon SageMaker and wants to use the built-in TensorFlow Serving container. What should the engineer provide in the model archive?

A financial services company is building a fraud detection model using transactional data stored in Amazon S3. The data includes transaction_id, timestamp, amount, merchant_category, and fraud_label (0/1). The data is collected from multiple sources and has inconsistencies: timestamps are in different timezones (UTC and EST), merchant categories are sometimes misspelled (e.g., 'RESTAURANT', 'Restaurant', 'restaurant'), and the fraud_label is missing for about 5% of records. The data science team uses AWS Glue for ETL. They need to prepare a clean dataset for training. The final dataset must have consistent timestamps in UTC, standardized merchant categories, and no missing fraud labels. The team also wants to minimize data loss. Which set of actions should the team take?

A company wants to deploy its trained model to edge devices such as cameras and IoT devices. The model must run efficiently with low latency and minimal memory footprint. Which THREE actions should the company take to prepare the model for edge deployment? (Choose THREE.)

A machine learning practitioner is building a binary classifier with severe class imbalance (1:1000). They want to use SMOTE for oversampling. What is a potential drawback of applying SMOTE on the entire dataset before splitting into training and test sets?

A team is training a large language model and needs to split the model layers across multiple GPUs due to memory constraints. Which distributed training strategy should they use?

A data scientist is training a linear learner model using SageMaker and notices that the loss is not decreasing. They suspect the issue is exploding gradients. Which SageMaker Debugger rule should they enable to monitor this?

A team is migrating their ML infrastructure to AWS and wants to use infrastructure as code to manage SageMaker Studio domains, user profiles, and associated resources. Which services can they use for this purpose? (Select THREE.)

A team is training a deep learning model on Amazon SageMaker using a custom Docker container. Which three practices should they follow to optimize training performance? (Choose three.)

A company uses SageMaker Model Monitor to detect bias drift in their real-time inference endpoint. They have collected ground truth labels and want to monitor for bias across different demographic groups. Which type of monitoring should they configure?

An ML team is preparing time-series data for a demand forecasting model. They want to evaluate model performance over time without leaking future information into past training windows. Which data splitting strategy is MOST appropriate?

An ML engineer is designing a SageMaker Pipeline for a computer vision model. The pipeline includes steps for data processing, training, evaluation, and registration. The engineer wants to enable caching to avoid reprocessing when step inputs have not changed. For which steps is caching supported? (Select TWO.)

A data scientist is preparing a dataset for training a binary classification model. The dataset has 100,000 rows and 50 features. The target variable is imbalanced, with only 5% positive cases. Which technique should the data scientist apply to address the class imbalance BEFORE training?

During deployment of a Hugging Face model, the endpoint logs show this error. Which step was likely missed?

A company is training a deep learning model on Amazon SageMaker. The training job started but has been stuck in 'InProgress' state for an unusually long time with low CPU utilization. The data scientist suspects a bottleneck. What should be the first troubleshooting step?

A team notices that inference requests to their SageMaker endpoint are failing with '504 Gateway Timeout' for large payloads. What change should be made?

A machine learning engineer needs to prepare a dataset containing customer transactions for training a fraud detection model. The dataset includes features such as transaction amount, timestamp, merchant category, and customer ID. The engineer wants to create a feature representing the average transaction amount per customer over the last 7 days. Which approach should be used in Amazon SageMaker Data Wrangler?

A financial services company is deploying a fraud detection model on SageMaker. To comply with regulations, they must ensure that the model's predictions are not biased against protected groups. They plan to monitor bias drift post-deployment using SageMaker Clarify. Which data inputs are required to configure Clarify's bias drift monitoring?

A company deploys a model on Amazon SageMaker for real-time inference. The inference latency is too high. The model is a large deep learning model. The company wants to reduce latency without significantly impacting accuracy. Which approach should the company consider?

A data scientist is cleaning a text dataset for natural language processing. The raw data contains HTML tags, URLs, and special characters. Which THREE steps should be taken to preprocess the text data? (Choose 3.)

A data scientist is building a text classification model using Amazon SageMaker. The dataset is stored as a CSV file in Amazon S3. The scientist wants to use the SageMaker built-in BlazingText algorithm. Which of the following steps are required to prepare the data for training? (Choose TWO.)

