Modeling

Which THREE factors should be considered when choosing between a parametric and a non-parametric machine learning model?

A data scientist needs to perform feature scaling for a dataset containing numerical features with different units (e.g., age in years and income in dollars). Which scaling method is most appropriate when the algorithm assumes data is normally distributed?

Which TWO services can be used to serve machine learning models for real-time inference? (Select TWO.)

A data scientist is working on a regression problem with a dataset that contains outliers. The data scientist is choosing between mean squared error (MSE) and mean absolute error (MAE) as the loss function. Which loss function is more robust to outliers?

A data scientist needs to evaluate a binary classification model's performance. Which metric is most appropriate when the cost of false positives is very high?

A machine learning engineer is training a neural network on Amazon SageMaker using a custom Docker container. The training job fails with an error: 'CUDA out of memory.' The training instance is an ml.p3.2xlarge with 16 GB GPU memory. The model and data fit into memory when using batch size 32, but the engineer wants to maximize GPU utilization. Which approach should the engineer use to fix the out-of-memory error while maintaining efficient training?

A company uses Amazon SageMaker to train a linear regression model. The training data includes a feature 'age' with values ranging from 0 to 100. The model's loss is not converging. What is the MOST likely cause?

A company wants to use Amazon SageMaker to train a deep learning model using a custom TensorFlow script. The data is stored in an S3 bucket. Which SageMaker API operation should be used to launch the training job?

A data scientist is using Amazon SageMaker to train a model. The training job fails with an error 'Insufficient instance capacity'. Which action should the scientist take to resolve this?

A data scientist ran an XGBoost training job on Amazon SageMaker using a CSV dataset. The training job failed with the error shown. What is the most likely cause of this failure?

A company uses Amazon SageMaker to deploy a model for real-time inference. The endpoint uses an ml.m5.large instance with automatic scaling based on CPU utilization. The team notices that during traffic spikes, the endpoint returns 5xx errors. What should the team do to improve the endpoint's availability?

A company uses a linear regression model to predict house prices. The model's R-squared is 0.95 on the training set but 0.60 on the test set. Which of the following is the most likely cause?

A team is training a deep learning model on Amazon SageMaker using a large dataset stored in S3. The training job is taking a long time, and the team suspects that data loading is the bottleneck. The dataset consists of many small files (average size 10KB). Which change would most effectively reduce the I/O bottleneck?

A financial services company is building a model to detect fraudulent credit card transactions. The dataset contains 1 million transactions, with only 0.1% labeled as fraud. The data scientist trains a logistic regression model on the raw dataset and obtains the following results on a held-out test set: accuracy = 99.8%, precision = 50%, recall = 60%, F1 = 0.545. The business requirement is to maximize recall while keeping precision above 80%. Which course of action should the data scientist take to improve the model?

A company uses SageMaker built-in BlazingText algorithm for text classification. The model performance is poor on the validation set. The data consists of short documents (average 50 words). Which hyperparameter tuning strategy is most likely to improve performance?

A data scientist is training a binary classification model on a dataset with 10,000 features. The model overfits severely. Which technique is MOST appropriate to reduce overfitting?

A data scientist is training a binary classification model using Amazon SageMaker's XGBoost. The dataset is highly imbalanced (99% negative class, 1% positive class). The data scientist wants to maximize the F1-score. Which parameter adjustment is most appropriate?

Refer to the exhibit. A data scientist is evaluating a binary classification model for spam detection. The exhibit shows a single prediction instance. What is the model's prediction for this instance?

A machine learning engineer is deploying a sentiment analysis model using Amazon SageMaker. The model is a BERT-based transformer that takes up to 512 tokens. The engineer notices that inference latency is high (over 500 ms per request) on a single ml.c5.xlarge instance. The application requires latency under 100 ms. The model has already been optimized using half-precision (FP16). Which action should the engineer take to reduce latency?

A company is building a text classification model to categorize customer support tickets. The dataset is highly imbalanced with 95% of tickets belonging to 'General Inquiry' and 5% to 'Complaint'. The data scientist is using a random forest classifier. Which metric is most appropriate for evaluating model performance on the minority class?

A data scientist is training a binary classifier using logistic regression on a dataset that is highly imbalanced (95% negative class, 5% positive class). The model achieves 95% accuracy but only predicts the negative class. Which metric should the scientist use to evaluate the model's performance on the positive class?

A company has a time series forecasting problem with daily sales data. The data shows both trend and seasonality. Which Amazon SageMaker built-in algorithm is most suitable?

A data scientist runs a SageMaker training job that fails with the above error. The S3 bucket and object exist, and the IAM role has s3:GetObject permission. What is the MOST likely cause?

A data scientist is building a recommendation system for an e-commerce platform using Amazon SageMaker. The system needs to provide personalized product recommendations based on user purchase history and product metadata. The dataset contains 10 million users and 1 million products. Which algorithm should the data scientist use as the core of the recommendation engine?

A company is building a binary classification model to predict customer churn. The dataset has 10,000 samples with 500 churners (positive class). The data scientist trains a logistic regression model and obtains an accuracy of 95%. However, the model predicts all customers as non-churn. Which metric should the data scientist use to evaluate the model's performance?

Which TWO actions can help reduce overfitting in a decision tree model? (Choose 2.)

A company is training a large language model on Amazon SageMaker using a single GPU instance. The training is taking too long. Which change would most likely reduce training time?

A company is building a model to classify customer reviews as positive or negative. The dataset has 10,000 positive and 100 negative reviews. Which metric is most appropriate for evaluating model performance?

A machine learning team is developing a model to predict housing prices. They have a dataset with numerical features like square footage and number of bedrooms, and categorical features like neighborhood. Which preprocessing step is essential before training a linear regression model?

A financial services company is building a fraud detection model using Amazon SageMaker. The dataset has 10 million transactions, with 0.1% fraudulent. They train an XGBoost model with default hyperparameters. The model achieves 99.9% accuracy on the test set, but only catches 10% of actual fraud cases. The company wants to maximize the number of fraud cases caught while keeping the false positive rate below 5%. The data scientist has already tried adjusting the class weights and threshold, but the recall is still low. What should the data scientist do next?

A data scientist is using Amazon SageMaker to train a model. The training job is using a large dataset stored in S3. Which data input mode provides the FASTEST data loading for training?

A company is building a machine learning model to detect anomalies in industrial sensor data. The data is time-series with seasonal patterns. The data scientist wants to use Amazon SageMaker to train a model. Which algorithm is most suitable for this task?

A financial services company is developing a fraud detection model using a highly imbalanced dataset where fraudulent transactions are only 0.1% of the data. The data scientist has trained a gradient boosting model that achieves 99.9% accuracy but only detects 20% of actual fraud cases. The business requirement is to detect at least 80% of fraud while minimizing false positives. The data scientist has access to SageMaker and can use any built-in algorithm or custom script. Which approach should the data scientist take to meet the business requirement?

A data scientist is assigned an IAM policy as shown. The data scientist attempts to create a SageMaker endpoint to deploy a model, but the request fails. What is the most likely reason?

A company is training a deep learning model on Amazon SageMaker. The training job is taking a long time and the data scientist suspects that the model is overfitting. Which of the following actions can help reduce overfitting and improve generalization?

A machine learning team is building a model to predict customer churn. They have a dataset with 10,000 samples and 50 features, including categorical variables with high cardinality (e.g., ZIP code). Which feature engineering technique is most appropriate to reduce dimensionality while preserving predictive information?

A data scientist is building a binary classifier and wants to evaluate model performance. Which THREE metrics are most commonly used?

A data scientist is training a linear regression model to predict house prices. The dataset includes features such as square footage, number of bedrooms, and location. After training, the model achieves an R² of 0.85 on the training set but only 0.60 on the test set. Which of the following is the MOST likely cause of this discrepancy?

A data scientist is building a model to predict customer churn. The dataset contains categorical features with high cardinality (e.g., ZIP code, customer ID). Which encoding method is MOST suitable?

A machine learning engineer is deploying a model on Amazon SageMaker. Which TWO steps are required to create a SageMaker endpoint?

Refer to the exhibit. A training job failed with the error shown. What is the most likely cause?

A data scientist is using Amazon SageMaker to train a model with the built-in XGBoost algorithm. The dataset contains missing values. What is the default behavior of SageMaker XGBoost regarding missing values?

A machine learning team is using Amazon SageMaker to train a model. The training data is stored in an S3 bucket. The team wants to ensure that the training job can access the data securely without using long-lived AWS credentials. Which approach should the team use?

A data scientist is building a binary classification model to predict loan default. The dataset is highly imbalanced (5% default, 95% non-default). Which TWO techniques should the data scientist use to address the class imbalance?

A data scientist is using SageMaker to train a model that requires access to a private S3 bucket in another account. The scientist has set up the correct IAM roles and bucket policies. However, the training job fails with an access denied error. What is the most likely cause?

A data scientist is training a binary classification model using Amazon SageMaker's built-in XGBoost algorithm. The dataset is highly imbalanced (95% negative class, 5% positive class). The model achieves high accuracy but poor recall on the positive class. Which TWO actions should the data scientist take to improve recall without significantly sacrificing precision?

A data scientist is training a binary classifier on a dataset with 1 million rows and 500 features. The model uses XGBoost and achieves an AUC of 0.95 on the training set but only 0.72 on the test set. The scientist suspects overfitting. Which combination of hyperparameter adjustments is most likely to improve generalization?

A data scientist is training a binary classifier on an imbalanced dataset where the positive class represents 1% of the data. The model is evaluated using accuracy, but the accuracy is 99% even though the model predicts all instances as negative. Which metric should the data scientist use to properly evaluate the model?

A machine learning engineer is deploying a model that predicts customer churn. The model outputs probabilities between 0 and 1. The business requires that at least 90% of customers flagged for churn actually churn (precision >= 0.9). Currently, the model's precision is 0.85 at the default threshold of 0.5. Which threshold adjustment should the engineer consider?

A data scientist is training a binary classification model on an imbalanced dataset where the positive class is rare. The model currently achieves 95% accuracy but only 10% recall on the positive class. Which metric should the data scientist prioritize to improve model performance?

A company is using SageMaker to deploy a real-time inference endpoint for a natural language processing model. The model receives input text and returns predictions. The data scientist notices that the endpoint latency increases significantly under load. Which design change would MOST effectively reduce latency?

A data scientist ran a hyperparameter tuning job for an XGBoost model. The tuning job completed, but the best validation RMSE is 2.34. The data scientist believes the model can perform better. Based on the exhibit, which change to the tuning strategy is most likely to improve the model's performance?

A data scientist is training an LSTM model for time series forecasting using Amazon SageMaker. The model is overfitting. Which action is LEAST likely to reduce overfitting?

A data scientist is training a regression model on a dataset with 50 features. After training a linear regression model, the model achieves an R-squared of 0.85 on the training set but only 0.55 on the test set. Which technique is most likely to reduce the generalization error?

A data scientist is training a multiclass classification model to categorize support tickets into 50 categories. The dataset has 100,000 labeled tickets. The scientist uses a random forest classifier with 100 trees. The model achieves 90% accuracy on the test set, but the F1-score for some rare categories is below 0.1. The scientist wants to improve performance on rare categories without significantly reducing overall accuracy. Which approach should the scientist try?

A team is deploying a model for fraud detection. The dataset is highly imbalanced (99% legitimate, 1% fraudulent). They trained a logistic regression model and achieved 99% accuracy on the test set. However, the model fails to detect most fraud cases. Which metric should the team focus on to evaluate the model?

A company's machine learning model is overfitting to the training data. The data scientist has already tried reducing the model complexity and adding regularization, but the model still overfits. Which technique could the data scientist use to further reduce overfitting?

A machine learning engineer is deploying a model to an Amazon SageMaker endpoint for real-time inference. The model requires a preprocessing step that involves tokenizing text and converting it to a numerical format. To minimize latency, where should the preprocessing logic be implemented?

A data scientist is using SageMaker to train a deep learning model for image classification. The training job is taking too long. Which approach can reduce training time?

A data scientist uses SageMaker Autopilot to automatically build a binary classification model. The dataset has 50 features and 100,000 rows. After the experiment, Autopilot provides multiple candidate models. Which candidate should the data scientist select to minimize inference latency for real-time predictions?

A team is training a deep learning model using TensorFlow on a single GPU instance in SageMaker. The GPU utilization is below 30%. Which change will MOST improve GPU utilization?

A data scientist is using Amazon SageMaker Autopilot to automatically build a binary classification model. The dataset has 50 features and 100,000 rows. After the experiment completes, the best candidate model achieves an F1 score of 0.85 on the validation set. However, when deployed to a real-time endpoint, the model's F1 score drops to 0.72 on production data. The data distributions between training and production are similar. What is the MOST likely cause of the performance drop?

A company is building a binary classifier to predict equipment failure. The dataset has 99% negative (no failure) and 1% positive (failure) examples. The data scientist uses a random forest model with default settings. The model achieves 99% accuracy on the test set but fails to identify any actual failures. Which metric should the data scientist use to evaluate the model?

A company is using Amazon SageMaker to deploy a model for real-time inference. The model takes 200 ms to respond, but the requirement is 100 ms. Which THREE actions could reduce latency? (Choose THREE.)

A company is training a deep learning model on a large dataset using Amazon SageMaker. The training script uses TensorFlow and requires GPUs. The training job is failing with an out-of-memory error. Which configuration change should be made to resolve this issue?

A data scientist needs to choose an algorithm for a regression problem with 50 features and 1 million training examples. The model must be interpretable and the training data fits in memory. Which algorithm is most appropriate?

A data scientist is training a linear regression model and wants to check for multicollinearity among the features. Which TWO methods can be used to detect multicollinearity? (Choose TWO.)

A machine learning engineer is using SageMaker's built-in XGBoost algorithm for a multi-class classification problem. The training job completes but the model accuracy is low. Which THREE hyperparameters should the engineer tune to improve performance?

A company is building a multiclass classification model using Amazon SageMaker. The dataset has 100 classes and is highly imbalanced. The model currently achieves high accuracy on the majority classes but poor performance on minority classes. Which technique should the data scientist use to improve minority class performance?

Which THREE of the following are valid metrics for evaluating a regression model?

A data scientist is using SageMaker to train a deep learning model. The training job runs on a single GPU instance and is taking too long. Which THREE actions can the data scientist take to reduce training time? (Choose three.)

A data scientist is building a regression model to predict housing prices. The dataset includes numerical features such as square footage, number of bedrooms, and year built, as well as categorical features such as neighborhood and roof type. Which TWO preprocessing steps are most important to apply before training a linear regression model?

A data scientist is building a recommendation system for an e-commerce platform. The dataset contains user interactions (clicks, purchases) and item metadata. The scientist wants to use matrix factorization. Which algorithm should be used?

A company is using Amazon SageMaker to train a deep learning model on a large dataset. The training job is taking too long. The team wants to reduce training time without changing the model architecture. Which action should they take?

A data scientist is training a binary classification model on a dataset with a severe class imbalance (95% negative, 5% positive). The model achieves 95% accuracy but only correctly identifies 10% of the positive class. Which metric should the data scientist use to evaluate model performance?

A healthcare company is building a model to predict patient readmission within 30 days. They have structured electronic health records (EHR) data with 200 features. The data includes missing values, categorical variables with high cardinality (e.g., diagnosis codes), and a severe class imbalance (5% readmission). They need to deploy a model on SageMaker that is interpretable and achieves high recall for the positive class. Which combination of techniques should they use?

A machine learning engineer needs to deploy a model that requires low latency (under 10 ms) for real-time inference. The model is a small ensemble of decision trees. Which Amazon SageMaker endpoint configuration is MOST appropriate?

A data scientist is using Amazon SageMaker to train a model, but the training job fails with an 'Out of memory' error. The instance type is ml.p3.2xlarge. Which action should the data scientist take to resolve the issue?

A company is using Amazon SageMaker to deploy a machine learning model that predicts equipment failure. The model is a binary classifier that outputs a probability. The company wants to set a threshold such that the model correctly identifies 95% of actual failures (recall >= 0.95). The model's precision at the current threshold of 0.5 is 0.7. The data scientist evaluates the model on a test set and obtains the following confusion matrix at threshold 0.5: TP=95, FN=5, FP=40, TN=860. The total actual positives are 100. Which threshold adjustment should the data scientist make to achieve the recall goal?

A data scientist is using Amazon SageMaker to train an XGBoost model for a regression problem. The training data contains missing values in some features. Which approach should the data scientist use to handle missing values in XGBoost?

A data scientist is setting up an IAM role for an Amazon SageMaker training job. The policy shown is attached to the role. The training job fails with an access denied error when trying to read the training data from s3://my-bucket/training/data.csv. What is the most likely reason?

Which TWO of the following are best practices for hyperparameter tuning using Amazon SageMaker? (Choose 2)

A data scientist is training a deep learning model for image classification using TensorFlow on Amazon SageMaker. The model trains slowly, and the GPU utilization is below 20%. Which action will MOST effectively increase GPU utilization and reduce training time?

A company uses Amazon SageMaker to train a deep learning model on a GPU instance. The training job is taking too long. Which action would MOST likely reduce training time?

A data scientist is training a model using SageMaker and wants to automatically stop training when the model stops improving. Which TWO options can be used?

A company is building a fraud detection model that must achieve low false positive rates. The dataset is highly imbalanced (0.1% positive class). Which metric is most appropriate for model evaluation?

A company is deploying a model for real-time inference with SageMaker. The endpoint receives spiky traffic, with occasional bursts of 10x normal load. Which scaling policy is MOST cost-effective while maintaining availability?

A data scientist is building a recommendation system using matrix factorization. The dataset has 1 million users and 100,000 items, with a sparse user-item interaction matrix. The scientist wants to minimize training time on Amazon SageMaker. Which algorithm would be most appropriate?

A data scientist is building a time series forecasting model for monthly sales data. The scientist has observed that the sales data shows a clear upward trend and a seasonal pattern that repeats every 12 months. Which algorithm would be most appropriate for this task?

Drag and drop the steps to deploy a model as a SageMaker endpoint for real-time inference in the correct order.

A machine learning engineer is monitoring a deployed model on SageMaker and notices that the prediction latency is increasing over time. The model is a linear regression with a small number of features. Which is the MOST likely cause?

A team is training a deep learning model on SageMaker using a custom Docker container. The training job fails with 'OutOfMemoryError'. The instance type is ml.p3.2xlarge with 61 GB memory. Which change should increase available memory?

A financial services company uses Amazon SageMaker to train a model for credit risk prediction. The dataset contains 500 features and 1 million records. The target variable is binary with 20% default rate. The data scientist uses a gradient boosting algorithm (XGBoost) with default hyperparameters. After training, the model achieves 95% accuracy, but the precision for the default class is only 30%, and recall is 15%. The business requires at least 50% recall and 40% precision for the default class. The data scientist tries to adjust the decision threshold, but this does not simultaneously meet both targets. The scientist suspects that the model is not learning the default patterns well. The company also has a large dataset of unlabeled transactions that could be used. Which action should the data scientist take to improve the model?

A company uses SageMaker to deploy a model for real-time inference. The model is a large ensemble that requires 8 GB of memory and has high latency. The team wants to reduce latency without increasing cost. Which strategy is most effective?

A data scientist is using Amazon SageMaker to train a model. The training data is stored in an S3 bucket encrypted with AWS KMS. During training, the job fails with an access denied error. What is the MOST likely cause?

A machine learning engineer is trying to deploy a model using a SageMaker endpoint but receives an access denied error. The IAM policy attached to the role is shown in the exhibit. What is the MOST likely cause of the error?

A data scientist trains a neural network using TensorFlow on SageMaker. The training job fails with a 'CUDA out of memory' error. What is the most likely cause and solution?

A data scientist is training a deep neural network on Amazon SageMaker. The training is taking a long time and the data scientist wants to speed it up. Which THREE actions can help reduce training time?

A startup is deploying a machine learning model for real-time recommendation on Amazon SageMaker. The model is a TensorFlow model (1 GB) and the endpoint uses a single ml.c5.2xlarge instance. The inference latency is currently 500 ms per request. The startup expects traffic to increase 10x in the next month. They want to maintain latency under 500 ms. What is the most cost-effective solution?

A data scientist is training a binary classification model on an imbalanced dataset where the positive class accounts for 5% of the data. The model achieves 95% accuracy but has a recall of only 10% for the positive class. Which metric should the data scientist primarily use to evaluate model performance?

A data scientist is using SageMaker to train a model. The training data is stored in an S3 bucket in a different AWS account. What is required to allow SageMaker to access the data?

A team is training a binary classifier and obtains a confusion matrix with 100 true positives, 10 false positives, 20 false negatives, and 200 true negatives. What is the precision of the model?

A data scientist is training a deep learning model for object detection. The training loss decreases rapidly in the first few epochs but then plateaus at a high value. The validation loss starts increasing after a few epochs. Which adjustment is MOST likely to improve generalization?

A data scientist is building a regression model to predict house prices. The dataset has 10 features, and the model shows high variance with a low bias. Which technique should the data scientist use to reduce variance?

A company is using Amazon SageMaker to deploy a model that predicts customer churn. The model was trained using a linear learner algorithm. During inference, the endpoint returns predictions that are always 0.5 (the probability of churn). What is the most likely cause?

A data scientist is using Amazon SageMaker to train a linear regression model. The training data contains outliers. Which THREE techniques can mitigate the impact of outliers?

A data scientist is training a linear regression model and notices that the model performs well on training data but poorly on validation data. Which technique should be applied to reduce overfitting?

A data scientist is evaluating a regression model. The RMSE on the training set is 2.5, and on the test set is 2.7. The R² on the test set is 0.98. What does this indicate?

A data scientist is training a linear regression model. After training, the model has a high bias and low variance. Which technique should the data scientist use to reduce bias?

A financial services company is building a fraud detection model using a large dataset of credit card transactions. The dataset contains 10 million rows with 50 features, including transaction amount, merchant category, time of day, and customer historical features. The label is binary: fraudulent (1% of data) or legitimate. The company wants to deploy a real-time inference endpoint using Amazon SageMaker that can score transactions with sub-100ms latency. The current model is a gradient boosting model (XGBoost) trained on a sample of 1 million rows due to memory constraints. The model achieves 0.95 AUC on a held-out test set but the fraud recall (sensitivity) is only 0.4, which is unacceptable because the cost of missing a fraud is high. The data science team has access to a larger compute instance (ml.m5.24xlarge) for training. Which course of action is most likely to improve fraud recall while maintaining latency requirements?

A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents 5% of the data. Which metric is most appropriate for evaluating model performance?

A company is using Amazon SageMaker to train a model on a dataset with many categorical features. They want to use SageMaker's built-in Linear Learner algorithm. What preprocessing step is required for the categorical features?

Which THREE factors should be considered when choosing between SageMaker built-in algorithms and custom algorithms? (Choose THREE.)

A machine learning engineer is evaluating a multi-class classification model that predicts product categories. The model outputs probabilities for 10 classes. The engineer wants to improve the model's calibration so that the predicted probabilities reflect the true likelihood of each class. Which THREE techniques can help?

Which TWO techniques can help reduce overfitting in a decision tree model?

A data science team is deploying a machine learning model to production using SageMaker. The model is a PyTorch model that requires custom inference logic including image preprocessing. The team needs to ensure that the endpoint can handle variable batch sizes and has low latency. Which deployment approach should the team use?

A data scientist is using Amazon SageMaker to train a linear regression model. The training data contains missing values. Which TWO techniques are appropriate for handling missing values in the dataset?

A company is using Amazon SageMaker to train a deep learning model for image segmentation. The training job uses a single ml.p3.2xlarge instance and takes 48 hours to complete. The team needs to reduce training time to under 12 hours to meet a deadline. The dataset is 50 GB of images stored in S3. The team currently uses File mode to download the data to the training instance. The model architecture is a convolutional neural network (CNN) with 50 layers. The team has access to multiple instances of the same type. Which approach will most effectively reduce training time?