A company wants to reduce costs for a SageMaker real-time endpoint that receives predictable traffic patterns: high during business hours and low at night. The model is a small PyTorch model. Which cost-saving strategy is most suitable?

A machine learning engineer needs to ingest streaming data from thousands of IoT devices into Amazon S3 for batch training. The data should be available in S3 within minutes of arrival. Which combination of services should the engineer use?

Which technique is commonly used to handle missing values in a categorical feature?

An MLOps engineer is building an automated retraining pipeline for a fraud detection model. The model must be retrained weekly, and the new model should only be promoted to production if it meets predefined performance thresholds compared to the current model. Which combination of SageMaker capabilities should the engineer use?

A machine learning team is building a product recommendation system. They have a dataset with millions of users and thousands of products. The team wants to reduce the dimensionality of the user-product interaction matrix while preserving as much variance as possible. Which THREE techniques are appropriate for dimensionality reduction? (Choose THREE.)

A data scientist is training an XGBoost model on a large dataset using a SageMaker Training Job. They want to minimize costs without sacrificing model performance. Which instance type and training strategy should they choose?

A team has deployed a real-time inference endpoint and wants to automatically scale based on CPU utilization. Which scaling policy type should they use with Application Auto Scaling for SageMaker endpoints?

A company uses Amazon SageMaker to train and deploy a machine learning model. After deployment, they notice that the model's accuracy drops significantly over time due to changes in the underlying data distribution. Which monitoring solution should they implement to detect this issue automatically?

A team uses SageMaker for training. They need to monitor training progress and view metrics like loss and accuracy. Which SageMaker feature should they use?

A data scientist is preparing a dataset for a binary classification model. The dataset has 10,000 records with 100 features. The target variable is imbalanced, with 95% negative class and 5% positive class. Which data preparation step should the data scientist take to address the imbalance before training?

A team has 200 small ML models that need to be served via HTTPS endpoints. Each model is used infrequently, and the team wants to minimize hosting costs. Which SageMaker deployment approach is MOST cost-effective?

A machine learning team uses SageMaker Pipelines to automate retraining. They want to avoid re-running data processing steps if the data has not changed since the last successful pipeline run. Which built-in feature should they enable?

A company uses SageMaker Model Monitor to detect data drift. They want to receive alerts when drift is detected and automatically trigger a retraining pipeline. Which TWO steps should they implement? (Select TWO.)

A model deployed on a SageMaker endpoint is returning predictions. The team wants to log all predictions to an S3 bucket for auditing. What is the most efficient way to achieve this?

A data scientist observes that a linear regression model has many irrelevant features. They want to perform feature selection to improve generalization. Which method combines feature selection with model training using a penalty that can shrink coefficients to zero?

An AWS IAM policy is attached to a role used by a CI/CD pipeline to deploy SageMaker endpoints. The policy includes: - An Allow statement for sagemaker:CreateEndpointConfig on all resources. - A Deny statement for sagemaker:CreateEndpoint on all resources with a condition that the VPC subnet must not equal subnet-0123456789abcdef0 (StringNotEquals on ec2:Subnet). The pipeline attempts to create an endpoint configuration with a VPC subnet that is not subnet-0123456789abcdef0. What will happen when the pipeline tries to create the endpoint configuration?

A team is training a large deep learning model on SageMaker using a single ml.p3.16xlarge instance. Training is taking too long. They want to reduce time by distributing across multiple GPUs but are constrained by model size that does not fit in a single GPU memory. Which distributed training strategy should they use?

A machine learning team is processing a large dataset in Amazon SageMaker using a processing job. The data is stored in S3 in CSV format. The team wants to split the data into training, validation, and test sets (70/20/10) while ensuring that the distribution of a categorical feature 'region' is preserved across splits. Which SageMaker SDK method should they use to write the output?