A data scientist is using SageMaker to train a custom TensorFlow model. The training script reads data from S3 using TensorFlow's tf.data API. The training is bottlenecked by I/O. Which strategy would MOST effectively improve data throughput?

Which TWO metrics are MOST appropriate for evaluating a regression model that predicts house prices, where the business is most sensitive to large errors?

A data scientist trains a model using SageMaker and notices that the training loss decreases but validation loss increases after a few epochs. What is the MOST likely issue?

A research team is developing a deep learning model to classify medical images into 10 disease categories. They have a dataset of 50,000 labeled images, but the class distribution is highly imbalanced: the most common class has 20,000 images, while the rarest class has only 200 images. To address this, they apply data augmentation (random rotations, flips, and brightness adjustments) to the minority classes until each class has 20,000 images. They then train a convolutional neural network (CNN) from scratch using cross-entropy loss. The model achieves 95% overall accuracy but only 30% recall on the rarest class. Which change is MOST likely to improve recall on the rarest class without significantly reducing overall accuracy?

A data scientist is training a k-means clustering model on a dataset with 1,000 points. The scientist uses the elbow method to choose the number of clusters. The elbow plot shows a clear bend at k=4. After running k-means with k=4, the scientist wants to evaluate the quality of the clustering. Which THREE of the following are suitable internal clustering validation metrics? (Choose THREE.)

A data scientist is training a linear regression model on a dataset with 50 features. After training, they notice that the model performs well on training data but poorly on test data. They suspect overfitting. Which action should they take to reduce overfitting?

A data scientist is training a linear regression model and observes that the training loss is low but validation loss is high. Which step should the data scientist take to address this issue?

Match each hyperparameter tuning strategy to its description.

A company is deploying a machine learning model for real-time fraud detection. The model must respond within 100ms. Which SageMaker endpoint deployment strategy should be used?

A data scientist is working on a binary classification problem to predict loan default. The dataset has 200,000 samples and 50 features. The target variable is imbalanced: 5% default, 95% non-default. The scientist trains a logistic regression model and achieves 95% accuracy, but the recall for the default class is only 20%. The business requires that at least 70% of actual defaults be identified (recall >= 0.7). Which approach should the scientist take to improve recall without significantly sacrificing precision?

Refer to the exhibit. A data scientist checks the status of a SageMaker endpoint and sees the output above. The endpoint is receiving traffic, but the data scientist notices that the number of instances has not increased to the desired count. What is the most likely reason?

Refer to the exhibit. An IAM policy is attached to a SageMaker notebook instance. The data scientist runs a training job that reads from s3://my-bucket/training-data/ and writes to s3://my-bucket/output/. The training job fails with an access denied error. What is the most likely cause?

A financial services company is developing a fraud detection model using gradient boosting. The dataset contains 10 million transactions with 0.1% fraudulent. The model is trained on a SageMaker ml.m5.2xlarge instance and takes 8 hours. The team needs to reduce training time without sacrificing model performance. They have permission to use up to 4 instances. What should they do?

Which TWO actions can help reduce overfitting in a neural network? (Choose 2.)

A data scientist is building a model to predict insurance claim amounts. The target variable is right-skewed with many small claims and a few very large claims. The scientist wants to minimize the impact of outliers. Which loss function or transformation is MOST appropriate?

A data scientist is using principal component analysis (PCA) for dimensionality reduction before training a classifier. The classifier's performance on the test set is poor. What is the most likely cause?

A data scientist is using Amazon SageMaker to train a classification model. The dataset contains categorical features with high cardinality. Which encoding method is most appropriate for handling high-cardinality categorical features in a linear model?

Which TWO metrics are appropriate for evaluating a binary classification model trained on imbalanced data? (Select TWO.)

Which THREE techniques are effective for reducing overfitting in a deep neural network?

A company wants to deploy a machine learning model that requires very low latency predictions (under 10ms). The model is a small ensemble of decision trees. Which SageMaker deployment option is most suitable?

A data scientist is training a binary classifier on an imbalanced dataset (95% negative, 5% positive). The model achieves 99% accuracy but only correctly identifies 2% of the positive samples. Which metric should the data scientist focus on to improve the model's performance?

A data scientist is training a binary classification model on a highly imbalanced dataset (0.1% positive class). To improve recall, the team decides to use SageMaker's built-in XGBoost algorithm. Which parameter adjustment is most likely to increase recall without significantly sacrificing precision?

A data scientist is building a model to predict customer churn. The dataset includes both numerical features (e.g., account age, usage minutes) and categorical features (e.g., region, plan type). The data scientist wants to use a linear classifier. Which feature engineering step is required before training?

A bank is building a credit risk model using a large dataset with 500 features and 2 million samples. The dataset contains many categorical features with high cardinality (e.g., zip code, occupation). The model must be deployed on SageMaker and provide real-time predictions with low latency. They also need to explain individual predictions for regulatory compliance. Which approach is most appropriate?

A data scientist is using Amazon SageMaker to train a model. The training job is taking longer than expected. The scientist wants to reduce training time without changing the algorithm or the hardware. Which action is most likely to help?

A machine learning engineer is using Amazon SageMaker to train a deep learning model. The training job is taking longer than expected. The engineer notices that the GPU utilization is low (around 30%) while CPU utilization is high. Which action is most likely to improve training speed?

A data scientist trains a gradient boosting model on a large dataset using SageMaker. The training completes successfully, but when deploying the model to a real-time endpoint, inference latency is too high. Which change is MOST likely to reduce latency without significant accuracy loss?

A data scientist notices that a linear regression model trained on a dataset has high variance. The model performs well on the training data but poorly on the test data. Which action is most likely to reduce the variance?

A data scientist is training a linear regression model and notices high bias in the training set. What action is most likely to reduce bias?

Match each AWS AI service to its capability.

Refer to the exhibit. An IAM policy is attached to a SageMaker notebook instance role. When the data scientist tries to run a training job that writes model artifacts to 's3://my-bucket/models/', the job fails with an access denied error. What is the MOST likely cause?

A company is using Amazon SageMaker to train an XGBoost model. The training data contains missing values. Which TWO methods can XGBoost handle missing values internally?

A company uses SageMaker to train a model. The training job fails with 'ResourceLimitExceeded' error. Which TWO actions should the company take to resolve this?

A data scientist is using Amazon SageMaker to train a deep learning model with a large dataset. The training job fails with a 'CUDA out of memory' error. What is the MOST efficient way to resolve this issue?

A data scientist is building a regression model to predict house prices. The dataset contains 10 features, including 'number_of_bedrooms' and 'square_footage'. The scientist observes that the model has high variance. Which TWO actions are most appropriate to reduce overfitting? (Choose TWO.)

A data science team is training a binary classification model using Amazon SageMaker. The dataset is highly imbalanced (95% negative class, 5% positive class). The team wants to maximize the F1 score. Which built-in SageMaker algorithm is most appropriate?

A data scientist is building a binary classifier using logistic regression. The dataset has 10 features and 100,000 observations. The model achieves 99% accuracy on the test set, but the precision is 50% and recall is 90%. Which TWO actions should the data scientist take to improve model performance? (Choose 2.)

A data scientist is using Amazon SageMaker to perform hyperparameter tuning for a neural network. The tuning job uses the 'Random' search strategy. After 10 training jobs, the best objective metric has plateaued. The scientist wants to improve the results without increasing the total number of training jobs. Which approach should they take?

A machine learning team trains a deep learning model on SageMaker. The training job uses a single ml.p3.2xlarge instance and takes 12 hours. The team needs to reduce training time without changing the algorithm. Which approach is most effective?

Which THREE of the following are best practices for training deep learning models on Amazon SageMaker?

A team is using SageMaker to train a model with hyperparameter tuning. The training jobs are taking too long. The team wants to reduce time without sacrificing model quality. Which approach should they take?

A machine learning engineer is training a deep learning model using the SageMaker built-in XGBoost algorithm. The training job is taking longer than expected. The engineer notices that the training data is stored in S3 in CSV format and is 500 GB in size. The instance type is ml.c4.8xlarge with 10 instances. Which change would most likely reduce training time?

A data scientist builds a Random Forest model using SageMaker. The model performs well on training data but poorly on test data. Which step is most likely to reduce overfitting?

A team is training a large language model using SageMaker's distributed training. They notice that the training loss is not decreasing after the first few epochs. Which action is MOST likely to resolve this issue?

A team trained a multiclass classification model using SageMaker built-in XGBoost. The model's accuracy is high, but for a specific class, recall is very low. The team wants to improve recall for that class without significant accuracy drop. Which approach is MOST effective?

A data scientist is training a model and wants to monitor training progress. Which AWS service can be used to track metrics like loss and accuracy in real time?

A data scientist is tuning a gradient boosting model using SageMaker automatic model tuning. The hyperparameter 'num_round' ranges from 50 to 500. The tuning job uses 'ObjectiveMetric' = 'validation:auc'. After 50 training jobs, the best objective value is 0.95. The data scientist suspects overfitting. What should the data scientist do?

Which TWO techniques are used for feature scaling? (Choose 2.)

A company is using Amazon SageMaker to deploy a model for real-time inference. The model receives requests with varying payload sizes. The company observes occasional latency spikes. Which feature can help mitigate this?

A research lab is training a large language model (LLM) on SageMaker using PyTorch. The model has 1 billion parameters and does not fit on a single GPU. They have access to a cluster of 16 p4d.24xlarge instances (each with 8 A100 GPUs). They need to train the model with minimal changes to the training script. Which SageMaker feature should they use?

A company uses Amazon SageMaker to host a model for real-time predictions. The model endpoint is experiencing high latency during peak hours. The data scientist wants to reduce latency without increasing cost. Which action should they take?

Which TWO of the following are common techniques to handle missing values in a dataset?

A team is using SageMaker to train a custom PyTorch model on a large dataset (10 TB) stored in S3. The training job is repeatedly failing due to 'OutOfMemory' errors on the GPU. The team is using a single ml.p3.8xlarge instance. Which change is most likely to resolve the issue?

A data scientist is training a binary classification model on a dataset with 100 features and 10,000 rows. The model overfits significantly: training accuracy is 99%, but validation accuracy is 80%. The data scientist has tried L1 and L2 regularization without improvement. The dataset is clean and representative. Which approach is MOST likely to reduce overfitting? A. Increase the number of training epochs. B. Add more training data by generating synthetic samples using SMOTE. C. Reduce the number of features using PCA. D. Use a simpler model like logistic regression instead of a decision tree ensemble. The data scientist needs to maintain a validation accuracy above 85%, but the current model is too complex. The company has limited budget for data labeling. Which option is BEST?

A company is deploying a real-time inference endpoint with SageMaker. The model is a large neural network that requires GPU acceleration. Which TWO configurations must be set?

A data scientist is using Amazon SageMaker to train a model with a large dataset that does not fit into memory on a single instance. The training algorithm supports distributed training. Which approach should the scientist use to train the model efficiently?

A company is deploying a real-time fraud detection system using a gradient boosting model on AWS SageMaker. The model uses 200 features and is trained on 50 GB of data. The inference latency requirement is under 10 ms per request. During load testing, the endpoint shows average latency of 15 ms. Which change is MOST likely to reduce latency below 10 ms?

A data scientist is building a binary classifier and obtains the following confusion matrix on the test set: TP=80, FP=20, TN=70, FN=30. What is the precision?

A data scientist is using Amazon SageMaker to train a model with a custom Docker container. The training job fails with an error: 'Container exited with code 137'. What is the most likely cause?

A company is using SageMaker's built-in image classification algorithm to classify product images into 100 categories. The training takes 3 hours on a single p3.2xlarge instance. They need to reduce training time to under 1 hour. They have access to a cluster of 4 p3.2xlarge instances. Which approach should they take?

A data scientist is training a binary classification model and wants to evaluate its performance using a metric that is robust to class imbalance. Which metric should be used?

A company uses Amazon SageMaker to train a deep learning model for image classification. The training dataset consists of 500,000 images, each 256x256 pixels, stored in S3. The team uses a single ml.p3.2xlarge instance for training. The training time is unacceptably long (over 48 hours). The team wants to reduce training time without sacrificing model accuracy. They have already optimized the data pipeline by using SageMaker Pipe mode and sharding the S3 dataset. The model is a ResNet-50 implemented in TensorFlow. The team is considering the following options: A) Switch to a ml.p3.16xlarge instance which has 8 GPUs and more memory. B) Implement distributed data parallelism using Horovod across multiple instances. C) Use SageMaker's built-in Hyperparameter Tuning to find optimal hyperparameters. D) Reduce the image resolution to 128x128 to speed up training. Which option will MOST effectively reduce training time while maintaining accuracy?

A company is using Amazon SageMaker to deploy a model for real-time inference. The model requires 500 MB of memory and has a latency requirement of 100 ms. The endpoint is receiving 10 requests per second. Which instance type should be chosen for cost-effectiveness?

A data scientist is using Amazon SageMaker to train a linear regression model. The target variable is right-skewed. Which transformation should the data scientist apply to the target variable to improve model performance?

A data scientist is training a time series forecasting model using Amazon SageMaker's DeepAR algorithm. The dataset contains daily sales data for 10,000 products over 2 years. The scientist splits the data chronologically: training on the first 18 months, validation on the next 3 months, and test on the last 3 months. The model performs well on validation but poorly on test. The data scientist suspects the model is overfitting to the validation period. Which action should the scientist take to improve test performance?

A data scientist wants to automate the selection of optimal hyperparameters for a model. Which SageMaker feature should be used?

A company wants to build a machine learning model to predict customer churn. The dataset includes customer demographics, usage patterns, and support interactions. The data is stored in Amazon S3. The data scientist needs to perform feature engineering, including creating aggregate features from support interactions and encoding categorical variables. Which AWS service is most suitable for building the feature engineering pipeline?

A data scientist is performing feature selection for a linear regression model. Which TWO methods are appropriate? (Choose TWO.)

A data scientist is training a binary classification model on an imbalanced dataset (95% negative class, 5% positive class). The model achieves 95% accuracy but only predicts the negative class for all examples. Which metric should the scientist use to evaluate model performance more appropriately?

A data scientist is working with a dataset containing categorical features with high cardinality. The scientist wants to use a tree-based model. Which encoding method should be used?

A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents 1% of the data. The model needs to maximize recall while keeping precision above 0.7. Which sampling strategy should the data scientist use?

A company is using Amazon SageMaker to build a binary classification model for customer churn. The dataset is highly imbalanced (90% no churn, 10% churn). Which technique is MOST effective for handling class imbalance?

Which TWO actions are best practices for tuning hyperparameters using Amazon SageMaker Automatic Model Tuning?

A data scientist is training a deep learning model for image classification. The model is overfitting on the training data. Which combination of techniques will most effectively reduce overfitting?

A data scientist is training a neural network for image classification. The dataset has 50,000 images across 100 classes. The model uses a ResNet-50 architecture pre-trained on ImageNet. The training loss decreases rapidly, but validation loss starts to increase after 5 epochs. Which of the following is the most effective technique to address this?

A data scientist is working on a multi-class classification problem with 10 classes. The model outputs probabilities and the scientist wants to evaluate the model's ability to rank classes correctly. Which metric is most appropriate?

A data scientist is building a regression model to predict house prices. The dataset includes features such as square footage, number of bedrooms, and location. After training a linear regression model, the scientist notices that the residuals have a pattern: they increase as the predicted value increases. Which action is most appropriate?

A company uses Amazon SageMaker to train a model for fraud detection. The dataset has 1 million transactions, with 0.1% fraud. The data scientist trains a random forest model and achieves 99.9% accuracy but 0% recall on the fraud class. Which technique is most likely to improve recall without significantly reducing precision?

A team is using SageMaker to train a deep learning model for image classification. The training job is failing with a 'CUDA out of memory' error. The team is using a p3.2xlarge instance (1 GPU, 16 GB GPU memory). The dataset consists of 256x256 RGB images. Which action is MOST likely to resolve the error without changing the instance type?

A company uses Amazon SageMaker to train an XGBoost model on a large dataset. Training takes a long time. Which action can reduce training time without significantly affecting model accuracy?

A data scientist is using Amazon SageMaker to train a model on a dataset that contains both numerical and categorical features. The categorical features have high cardinality (e.g., postal codes, product IDs). Which feature engineering approach is most suitable for handling these high-cardinality categorical features in a tree-based model?

A team is training a linear regression model to predict house prices. After training, they observe that the model has high bias (underfitting). Which action is most likely to reduce bias?

A data scientist is using Amazon SageMaker to train a natural language processing model using a custom Docker container. The training script reads data from an S3 bucket and writes checkpoints to an S3 bucket. The training job is failing with the error 'Unable to write to checkpoint path: s3://my-bucket/checkpoints/'. The IAM role associated with the training job has the following policy: {'Effect': 'Allow', 'Action': 's3:PutObject', 'Resource': 'arn:aws:s3:::my-bucket/checkpoints/*'}. The bucket 'my-bucket' exists and the prefix 'checkpoints/' is empty. What is the most likely cause of the failure?

A company uses SageMaker to host a real-time inference endpoint for a classification model. The endpoint receives traffic spikes that cause high latency. The team wants a solution that automatically scales based on demand while keeping costs low. Which approach is BEST?

A machine learning engineer is tuning a Gradient Boosting model for a regression task. The dataset contains 50 features and 100,000 samples. The engineer wants to speed up training without sacrificing predictive performance significantly. Which THREE hyperparameters should the engineer consider adjusting? (Choose THREE.)

A research team is training a deep learning model for object detection using SageMaker's built-in SSD algorithm. The dataset contains 50,000 images with bounding box annotations. The team uses a single ml.p3.2xlarge instance. After 24 hours of training, the model's loss has plateaued, but the mean average precision (mAP) on validation is only 0.45. The team wants to improve mAP without increasing training time. Which action should they take?

A machine learning team is using SageMaker to train a deep learning model. The training job is failing due to insufficient GPU memory. Which approach should the team take to resolve this issue without changing the model architecture?

A company wants to deploy a machine learning model that predicts customer churn. The model must provide interpretable predictions to explain why a customer is likely to churn. Which algorithm is most appropriate?

A data scientist is training a random forest model for regression. The model shows high variance on the validation set. Which TWO actions are most likely to reduce variance? (Choose 2.)

A machine learning team is training a deep learning model on Amazon SageMaker and notices that the training loss is decreasing but the validation loss is increasing. What is the most likely cause?

A data scientist is troubleshooting a failed SageMaker training job that uses a custom Docker image. The failure reason shows 'unrecognized arguments: --sagemaker_program'. What is the most likely cause?

A company is deploying a machine learning model on SageMaker for real-time inference. The model requires GPU for low latency. Which THREE steps are necessary to set up the endpoint?

Refer to the exhibit. A data scientist is trying to run a SageMaker training job using a script that reads training data from 's3://my-bucket/training/data.csv'. The job fails with an access denied error. What is the MOST likely reason?

Which THREE of the following are appropriate methods to reduce overfitting in a decision tree model?

Refer to the exhibit. What is the recall of the model?

A data scientist is building a recommendation system for an e-commerce platform. The dataset includes user-item interactions (clicks, purchases, ratings). The scientist wants to use matrix factorization. Which approach is most appropriate for handling implicit feedback (e.g., clicks) rather than explicit ratings?

A data scientist is using Amazon SageMaker Autopilot to automatically build a model for a regression problem. The dataset has 100 features and 50,000 rows. Autopilot recommends a model with an R² of 0.85 on the validation set. However, when deployed to production, the model performs poorly (R² of 0.2). What is the most likely cause?

A company uses SageMaker to deploy a model for predicting customer churn. The model was trained on historical data and achieves 85% accuracy on the test set. After deployment, the model's predictions are significantly worse on new data due to changes in customer behavior. What is the MOST likely cause?

A machine learning team is building a recommendation system for an e-commerce platform. They have user-item interaction data (clicks, purchases). They need to choose an algorithm that can capture both user and item latent factors and handle missing data. Which algorithm should they use?

A data scientist is training a binary classification model on imbalanced data (95% negative, 5% positive). The model achieves 95% accuracy but only 10% recall on the positive class. Which metric should be used to evaluate model performance?

A company is using Amazon SageMaker to deploy a model for real-time inference. The model receives requests that are small but arrive in bursts. The data scientist wants to minimize latency and cost. Which deployment option is MOST appropriate?

A team is training a deep learning model on Amazon SageMaker. The training job is slow because the data is stored in S3 as many small files. Which approach is MOST effective to improve training throughput?

A data scientist is developing a deep learning model for object detection using Amazon SageMaker. The training dataset has 50,000 labeled images. The data scientist wants to improve model generalization without collecting more data. Which TWO techniques can be applied? (Choose two.)

A data scientist is building a text classification model using a bag-of-words approach with logistic regression. The dataset has 10,000 documents and 50,000 unique tokens. The model overfits. Which TWO techniques can help reduce overfitting?

A machine learning team is deploying a time-series forecasting model using Amazon SageMaker. The model is trained on historical data and needs to be updated daily with new data. The team wants to automate the retraining pipeline and avoid manual intervention. Which approach is the most efficient?

Which TWO of the following are best practices for training deep learning models on Amazon SageMaker? (Select TWO.)

A company is building a recommendation system using Amazon SageMaker's Factorization Machines algorithm. The dataset includes user IDs, item IDs, and ratings. The data is sparse. Which data format should be used for training?

A company uses Amazon SageMaker to train a model for fraud detection. The dataset has 1 million samples with 200 features. The data is highly imbalanced (0.1% fraud). The team wants to use a random forest model. Which technique should they use to handle the class imbalance during training?

A data scientist is using Amazon SageMaker to train a deep learning model for natural language processing. The training job is taking too long to converge. The data scientist wants to speed up training without significantly sacrificing model accuracy. Which THREE strategies should the data scientist consider? (Choose three.)

A company is fine-tuning a BERT model on Amazon SageMaker for a text classification task. The training script uses PyTorch and Hugging Face Transformers. The training job completes successfully, but the final model accuracy is low. The dataset has 10,000 labeled samples. What is the most likely cause and solution?

A data scientist is using Amazon SageMaker to deploy a model for real-time inference. The model is a TensorFlow neural network. The scientist wants to use automatic scaling based on the number of incoming requests. Which service integration is required?

An IAM policy attached to a SageMaker execution role is shown. A training job executed with this role fails with an error that the role cannot access the S3 bucket. The training job uses input data from s3://my-bucket/train/data.csv and output to s3://my-bucket/output/. What is the most likely cause?

Which TWO of the following are valid Amazon SageMaker built-in algorithms for regression tasks? (Select TWO.)

A company is using a custom Docker container in SageMaker for training. The training job fails with 'ResourceLimitExceeded' error. Which action should the data scientist take?

A data scientist is using Amazon SageMaker to deploy a custom model container. The model is a large transformer that requires 16 GB of memory. The scientist wants to minimize inference latency. Which SageMaker hosting option should they choose?

A machine learning engineer is evaluating a binary classification model. The model has a high recall but low precision. Which of the following is the most likely consequence?

A data scientist is building a classification model and wants to evaluate its performance. Which TWO metrics are appropriate for a multi-class classification problem? (Choose 2)

A machine learning engineer is training a regression model to predict house prices using Amazon SageMaker. The dataset contains 10,000 samples and 50 numerical features. After training a linear regression model, the engineer notices that the training loss is low, but the validation loss is high. The engineer suspects overfitting. The dataset is already normalized. Which action should the engineer take to reduce overfitting?

A company is building a sentiment analysis model for customer reviews. The dataset includes 10,000 positive and 10,000 negative reviews. The data scientist splits the data into 70% training, 15% validation, and 15% test sets. After training, the model achieves 99% accuracy on training set but only 82% on validation set. What is the most likely issue?

During training, a binary classification model has an AUC of 0.99 on the training set but only 0.72 on the validation set. Which of the following is the most likely cause?