A healthcare company is building a model to predict patient readmission rates. The dataset contains a mix of numeric features (age, blood pressure, lab test results) and categorical features (gender, diagnosis code, hospital department). The dataset has 2 million rows. The data is stored in an Amazon S3 bucket, and they use AWS Glue to catalog and preprocess the data. The data scientist notices that the 'diagnosis_code' column has 10,000 unique codes, and 20% of the rows have missing values for 'blood_pressure'. They plan to use a SageMaker built-in XGBoost model. For optimal model performance, which preprocessing steps should they apply using AWS Glue ETL?

A company deploys a model on SageMaker that serves predictions to a web application. The model's performance degrades over time due to data drift. The company wants to set up continuous monitoring. Which TWO actions should the company take to monitor and retrain the model effectively? (Choose TWO.)

A company is using AWS Step Functions to orchestrate their ML retraining pipeline. They want to trigger retraining when new data arrives, but only if the model's performance has degraded below a threshold. Which THREE AWS services should they use together to achieve this? (Choose three.)

Your company uses SageMaker batch transform to process a large dataset (5 TB) of customer transactions every night. The batch transform job uses a single ml.c5.4xlarge instance and takes about 6 hours to complete. However, the job recently started failing with an error message: 'Timed out waiting for transformation to complete. The maximum job duration is 3600 seconds.' You check the input data and notice that one of the input files is a single large JSON file of 50 GB, while the rest are smaller files. The job is configured with a batch strategy of 'MultiRecord' and a maximum payload size of 6 MB. What is the most likely cause of the timeout and which fix should you apply?

A company is deploying a large number of small models (each < 100 MB) for different customers. They want to minimize costs and management overhead while serving traffic that varies significantly. Which SageMaker endpoint type should they choose?

Which TWO tools are specifically designed for debugging and analyzing training jobs in SageMaker?

A data scientist is using Amazon SageMaker Debugger to monitor training metrics. They want to stop training automatically if the model is overfitting. Which action should they take?

Which SageMaker feature provides AutoML capabilities, including automatic data preprocessing, model selection, and hyperparameter tuning?

A company uses SageMaker Pipelines to automate their ML workflow. They need to add model versioning and approval workflow. Which THREE steps should they include in their pipeline to achieve this? (Choose THREE.)

A data engineer notices that an AWS Glue ETL job is failing with an Out of Memory error when processing a large dataset. The dataset is 500 GB in size, and the worker type is G.1X. Which change is MOST likely to resolve the issue?

Which THREE components are required to set up automated model retraining in response to performance degradation using Amazon SageMaker? (Select THREE.)

A financial services company deploys a fraud detection model with a SageMaker endpoint. They need to ensure that all data sent to the endpoint is encrypted in transit and at rest, and that the endpoint cannot be accessed from the public internet. Which combination of settings should they use?

A data science team has trained a PyTorch model using Amazon SageMaker and wants to deploy it with a custom inference container that includes a pre-processing step. The team needs to minimize latency and ensure the pre-processing runs only once per request. Which SageMaker real-time inference option should they use?

A data scientist is building a regression model to predict house prices. The dataset contains a feature 'neighborhood' with 500 distinct values, and most neighborhoods have fewer than 10 samples. Which approach is MOST appropriate for handling this high-cardinality categorical feature?

A company has 50 small PyTorch models that are used infrequently for inference. They want to minimize costs while maintaining the ability to serve all models from a single endpoint. Which SageMaker feature should they use?

A machine learning engineer needs to select features for a regression model. The dataset contains 50 numeric features, and the target variable is continuous. The engineer wants to reduce dimensionality by selecting features that have the strongest linear relationship with the target. Which feature selection method is MOST appropriate?

Which SageMaker built-in algorithm is designed for time series forecasting?

A data scientist is evaluating a binary classification model. They have the confusion matrix and want to assess the model's performance comprehensively. Which THREE metrics should they consider? (Select THREE.)

A team is using Amazon SageMaker Ground Truth to build a labeled dataset for a multi-class classification task. They have a small budget and want to reduce labeling costs. Which THREE features or strategies should they use? (Select THREE.)

A data science team is deploying a model on Amazon SageMaker and wants to protect the endpoint from unauthorized access. Which TWO methods can the team use to secure the endpoint? (Choose TWO.)