A data scientist is building a regression model to predict house prices. The dataset contains features like 'number_of_rooms' (integer), 'sqft' (float), 'location' (categorical with 1000 unique values). Which feature engineering approach is BEST for the 'location' feature?

A data scientist is training a random forest model on a dataset with 50 features. After training, the model achieves 98% accuracy on the training set but only 85% on the test set. Which technique is most appropriate to reduce the generalization error?

Refer to the exhibit. The log shows the end of a successful SageMaker training job. However, the ML engineer cannot find the model artifacts in the specified S3 bucket. What is the most likely cause?

A company is deploying a machine learning model for real-time fraud detection. The model must have low latency (under 100 ms) and high throughput. The model is an ensemble of 5 gradient boosted trees (XGBoost), each 200 MB. Which deployment strategy is MOST suitable?

A data scientist is reviewing the training logs from a SageMaker training job. The logs show training and validation loss per epoch. Based on the exhibited logs, which statement is correct?

A machine learning team is using Amazon SageMaker to build a regression model. The target variable is heavily right-skewed with a long tail. Which data transformation should the team apply to the target variable before training?

A healthcare company is building a model to predict patient readmission within 30 days of discharge. The dataset includes 10,000 patient records with 200 features, including lab results, demographics, and historical admissions. The target variable is highly imbalanced: only 8% of patients are readmitted. The data scientist splits the data into 80% training and 20% test sets, ensuring the same proportion of readmissions in each. The scientist trains a logistic regression model and a random forest model. The logistic regression achieves 92% accuracy but recall of 10% for the readmitted class. The random forest achieves 90% accuracy but recall of 25%. The business requirement is to achieve at least 60% recall for readmissions while maintaining reasonable precision. The scientist also has access to a large collection of unlabeled patient records from other hospitals. Which strategy should the data scientist use to meet the business requirement?

A data scientist is training a deep learning model on a large dataset using Amazon SageMaker. The training job is taking too long. Which action would MOST likely reduce training time without sacrificing model accuracy?

Refer to the exhibit. A data scientist runs the AWS CLI command to create a SageMaker training job. The job fails immediately with 'ValidationException: Invalid instance type'. What is the most likely issue?

A data scientist is training a binary classifier using logistic regression. The dataset has 100 features and 1 million samples. After training, the model achieves AUC of 0.85 on the test set. The business wants to understand which features contribute most to predictions. Which technique should the data scientist use?

A company is building a recommendation system for an e-commerce platform. They have user-item interaction data (clicks, purchases) and want to use matrix factorization. They plan to use Amazon SageMaker to train the model. Which dataset format is MOST appropriate for the built-in Factorization Machines algorithm?

A team is training a large deep learning model on Amazon SageMaker. The training job is taking too long and they want to reduce training time without changing the model architecture. Which action is MOST effective?

A data scientist is training a neural network for image classification. The training loss decreases but validation loss increases after a few epochs. Which TWO actions should be taken to address this?

A machine learning team is using Amazon SageMaker to train a large language model. The training script uses PyTorch and the model requires significant memory. The team wants to use model parallelism across multiple GPUs. Which SageMaker feature should they use?

A data scientist is training a neural network for image classification. The training loss is decreasing steadily, but the validation loss starts increasing after a few epochs. What is the MOST likely cause?

Refer to the exhibit. An IAM policy is attached to a SageMaker execution role. A data scientist tries to create a training job that reads training data from s3://my-bucket/confidential/data.csv. What will happen?

A data scientist is using Amazon SageMaker to train a linear regression model. The dataset has 500 features and 50,000 observations. The model converges but has high bias. Which technique should the data scientist use to reduce bias?

A data scientist is training a binary classifier on an imbalanced dataset where the positive class represents only 2% of the data. The model achieves 99% accuracy but only identifies 5% of actual positives. Which metric should the scientist use to evaluate the model's ability to detect the positive class?

Refer to the exhibit. A data scientist wants to update the endpoint to use a new model image. The scientist updates the endpoint configuration with the new image and calls UpdateEndpoint. After the update, the endpoint status is 'Updating' but remains in that state for a long time. What is the most likely cause?

A machine learning engineer is evaluating a classification model that predicts whether a transaction is fraudulent. The model outputs a probability score. The cost of a false negative (missed fraud) is 10 times higher than the cost of a false positive (false alarm). Which TWO evaluation metrics should the engineer use to tune the model? (Choose TWO.)

A company is using Amazon SageMaker to train a large language model. The training job is taking too long. The data scientist wants to reduce training time without sacrificing model accuracy. Which THREE strategies are MOST appropriate?

A data scientist is using Amazon SageMaker to train a gradient boosting model on a dataset with categorical features. The dataset contains a column 'UserID' with over 1 million unique values. The training is taking very long and the model size is large. Which technique would MOST effectively reduce training time and model size while maintaining accuracy?

A company is using Amazon SageMaker to deploy a model for real-time inference. The model is updated frequently. Which deployment strategy allows for zero-downtime updates and easy rollback?

A data scientist is training a recurrent neural network (RNN) for time series forecasting. The training loss decreases steadily for the first 10 epochs, then plateaus. The validation loss starts increasing after epoch 10. What is the most appropriate action?

A data scientist is training a convolutional neural network (CNN) for image classification using Amazon SageMaker. The training loss decreases steadily but validation loss starts increasing after a few epochs. Which action should the data scientist take to address this issue?

A data scientist is using Amazon SageMaker to train a linear regression model. The dataset has outliers. Which TWO techniques can help reduce the impact of outliers? (Choose TWO.)

A team is using Amazon SageMaker to train a model. The training job repeatedly fails with a 'ResourceLimitExceeded' error. Which action should the team take to resolve this issue?

A research team is training a deep learning model for image classification using Amazon SageMaker. The model is a convolutional neural network (CNN) with 50 layers. The team uses a single ml.p3.2xlarge instance. After 10 hours of training, the model has not converged and the loss is decreasing very slowly. The team suspects vanishing gradients. They want to diagnose and fix the issue without significant code changes. Which action should they take?

A data scientist is training a text classification model using a bag-of-words approach. The dataset contains 1 million documents and 100,000 unique words. The resulting feature matrix is very sparse. Which technique should the data scientist use to reduce the dimensionality of the feature space?

A data scientist is tuning a gradient boosting model using Amazon SageMaker Automatic Model Tuning (AMT). Which THREE hyperparameters should the scientist consider tuning to reduce overfitting? (Select THREE.)

A company is building a sentiment analysis model for customer reviews. The dataset is balanced with 10,000 positive and 10,000 negative reviews. The model achieves 95% accuracy on the test set but fails to generalize to new reviews from a different product category. Which TWO techniques can improve generalization?

A data scientist is tuning a random forest model using SageMaker Hyperparameter Tuning. The objective metric is validation:accuracy. Which THREE hyperparameters are most commonly tuned for random forest? (Choose THREE.)

A data scientist is training a linear regression model on a dataset with 10 features. After training, the model has high variance on the test set. Which technique should the data scientist use to reduce variance without significantly increasing bias?

Which THREE of the following are best practices for training a deep learning model on Amazon SageMaker?

A data scientist is trying to run a SageMaker training job that writes output to an S3 bucket 'my-bucket'. The IAM policy is shown. The training job fails with an AccessDenied error when trying to write to S3. What is the reason?

A data scientist is using SageMaker to train a TensorFlow model. The training script uses tf.data.Dataset to load data from S3. Training is slow because of I/O bottleneck. Which change should the data scientist make to improve I/O performance?

A data scientist is performing hyperparameter optimization for a gradient boosting model using Amazon SageMaker Automatic Model Tuning. The objective metric is 'validation:logloss'. Which TWO strategies can help the tuning job converge faster? (Choose TWO.)

A data scientist is training a binary classification model on an imbalanced dataset (95% negative class, 5% positive class). The model currently achieves 94% accuracy but a recall of only 0.10 on the positive class. Which TWO strategies should the data scientist consider to improve recall without significantly sacrificing precision? (Choose 2.)

A company wants to build a real-time anomaly detection system for IoT sensor data. The data arrives as a stream of numerical values. The model should adapt to concept drift over time. Which approach is most suitable?

A company is training a deep learning model on Amazon SageMaker using a large dataset stored in S3. The training job is failing with an error indicating insufficient memory. The model architecture and hyperparameters are fixed. Which change is MOST likely to resolve the issue without modifying the model code?

A data scientist trains a linear regression model to predict house prices. The model has high bias (underfitting). Which action is most likely to reduce bias?

A data scientist is using Amazon SageMaker built-in XGBoost algorithm to train a regression model. The training job completes successfully but the model performance on the test set is poor, with high bias. Which hyperparameter adjustment is most likely to help reduce bias?

A data scientist is training a binary classification model on a highly imbalanced dataset where the positive class represents only 1% of the data. Which metric should be used to evaluate model performance during training to ensure the model is learning to detect the positive class?

A data scientist is training a binary classification model on a dataset with 100,000 positive samples and 1,000 negative samples. The model achieves 99% accuracy on the test set but a very low F1 score. What is the most likely cause?

A data scientist is training a deep learning model on Amazon SageMaker and notices that training is taking much longer than expected. The training job uses a single GPU instance. The model is a large transformer with millions of parameters. Which change would most likely reduce training time?

A machine learning team is building a multi-class image classifier using a pre-trained ResNet-50 model in Amazon SageMaker. The dataset has 10 classes but is highly imbalanced, with one class representing 80% of the samples. The team wants to improve model performance on the minority classes. Which TWO of the following approaches are most likely to help? (Select TWO.)

A startup is building a recommendation system for an e-commerce platform using collaborative filtering. They have a dataset of user-item interactions (ratings) with 1 million users and 100,000 items. The data is sparse (99% missing ratings). They need to train a model on Amazon SageMaker that can handle large-scale sparse data efficiently. Which approach should they use?

A data scientist is training a binary classifier to predict customer churn. The dataset has 10,000 samples, with 500 churners (positive class). The scientist trains a logistic regression model and obtains an F1-score of 0.6. To improve the F1-score, which approach is MOST likely to be effective?

A company wants to use Amazon SageMaker to automatically tune hyperparameters for a XGBoost model. Which built-in SageMaker feature should be used?

A data scientist is working on a regression problem to predict house prices. The dataset has 80 features, including categorical variables with high cardinality (e.g., zip code with 10,000 unique values). The target variable is log-transformed. The data scientist trains a linear regression model and obtains an R² of 0.45 on the test set. To improve performance, the data scientist considers: A) Applying one-hot encoding to all categorical features and using Ridge regression. B) Using target encoding for high-cardinality features and using a tree-based model like XGBoost. C) Removing all categorical features and using polynomial features for numerical features. D) Using principal component analysis (PCA) on all features before training a linear model. Which approach is MOST likely to improve the model's performance?

A data scientist is evaluating a binary classification model. The model's AUC-ROC is 0.95. Which TWO statements are true?

A data scientist is training a linear regression model on a dataset with 10 numerical features. After training, the model's R-squared value is 0.99 on the training set but only 0.60 on the test set. Which TWO of the following are appropriate actions to reduce overfitting? (Choose TWO.)

Which TWO actions are valid ways to handle missing data in a dataset before training a machine learning model? (Select TWO.)

A company uses Amazon SageMaker to train a time-series forecasting model using the built-in DeepAR algorithm. The training data consists of daily sales for 1000 products over 2 years. The model performs well on most products, but for a few products with intermittent demand (sporadic sales), the predictions are poor. Which action should the data scientist take to improve predictions for these products?

Which TWO approaches are valid for handling missing categorical values in a dataset before training a machine learning model?

A company uses Amazon SageMaker to train a model. The training job runs successfully but the model artifacts are not saved to the specified S3 output path. What is a likely cause?

A company uses SageMaker to train a time-series forecasting model using Amazon Forecast. The dataset contains historical sales data for 10,000 products over 2 years. Which data format is required for the target time series?

A data scientist is evaluating a binary classification model that predicts whether a customer will churn. The model achieves an AUC of 0.85 on the test set. Which TWO statements about AUC are correct? (Choose two.)

A data scientist is training a linear regression model using Amazon SageMaker's built-in Linear Learner algorithm. The dataset has 500 features and 1 million rows. After training, the model's training RMSE is 2.5 and validation RMSE is 2.6, which is acceptable. However, the scientist notices that many feature coefficients are very small but non-zero, and the model takes a long time to train. The scientist wants to reduce training time while maintaining similar accuracy. Which action should the scientist take?

A data scientist is using Amazon SageMaker to train a deep learning model for image classification. The training job is using a single GPU instance and is taking too long. The scientist wants to reduce training time without sacrificing model accuracy. The dataset contains 100,000 images of size 256x256. Which change would most effectively reduce training time?

A data scientist is training a binary classification model on a highly imbalanced dataset where the positive class represents only 1% of the data. The model achieves 99% accuracy but only identifies 5% of the actual positives. Which metric should the data scientist use to evaluate model performance?

A company uses Amazon SageMaker to build a text classification model using a pre-trained BERT model. The dataset contains 10,000 labeled documents. The model is overfitting: training accuracy is 99%, validation accuracy is 85%. Which TWO of the following are most likely to help reduce overfitting? (Choose TWO.)

A data scientist is training a binary classification model on imbalanced data (95% negative, 5% positive). Which metric is most appropriate for evaluating model performance?

Which THREE of the following are valid approaches for deploying a machine learning model to an Amazon SageMaker endpoint for real-time inference?

A data scientist is training a binary classifier to detect network intrusions. The dataset has 1,000 features and 10 million samples, but only 0.1% are positive (intrusions). The scientist uses XGBoost with scale_pos_weight set to 100. The model achieves a recall of 0.90 and precision of 0.05 on the test set. The business requires precision of at least 0.50 while maintaining recall above 0.80. Which technique should the scientist apply?

A company is deploying a real-time inference endpoint using SageMaker. The model is a large deep learning model (5 GB) with strict latency requirements (< 100 ms per request). The team expects bursty traffic with up to 1000 requests per second. Which configuration best meets the latency and throughput requirements?

A data scientist is training a model using SageMaker's built-in XGBoost algorithm. The dataset has 500 features and 1 million rows. The training job is taking too long. The scientist wants to reduce training time without sacrificing accuracy. Which action is LIKELY to be most effective?

A data scientist is training a decision tree classifier and notices that the model performs well on training data but poorly on test data. Which technique should the data scientist use to address this issue?

A data scientist is training a deep learning model for image segmentation using a U-Net architecture. The model overfits severely. The scientist tries L2 regularization, dropout, and data augmentation, but validation loss remains high while training loss approaches zero. Which additional strategy is most likely to reduce overfitting?

Refer to the exhibit. A data scientist is trying to create a SageMaker training job but receives an access denied error. The IAM policy shown is attached to the user. What is the likely issue?

A data scientist is training a binary classification model using SageMaker XGBoost and notices that training loss decreases but validation loss increases after a few epochs. Which action should the data scientist take to address this issue?

A data scientist is building a regression model to predict house prices. The dataset contains many features, some of which are highly correlated. The model is overfitting. Which regularization technique should the scientist use to penalize large coefficients and perform feature selection?

A data scientist is training a neural network on Amazon SageMaker. The network has many layers and the training is very slow. The scientist suspects that the gradients are vanishing. Which technique is most specifically designed to mitigate the vanishing gradient problem?

Refer to the exhibit. A data scientist wants to use SageMaker to train a model using data stored in 'my-bucket'. The training job fails with an access denied error. What is the MOST likely cause?

A data scientist is using Amazon SageMaker to train a neural network. The training job fails with the error 'ResourceLimitExceeded: The account-level service limit for ml.p3.8xlarge for training job usage is 0.' What is the most likely cause and solution?

A data scientist is tuning a neural network on a small dataset and observes that the training loss decreases but validation loss increases after a few epochs. Which technique should be applied to mitigate this issue?

A data scientist is training a binary classification model on imbalanced data (95% negative, 5% positive). The model achieves 99% accuracy on the test set but fails to detect any positive cases. Which metric should the scientist focus on to evaluate model performance?

A data scientist is training a deep learning model for image classification using Amazon SageMaker. The training job is taking too long. The data scientist wants to use distributed training across multiple GPUs to speed up the process. Which SageMaker feature should the data scientist use?

A data scientist is training a binary classification model for fraud detection. The dataset is highly imbalanced with only 1% fraudulent transactions. The model currently achieves 99% accuracy but only catches 5% of actual fraud cases. Which metric should the data scientist focus on to better evaluate model performance?

A company is deploying a real-time fraud detection model using Amazon SageMaker. The model must make predictions in under 100 milliseconds. The data scientist uses a pre-trained XGBoost model and deploys it to a SageMaker endpoint with an ml.c5.xlarge instance. After load testing, the average latency is 150 ms. Which action should the data scientist take to reduce latency?

Which TWO of the following are valid methods for handling missing values in a dataset before training a machine learning model?

A company is training a deep learning model for object detection using Amazon SageMaker. The training job is taking too long. Which TWO actions can reduce training time?

Which TWO techniques are used to handle missing values in a dataset before training? (Choose 2.)

A company uses Amazon SageMaker to train a linear regression model on a dataset with 10 million rows and 50 features. The training job takes 8 hours to complete. A data scientist wants to reduce the training time to under 2 hours without changing the dataset size or the model algorithm. The SageMaker instance type currently used is ml.m5.2xlarge. Which action should the data scientist take to achieve the desired training time?

A data scientist is training a neural network on a dataset with 1 million images. The training loss decreases steadily but the validation loss starts to increase after 10 epochs. Which action should the scientist take to improve generalization?

A data scientist wants to use a linear regression model to predict house prices. After training, the model shows high bias and low variance. Which action would most likely improve the model's performance?

A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents only 5% of the data. The model currently achieves 95% accuracy but only 10% recall on the positive class. Which metric should the scientist focus on to improve the model's ability to detect the positive class?

A machine learning team is using Amazon SageMaker to train a model with a large dataset stored in S3. The training job is taking too long. Which THREE of the following actions can reduce training time? (Choose three.)

A machine learning engineer is deploying a model to an Amazon SageMaker endpoint for real-time inference. The model is a large ensemble that requires 4 GB of memory. The engineer wants to minimize cost while ensuring the endpoint can handle up to 100 concurrent requests with a latency under 200 ms. Which instance configuration is most appropriate?

A data scientist is training a recurrent neural network (RNN) for time series forecasting. The model's training loss is not decreasing, and the gradients are vanishing. Which technique should the scientist apply to address vanishing gradients?

Which THREE techniques can help reduce overfitting in a neural network? (Choose 3)

A team has trained a deep learning model on Amazon SageMaker using a custom Docker container. They want to deploy the model to a SageMaker endpoint for real-time inference. Which format should the model artifacts be in?

A machine learning team is using Amazon SageMaker to tune hyperparameters for a neural network. They have defined a hyperparameter tuning job with a random search strategy. The training time per job is very long. Which strategy can reduce the total tuning time?

A company uses SageMaker to train a model that processes sensitive customer data. Due to compliance, the training data must be encrypted at rest and in transit, and the model artifacts must be stored in a secured S3 bucket with encryption. Which combination of actions is REQUIRED?

A data scientist is evaluating a binary classification model. The model's confusion matrix shows: True Positives=80, False Positives=20, True Negatives=900, False Negatives=0. Which THREE metrics can be calculated from this confusion matrix? (Choose three.)

A company uses Amazon SageMaker to train a model for fraud detection. The training data is highly imbalanced. The data scientist uses SMOTE to oversample the minority class. However, the model still has poor recall on the minority class. Which additional technique should the data scientist consider?

A data scientist is building a text classification model. The dataset contains 10,000 documents, each labeled with one of 5 categories. Which algorithm is most suitable for this task?

A data scientist is using Amazon SageMaker Autopilot to automatically build a model. The dataset contains a mix of numerical and categorical features. After the experiment completes, Autopilot provides several candidate pipelines. Which pipeline is MOST likely to be ranked highest by Autopilot?

A machine learning engineer is tuning hyperparameters for a gradient boosting model using Amazon SageMaker Automatic Model Tuning. The objective metric is validation accuracy. After several tuning jobs, the best accuracy achieved is 0.85, but the engineer suspects the model is overfitting. Which hyperparameter adjustment is most likely to reduce overfitting?

Which TWO of the following are valid techniques for handling missing values in a dataset for machine learning?

Refer to the exhibit. A data scientist creates a SageMaker notebook instance using this Terraform configuration. The notebook fails to start. The logs indicate 'The IAM role does not have the necessary permissions'. Which addition to the IAM role policy is MOST likely needed?

An e-commerce company wants to build a recommendation system. They have user-item interaction data (clicks, purchases) and user demographic data. The goal is to recommend items that a user is likely to purchase. Which approach should be used?

A data scientist is performing feature engineering for a machine learning model. The dataset contains categorical features with high cardinality. Which THREE techniques are appropriate for encoding high-cardinality categorical features?

A team is building a regression model to predict house prices. They observe that the model performs well on training data but poorly on validation data. Which THREE actions can help reduce overfitting? (Choose THREE.)

Refer to the exhibit. A data scientist checks the status of a SageMaker endpoint and sees the output above. What does this indicate?

A data scientist trains a convolutional neural network (CNN) for image classification. The training loss decreases steadily, but the validation loss starts increasing after 10 epochs. Which technique should the data scientist use to address this problem?

A company is building a recommendation system using collaborative filtering on Amazon SageMaker. The dataset contains user-item interactions with a long-tail distribution: a few items have millions of interactions, while most items have very few. The model currently uses matrix factorization with ALS. The recall@20 metric is low for niche items. Which modification would most likely improve recall for long-tail items?

Which THREE techniques help reduce overfitting in a neural network? (Select THREE.)

A data scientist is training a binary classification model to predict customer churn. The dataset has 10,000 samples with 500 churners (5% positive class). Which TWO techniques should the scientist use to address the class imbalance? (Choose TWO.)

A machine learning engineer is tuning a gradient boosting model using SageMaker Hyperparameter Tuning. The objective is to minimize MAE. The tuning job uses 20 training jobs. After 10 jobs, the best objective value is 5.2. Which action should the engineer take to potentially improve the result?

A data scientist is training a random forest model for a binary classification task. The dataset has 100,000 samples and 500 features. The model is overfitting. Which TWO actions are MOST likely to reduce overfitting?

A data scientist is using Amazon SageMaker to train a linear regression model. The training job fails with the error: 'AlgorithmError: Input data has NaN values'. Which step should the data scientist take to resolve this issue?

A data scientist is training a deep learning model on a large dataset using Amazon SageMaker. The training job is taking too long. The scientist notices that GPU utilization is low and data loading is the bottleneck. Which action should the scientist take to improve training performance?

A data scientist is training a deep learning model using TensorFlow on Amazon SageMaker. The training job uses a single GPU instance but the GPU utilization is low. Which action is MOST likely to improve GPU utilization?

A data scientist is training a deep learning model on Amazon SageMaker using a large dataset stored in S3. The training job is taking too long due to high I/O latency waiting for data to be downloaded from S3. Which action would MOST effectively reduce the I/O latency?

A company is building a recommendation system for an e-commerce platform. The system needs to suggest products to users based on past purchases and browsing history. Which approach would be most appropriate for this use case?

A data scientist is training a binary classifier using a large dataset with class imbalance (90% negative, 10% positive). After training a logistic regression model, the F1 score is low but accuracy is high. Which TWO actions should the data scientist take to improve model performance? (Choose 2.)

A data scientist is building a recommender system using Amazon SageMaker. The dataset contains user-item interactions with implicit feedback (clicks). Which THREE evaluation metrics are appropriate for this use case?

A data scientist is training a classification model on an imbalanced dataset where the positive class represents only 5% of the data. Which technique would BEST address the class imbalance without discarding data?

A company is building a recommendation system using matrix factorization. The training data contains user-item interactions. The model performs well on the training set but poorly on the test set. Which regularization technique should be applied to improve generalization?

A company uses SageMaker to train a large language model. The training job is taking too long. The data scientist wants to use distributed training with data parallelism. Which SageMaker feature should be used?