A machine learning engineer is training a model using SageMaker and wants to set up monitoring to detect if gradients become too large, which could destabilize training. Which SageMaker Debugger built-in rule should they enable?

A company is using Amazon SageMaker to train a large deep learning model. The training job is taking a very long time. The data scientist suspects that the GPU utilization is low due to inefficient data loading. Which action should the data scientist take to diagnose and address this issue?

A financial services company needs to deploy a SageMaker endpoint that only accepts inference requests from within a specific VPC and denies all public traffic. The endpoint must also encrypt data in transit between containers. How should the endpoint be configured?

Refer to the exhibit. A team observes that their SageMaker endpoint scales out quickly when load increases, but scales in very slowly when load decreases, causing over-provisioning. What is the most likely cause?

A machine learning team needs to automatically retrain a model when concept drift is detected in the deployed endpoint's predictions. Which TWO steps should they take? (Choose TWO.)

A machine learning engineer is preparing a dataset for a multiclass classification task. The dataset has 10 features and 100,000 rows. Which TWO techniques should the engineer use to reduce the risk of overfitting during data preparation?

An e-commerce company is building a recommendation system using user interaction data stored in Amazon DynamoDB. The data includes user_id, product_id, timestamp, event_type (click, add_to_cart, purchase), and session_id. The data science team exports the data to Amazon S3 as JSON files. During preprocessing, they discover that the 'event_type' field contains inconsistent values due to logging errors: 'Click', 'click', 'CLICK', and 'clck' all appear. Also, there are duplicate records where the same user_id, product_id, and timestamp appear multiple times with the same event_type. The team wants to use AWS Glue to clean the data for training a sequence-based recommendation model. Which set of actions should they perform?

A machine learning engineer is building an ML pipeline using Amazon SageMaker. The engineer needs to prepare the data, detect bias in the dataset, and then create features for training. Which TWO AWS services or features should the engineer use? (Choose TWO.)

A machine learning engineer is developing a text classification model using Amazon SageMaker. The dataset consists of 1 million customer reviews, with labels indicating sentiment (positive, negative, neutral). The engineer uses a pre-trained BERT model from the Hugging Face Model Hub and fine-tunes it on the dataset using SageMaker's Hugging Face estimator with a ml.p3.2xlarge instance. After 2 hours of training, the training job fails with a 'ResourceExhaustedError: CUDA out of memory' error. The error occurs during the forward pass of the first epoch. The engineer confirms that the batch size is set to 32, the maximum sequence length is 512 tokens, and the dataset is stored in a S3 bucket in the same AWS region. The engineer needs to complete fine-tuning without increasing instance costs. Which course of action should the engineer take?

A company wants to reduce costs for a production SageMaker endpoint that has predictable traffic patterns. They have purchased a Savings Plan. What additional step can they take to further optimize costs while maintaining performance?

A company wants to allow a SageMaker model in one AWS account to be accessed by a different AWS account for inference. They need to maintain security and compliance. Which approach meets the requirement?

A team is using AWS Glue to process streaming data from Amazon Kinesis. The streaming data contains both structured and semi-structured fields. The team needs to flatten the semi-structured fields into columns for downstream ML training. Which Glue feature is BEST suited?

A machine learning team needs to deploy a PyTorch model that has been compiled with SageMaker Neo to improve inference performance on edge devices. Which TWO statements about SageMaker Neo are correct? (Select TWO.)

A company uses SageMaker endpoints with auto-scaling based on CPU utilization. During a flash sale, latency increases despite low CPU. What should be done?

A data science team has trained a model using SageMaker and wants to deploy it for real-time inference with automatic scaling based on request latency. The deployment must handle unpredictable traffic spikes without manual intervention. Which combination of SageMaker features should the team use?

A company uses SageMaker for training and inference. They have a model that retrains weekly. After each retraining, the model is evaluated on a held-out test set. If the evaluation metrics meet a threshold, the model is registered as 'Approved' in the SageMaker Model Registry. The team manually deploys the approved model to a production endpoint. They want to automate this deployment process to reduce manual errors. However, the deployment should only proceed if the new model passes a canary test in a staging environment. Which combination of AWS services should the team use to achieve this?