A data scientist is training a deep learning model for image classification using Amazon SageMaker. The training job is taking too long. The data scientist notices that GPU utilization is low (around 30%). Which action is most likely to improve GPU utilization and reduce training time?

Refer to the exhibit. A SageMaker training job using the built-in Linear Learner algorithm fails with 'Loss function returned NaN'. Which hyperparameter change is MOST likely to resolve this issue?

A data scientist is building a recommendation system using collaborative filtering. The dataset contains user-item interactions in a sparse matrix. The model will be trained on Amazon SageMaker using the built-in Factorization Machines algorithm. Which data format should the scientist use for the training data?

A company is building a binary classification model to predict customer churn. The dataset is highly imbalanced (95% non-churn, 5% churn). The data scientist uses SMOTE to oversample the minority class. After training a logistic regression model, the recall for the churn class is 0.80, but the precision is only 0.10. Which action would MOST likely improve precision without significantly harming recall?

A data scientist is training a deep learning model on Amazon SageMaker using a PyTorch estimator. The training job runs on a single ml.p3.2xlarge instance but is taking too long. The scientist wants to reduce training time by using distributed data parallelism across multiple GPUs. Which change to the training script and SageMaker estimator is required?

A data scientist is training a gradient boosting model using SageMaker. The model is overfitting to the training data. Which TWO actions can help reduce overfitting? (Choose 2)

A data scientist is building a regression model to predict house prices. The dataset contains features like number of bedrooms, square footage, and location. After training, the model has high variance. Which technique should the data scientist use to reduce variance without significantly increasing bias?

A company is building a recommendation system using matrix factorization. The dataset has 1 million users and 100,000 items. The data scientist trains a model using SageMaker's Factorization Machines algorithm. The model achieves a root mean squared error (RMSE) of 0.95 on the test set. However, the business requires RMSE below 0.90. The data scientist has already tuned hyperparameters like number of factors and learning rate. Which additional step should the data scientist take to improve RMSE?

A data scientist is training a deep learning model for image classification on Amazon SageMaker. The dataset consists of 10,000 images of size 224x224 pixels. The training job uses a single ml.p3.2xlarge instance. The data scientist notices that the GPU utilization is very low (~20%) and the training is slow. Which change would most likely improve GPU utilization?

A company runs an e-commerce platform on AWS. They have a SageMaker endpoint serving a product recommendation model. The model uses a custom container with a TensorFlow model. Recently, the endpoint has been returning high latency and occasional 504 errors during peak traffic. The data scientist observes that the model inference time is around 200 ms per request, but the endpoint is configured with a single ml.c5.large instance. The traffic spikes can reach 100 requests per second. The data scientist needs to reduce latency and eliminate 504 errors. Which course of action is most appropriate?

A data scientist is training a random forest model. During hyperparameter tuning, which parameter is MOST effective at reducing overfitting?

A data scientist is training a random forest classifier on Amazon SageMaker and wants to reduce overfitting. Which TWO actions should the scientist take? (Choose TWO.)

A machine learning engineer needs to deploy a real-time inference endpoint for a model that requires GPU acceleration for low latency. Which AWS service should be used?

Refer to the exhibit. A SageMaker training job failed with the error shown. What is the most likely cause of this error?

A data scientist is trying to launch a SageMaker training job using an IAM role with the above policy. The training job fails with an access denied error. What is the MOST likely reason?

A machine learning team is deploying a real-time inference endpoint for a fraud detection model using Amazon SageMaker. The model requires low latency (<100 ms) and the team expects a steady stream of requests with occasional spikes. Which instance type and deployment strategy should they use to minimize cost while meeting latency requirements?

A machine learning engineer is training a linear regression model on a dataset with 50 features. After training, the model achieves high accuracy on the training set but poor accuracy on the test set. Which technique should the engineer use to address this issue?

A data scientist is tuning a linear regression model and observes that the model has high bias and low variance. Which action is most likely to improve model performance?

A machine learning engineer needs to deploy a model that makes real-time predictions with latency under 100ms. The model is a small ensemble of decision trees. Which AWS service is MOST suitable?

A data scientist is tuning hyperparameters for an XGBoost model on a large dataset using Amazon SageMaker. The training job is taking too long, and they want to speed up the tuning process. Which strategy is most effective?

A data scientist is training a classification model on a dataset with missing values in several features. The data scientist wants to use SageMaker to train the model. Which TWO approaches can the data scientist use to handle missing data within the SageMaker training pipeline? (Choose two.)

An e-commerce company uses a linear regression model to predict customer lifetime value (LTV). The model shows high variance on the test set, with training RMSE much lower than test RMSE. Which of the following is the MOST effective approach to reduce overfitting?

A data scientist is training a deep learning model using Amazon SageMaker. The training loss is decreasing, but the validation loss starts increasing after 10 epochs. The model is overfitting. Which TWO actions should the data scientist take to reduce overfitting? (Choose 2.)

A company uses Amazon SageMaker to train a model for detecting fraudulent transactions. The dataset is highly imbalanced (99.9% legitimate, 0.1% fraudulent). Which approach is most effective to address this imbalance?

A team deployed a SageMaker endpoint with the configuration shown in the exhibit. During a traffic spike, the endpoint becomes unresponsive. Which change to the endpoint configuration would best improve availability?

A data scientist is training a gradient boosting model using SageMaker's built-in XGBoost algorithm. The dataset has missing values in several features. Which TWO actions should the data scientist take to handle missing values effectively? (Choose two.)

Which TWO metrics are suitable for evaluating a regression model? (Select TWO.)

A company is using SageMaker to train a model, but the training job fails with an out-of-memory error. Which action should the data scientist take to resolve this issue?

A company is using SageMaker to train a deep learning model with TensorFlow. The training job is running on an ml.p3.16xlarge instance. The data scientist wants to maximize GPU utilization. Which configuration should be used?

Drag and drop the steps to use Amazon SageMaker Debugger to debug a training job in the correct order.

A team is using Amazon SageMaker to train a deep learning model. The training job is taking too long, and they want to reduce training time without significant accuracy loss. They have already tried increasing the number of instances. Which technique should they consider next?

A data scientist is training a deep learning model for image classification using Amazon SageMaker. The training job is taking too long. The data scientist wants to speed up training by using distributed training across multiple GPUs. Which SageMaker feature or configuration should the data scientist use?

A company is using Amazon SageMaker to deploy a machine learning model for real-time inference. The model was trained using XGBoost and achieves high accuracy. However, during deployment, the endpoint returns a 'ModelError' when receiving input data. The input is a CSV string. What is the most likely cause?

A data scientist is training a model using SageMaker's built-in XGBoost algorithm with a large dataset stored in CSV format. The training job is using File mode. The data scientist wants to reduce the time it takes to start training. Which approach would be most effective?

A data scientist is trying to create a SageMaker training job but receives an access denied error. The IAM policy attached to the role is shown in the exhibit. What is the most likely cause of the error?

A team is using Amazon SageMaker to train a model and wants to automatically stop training when the model stops improving to save costs. Which SageMaker feature should they use?

A data scientist is using Amazon SageMaker to train a random forest model for a binary classification task. The dataset has 50 features and 10,000 samples. The model achieves high training accuracy but poor test accuracy. Which TWO actions should the scientist take to improve generalization?

A data scientist has this IAM policy attached to an IAM role used by SageMaker. When trying to create a training job, the scientist gets an access denied error. The training data is in 's3://my-bucket/training-data/'. What is the most likely cause?

A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents 5% of the data. The model achieves 99% accuracy but only identifies 10% of the actual positive cases. Which metric should the data scientist focus on to evaluate the model's performance on the positive class?

A data scientist is training a binary classifier using imbalanced data. Which TWO techniques can help improve model performance on the minority class? (Choose two.)

Refer to the exhibit. A data scientist ran a SageMaker training job and reviewed the logs. The training completed quickly, but the model performance is very poor. What is the most likely cause?

A data scientist is performing feature selection for a classification problem with 100 features. The data scientist wants to reduce overfitting and improve model interpretability. Which THREE methods are appropriate for feature selection? (Choose THREE.)

A data scientist is training a linear regression model and wants to handle multicollinearity among features. Which TWO actions are appropriate?

A company uses Amazon SageMaker to train a classification model. The training job fails with an error indicating that the algorithm requires a GPU but the instance type does not have one. The scientist used the built-in XGBoost algorithm. What should the scientist do to resolve the issue?

A machine learning team is deploying a model that performs real-time inference on streaming data from Amazon Kinesis Data Streams. The model requires sub-100ms latency. Which deployment option should the team choose?

A data scientist is evaluating a classification model. The confusion matrix shows that the model has 50 true positives, 100 true negatives, 20 false positives, and 30 false negatives. Which TWO metrics can be calculated from this confusion matrix? (Choose two.)

A company is building a recommendation system for an e-commerce platform. The data includes user IDs and item IDs. Which SageMaker built-in algorithm is most appropriate?

A company is building a binary classifier to detect fraudulent transactions. The dataset is highly imbalanced with only 0.1% positive cases. The data scientist uses logistic regression and obtains 99.9% accuracy on the test set. Which metric should the data scientist use to evaluate the model's performance?

A company wants to deploy a machine learning model that requires GPU acceleration for inference. The model is small and can fit on a single GPU. Which SageMaker endpoint configuration is MOST cost-effective?

A data scientist is building a binary classifier for loan default prediction. The cost of a false negative (missing a default) is 10 times higher than the cost of a false positive. Which evaluation metric is MOST appropriate?

A data scientist is building a time series forecasting model for monthly sales. The data shows strong seasonality with a yearly pattern. They plan to use Amazon Forecast. Which algorithm should they choose?

Which THREE of the following are common causes of overfitting in machine learning models?

A data scientist is tuning a gradient boosting model using Amazon SageMaker's Automatic Model Tuning (hyperparameter optimization). The objective metric is validation:auc. After 50 training jobs, the best model still has a validation AUC of only 0.65. The scientist suspects overfitting because the training AUC is 0.99. Which hyperparameter configuration is MOST likely to reduce overfitting?

A company is using SageMaker Autopilot to automatically build a binary classification model. After the AutoML job completes, the data scientist wants to understand which features are most important for the best candidate model. How can the scientist get feature importance?

A company is using Amazon SageMaker to deploy a model for real-time inference. The model is a deep neural network that requires GPU for low latency. The endpoint currently uses a single ml.p3.2xlarge instance. Traffic is expected to increase by 5x. Which TWO actions should the company take to handle the increased traffic?

A machine learning team is using Amazon SageMaker to train a model using a custom Docker container. The training job fails with an error: 'Unable to write to /opt/ml/model'. The container does not have root access. What is the most likely cause?

A data scientist is building a model to predict customer churn. The dataset has 20 features, including categorical variables with high cardinality (e.g., ZIP code). The data scientist wants to use a linear model. Which feature engineering technique is MOST appropriate for the high-cardinality categorical features?

A data scientist is using Amazon SageMaker to train a model using the built-in XGBoost algorithm. The training job is taking a long time. The data scientist notices that the input data is in CSV format and the training job is using File mode. The data size is 50 GB. What is the BEST way to reduce training time?

A company is training a deep learning model on SageMaker using a large dataset stored in S3. The training job is taking a long time due to I/O bottlenecks. Which action would MOST effectively reduce the I/O bottleneck?

A data scientist is using Amazon SageMaker to build a text classification model. The dataset has 100,000 labeled samples and 20 classes. The scientist wants to use a pre-trained BERT model and fine-tune it. Which approach is MOST cost-effective?

A team is using Amazon SageMaker to train a deep learning model for image classification. The training job is taking too long, and they want to reduce training time without sacrificing model accuracy. Which approach is most effective?

A company uses Amazon SageMaker to deploy a real-time inference endpoint for a regression model. The endpoint is experiencing high latency during spikes in traffic. The data scientist needs to reduce latency while maintaining cost efficiency. Which action should the data scientist take?

A data scientist is using Amazon SageMaker to train a linear learner model for regression. After reviewing the training logs, the data scientist notices that the loss is not decreasing and remains high. The learning rate is set to 0.01. The data is normalized. What should the data scientist do to improve convergence?