A data scientist runs the exhibit AWS Glue ETL job. The job fails with a Spark stage failure error. What is the most likely cause?

A data scientist needs to evaluate a binary classification model. The dataset is highly imbalanced (5% positive class). Which metric is MOST appropriate for assessing model performance?

A company wants to track the lineage of their ML models, including the training dataset, hyperparameters, and training job used to produce each model version. Which AWS service should they use?

A data scientist wants to use SageMaker Autopilot to automatically build a regression model. The dataset contains 200 features and 50,000 rows. Which output does SageMaker Autopilot provide?

A company has a SageMaker endpoint running a model that provides real-time recommendations. Recently, the model's accuracy has degraded due to data drift. The team wants to automatically retrain the model when a drift metric exceeds a threshold and deploy the new model without downtime. Which architecture should the team implement?

A data scientist is preparing a dataset for binary classification. The dataset has a target variable with 90% of samples belonging to class 0 and 10% to class 1. Which data splitting strategy should the scientist use to ensure that the training and test sets maintain the same class proportion as the original dataset?

A data scientist is exploring data stored in an Amazon Redshift cluster. The data includes timestamp columns with different formats. The scientist wants to create a new column that standardizes the timestamp format to UTC. Which approach is MOST efficient?

A healthcare company deploys a model that predicts patient readmission risk. The model is deployed using a SageMaker real-time endpoint with data capture enabled. The compliance team requires that all inference data be encrypted at rest in S3 using AWS KMS with a customer managed key. The team has configured the endpoint to use an IAM role that includes the necessary KMS permissions. However, after deployment, the captured data is not being written to the S3 bucket. The team checks the CloudWatch logs for the endpoint and finds no errors. The S3 bucket policy is as follows: { "Version": "2012-10-17", "Statement": [ { "Effect": "Deny", "Principal": "*", "Action": "s3:PutObject", "Resource": "arn:aws:s3:::my-bucket/*", "Condition": { "Bool": { "aws:SecureTransport": "false" } } } ] } The bucket also has a default KMS key. What is the MOST likely reason that the captured data is not being written?

A company wants to deploy a trained model to a SageMaker endpoint with automatic scaling based on traffic. Which TWO configurations are required? (Choose two.)

A company deploys a model with SageMaker and wants to monitor for concept drift. They have noticed that the relationship between input features and the target variable has changed, causing model accuracy to degrade. However, the input data distribution remains stable. Which type of drift is this, and what is the most appropriate response strategy?

A company wants to audit all API calls made to SageMaker endpoints for security compliance. Which AWS service should they enable?

A data engineer is using SageMaker Pipelines to automate data preparation. Which TWO statements about data validation within a pipeline are correct?

A data scientist is using SageMaker to train an XGBoost model for a regression problem. After training, they evaluate the model on a test set and get an RMSE of 10 and an R² of 0.85. Which additional metric would give the MOST insight into the model's average prediction error magnitude?

A team is deploying a machine learning model using Amazon SageMaker. They need to serve predictions with sub-100ms latency for a real-time application. The model is a large ensemble that requires 4 GB of memory. The team expects traffic of 100 requests per second initially, but it may double during peak hours. Which instance type and deployment configuration should the team choose to minimize cost while meeting the latency requirement?

A company is training a large Transformer model on SageMaker and wants to use model parallelism to fit the model into memory. The model has 10 billion parameters. Which instance type is MOST cost-effective for this task while supporting SageMaker's model parallelism?

A data scientist needs to normalize numeric features for a deep learning model. The features have different scales and distributions, and the model uses gradient descent. Which scaling method is MOST appropriate?

A company is deploying a real-time inference endpoint for a natural language processing model using Amazon SageMaker. The model requires GPU acceleration and must handle variable traffic patterns, including sudden spikes. The team wants to minimize costs while maintaining low latency during spikes. Which endpoint configuration strategy should they use?

An organization needs to ensure that all data transmitted between containers in a SageMaker training job is encrypted. In the training job configuration, which setting should they enable?

A data scientist wants to fine-tune a large language model for a question-answering task. They want to reduce memory usage during training by using a low-rank approximation of the weight updates. Which technique should they use?