A data scientist is training a text classification model using Amazon SageMaker's built-in BlazingText algorithm. The dataset contains 1 million documents. Which TWO hyperparameters are most important to tune for improving model accuracy?

A company is building a fraud detection model using a random forest classifier. The dataset is highly imbalanced with 99% legitimate transactions and 1% fraudulent. The model currently achieves 99% accuracy on the test set, but the fraud recall is only 10%. The business requires at least 80% recall for fraud. The data scientist has tried oversampling the minority class and adjusting class weights, but recall remains below 40%. The dataset contains millions of transactions with hundreds of features. Which approach should the data scientist try next to improve fraud recall?

A company uses Amazon SageMaker to train a model and wants to track metrics like loss and accuracy in real-time. Which SageMaker feature should be used?

Which THREE techniques can help reduce overfitting in a neural network trained on a small dataset?

A machine learning engineer is using Amazon SageMaker to deploy a model for real-time inference. The model is a large ensemble that requires 4 GB of memory and has a latency requirement of 100 ms. Which instance type and deployment configuration should the engineer choose to optimize cost while meeting requirements?

A company is building a real-time fraud detection system using Amazon SageMaker. The model must have low latency (under 10ms) and high throughput (thousands of predictions per second). The team has trained a gradient boosting model using XGBoost. Which SageMaker inference option is MOST suitable?

A data scientist is building a regression model to predict energy consumption. The dataset includes features like temperature, humidity, day of week, and holiday flags. The scientist uses a linear regression model and obtains an R-squared of 0.85 on training and 0.40 on test. The scientist suspects the model is not capturing non-linear relationships. Which approach should the scientist use to capture non-linearity?

A data scientist is building a classification model to predict customer churn. The dataset has 10,000 samples with 100 features. After training a logistic regression model, the scientist observes that the model has high variance (overfitting). Which technique can reduce overfitting?

A data scientist is using Amazon SageMaker to train a model. The training dataset is stored in S3 as CSV files. The scientist wants to use the SageMaker built-in Linear Learner algorithm. Which input mode should be used for optimal performance?

A company runs a real-time recommendation system on SageMaker with a model that uses a deep neural network. The endpoint uses a single ml.p3.2xlarge instance. Recently, the number of users has grown, and the endpoint's latency has increased from 50ms to 200ms, exceeding the SLA of 100ms. The model inference code is optimized and cannot be improved further. The company wants to reduce latency while minimizing cost. The data scientist has the following options: A. Switch to a larger instance type with more GPU memory, such as ml.p3.8xlarge. B. Use SageMaker's Elastic Inference to attach an EI accelerator to the existing instance. C. Deploy the model on multiple smaller instances (e.g., ml.p3.2xlarge) behind a load balancer and distribute traffic. D. Convert the model to use TensorFlow Lite and deploy on a CPU-based instance. Which option is the MOST cost-effective and meets the latency requirement?

Refer to the exhibit. An IAM policy is attached to a SageMaker notebook instance. A data scientist is trying to invoke the endpoint 'my-endpoint' from the notebook but receives an AccessDenied error. What is the likely cause?

Which TWO of the following are appropriate use cases for Amazon SageMaker built-in algorithms?

A machine learning engineer is using Amazon SageMaker to train a model. The training job fails with an out-of-memory error. The training data size is 10 GB and the instance is ml.m5.xlarge (16 GB memory). Which change is MOST likely to resolve the issue without increasing cost?

A data scientist is evaluating a linear regression model. Which TWO metrics are appropriate for evaluating the model's performance?

A team deployed a SageMaker endpoint for real-time inference using a PyTorch model. After monitoring, they notice that the latency is highly variable, with p99 latency 10x the p50 latency. The endpoint uses a single ml.c5.2xlarge instance with auto-scaling based on average CPU utilization. Which change is most likely to reduce latency variability?

A company is building a sentiment analysis model using Amazon SageMaker BlazingText. The training data consists of 100,000 product reviews. The data scientist wants to use the Word2Vec algorithm to generate word embeddings. Which configuration is required to use the continuous bag-of-words (CBOW) architecture?

A data scientist is building a binary classification model to predict customer churn. The dataset is highly imbalanced, with only 5% of customers churning. The scientist evaluates several models using accuracy, precision, recall, and F1 score. Which metric is most appropriate for comparing model performance in this scenario?

A data scientist is building a binary classification model to predict customer churn. The dataset has 10,000 samples with 500 churners (positive class). Which TWO techniques should be used to address the class imbalance? (Choose 2.)

A data scientist is training a binary classification model on a dataset where the positive class represents only 1% of the data. The model's accuracy is 99%, but the recall for the positive class is 0%. Which metric should the scientist use to evaluate the model's performance effectively?

A data scientist is using Amazon SageMaker to train a model and wants to automatically stop the training job if the loss does not improve for a certain number of epochs. Which SageMaker feature can be used for this purpose?

A company has deployed a real-time inference endpoint using SageMaker for a fraud detection model. The model uses a Random Forest classifier. The endpoint receives predictions but the latency is too high. The metric shows p99 latency of 500ms, but the requirement is under 200ms. The team has already optimized the instance type to the maximum allowed by their budget. The data scientist suggests: A) Reducing the number of trees in the Random Forest model. B) Switching to a linear model like Logistic Regression. C) Enabling SageMaker's batch transform instead of real-time endpoint. D) Adding more instances to the endpoint behind a load balancer. Which option will MOST effectively reduce latency while maintaining acceptable accuracy?

Which TWO metrics are appropriate for evaluating a binary classification model when the cost of false negatives is high?

A machine learning engineer is using SageMaker to train an XGBoost model on a dataset with a severe class imbalance (1:1000). The goal is to maximize recall on the minority class. Which hyperparameter tuning strategy is MOST appropriate?

Refer to the exhibit. A data scientist is configuring SageMaker Model Monitor for data quality checks. The configuration above is used. What is the purpose of the `ProbabilityThresholdAttribute` set to "0.5"?

A data scientist is building a recommender system using collaborative filtering. The dataset is sparse (99% missing values). Which algorithm is best suited?

Which TWO SageMaker features can be used to perform hyperparameter optimization? (Choose 2)

During training of a SageMaker built-in object detection algorithm, the loss is not decreasing after several epochs. Which troubleshooting step should be taken first?

A data scientist is using Amazon SageMaker to train a linear regression model. After training, the scientist notices that the model has a high bias. What is the most likely cause?

Refer to the exhibit. A data scientist is trying to run a SageMaker training job using a script that reads data from the S3 bucket 'my-bucket' and writes the model artifact to the same bucket. The training job fails with an access denied error. What is the likely cause?

A data scientist is building a binary classifier to predict customer churn. The dataset is highly imbalanced (5% churn). Which TWO techniques can help improve the model's ability to detect churn?

A machine learning engineer is deploying a model using Amazon SageMaker and wants to automatically scale the endpoint based on the number of incoming requests. Which scaling policy should be used?

A machine learning team needs to deploy a model that makes real-time predictions with latency under 100 ms. The model is a deep neural network with 500 MB of parameters. Which AWS service should they use?

A machine learning team is deploying a real-time inference endpoint for a recommendation model using Amazon SageMaker. The model takes a long time to load (several minutes) due to its size (5 GB). Which deployment strategy minimizes the cold start latency?

A data scientist is training a deep learning model for object detection using Amazon SageMaker. The training job is using a single GPU instance and is taking too long. Which THREE actions can reduce training time? (Choose THREE.)

A data scientist is deploying a regression model in Amazon SageMaker that predicts housing prices. The model shows high bias (underfitting). Which action is most likely to reduce bias?

A data scientist is evaluating a regression model. Which TWO metrics are appropriate for evaluating regression performance?

A company is using Amazon SageMaker to train a XGBoost model for predicting customer churn. The training data is stored in an S3 bucket as CSV files. The data scientist runs a hyperparameter tuning job with 50 training jobs. The tuning job completes, but the best model's accuracy on the holdout set is lower than expected. The data scientist suspects that the hyperparameter ranges are too narrow. Which corrective action is most appropriate?

A company is using Amazon SageMaker to host a model for real-time inference. The model is a large ensemble of 10 XGBoost models, each 2 GB. The endpoint uses a single ml.c5.18xlarge instance. The inference latency is high (average 2 seconds). Which change would most effectively reduce latency?

A data scientist is using Amazon SageMaker to train a linear regression model. The training data has 10 features, and the scientist wants to interpret the model's coefficients. Which algorithm should they use?

A company uses an XGBoost model to predict equipment failures. The model has high precision but low recall. The business impact of a false negative is very high (missing a failure). Which action would MOST effectively increase recall while keeping precision reasonably high?

A company wants to deploy a machine learning model that provides real-time inference with low latency. The model is a small ensemble of three tree-based models. Which Amazon SageMaker approach is most appropriate?

A data scientist is training a linear regression model to predict house prices. The dataset contains 10 features. After training, the data scientist notices that the model has high bias (underfitting). Which action should the data scientist take to reduce bias?

A data scientist is using Amazon SageMaker to train a deep learning model using a built-in algorithm. The training job uses an ml.p3.2xlarge instance and takes 10 hours to complete. The scientist wants to reduce training time without changing the algorithm or model architecture. The instance's GPU utilization is consistently at 95%, but CPU utilization is only 20%. The data input pipeline uses SageMaker Pipe mode with the 'TrainingInputMode' set to 'Pipe'. The training dataset is 200 GB in CSV format stored in S3. Which approach is most likely to reduce training time?

A data scientist wants to build a binary classifier to predict customer churn. The dataset has 10,000 records with 500 churners (5%). Which technique should the data scientist use to address class imbalance?

A data scientist is using SageMaker to train a linear regression model. The target variable has a long-tail distribution. Which data transformation is LEAST likely to improve model performance?

A company wants to build a model to detect fraudulent transactions. The dataset has a highly imbalanced class distribution. Which technique should be used during training to handle class imbalance?

A data scientist is using an IAM role with the policy shown in the exhibit to train a model in SageMaker. The training job fails with a permissions error. What is the missing permission?

A data scientist is training a binary classifier using logistic regression. The dataset has 100,000 samples and 500 features. After training, the model achieves 95% accuracy on the training set but only 70% on the test set. The data scientist suspects overfitting. Which technique would best reduce overfitting while preserving interpretability?

A company is using Amazon SageMaker to train a time series forecasting model using the DeepAR algorithm. The training data contains multiple time series. The model is overfitting. Which action is LEAST likely to reduce overfitting?

A company uses Amazon SageMaker to host a model for real-time inference. The model is a large ensemble of 10 deep learning models, each 500 MB. The total model size is 5 GB, which exceeds the 5 GB limit for SageMaker real-time endpoints. The data scientist wants to reduce the model size without significantly impacting accuracy. The ensemble uses averaging of predictions from all models. The scientist has access to a validation set with 10,000 samples. Which technique should the scientist use to reduce the model size?

A company uses Amazon SageMaker to train a deep learning model for image classification. The training job is taking longer than expected. The data scientist observes that GPU utilization is low (around 30%) and CPU utilization is high. Which action is most likely to reduce training time?

A data scientist is building a fraud detection model using a highly imbalanced dataset. The model uses a random forest classifier. The recall for the minority class is 0.6, and precision is 0.9. The business requires recall above 0.8. Which action should the data scientist take to improve recall?

A data scientist runs a SageMaker training job and receives the above error. The S3 bucket 'my-bucket' contains a folder 'data' with a file 'data.csv'. What is the MOST likely cause of the error?

Which THREE factors should be considered when selecting the appropriate algorithm for a regression problem? (Choose 3.)

A company is using SageMaker built-in object detection algorithm to detect defects in manufacturing images. The model is trained on 10,000 labeled images and achieves 95% accuracy. However, in production, the model misclassifies many defective items as non-defective (false negatives). The business requires recall > 90% for the defect class. Which action should they take?

A company is deploying a machine learning model for fraud detection. The model outputs a probability score. The cost of false negatives is very high. Which TWO metrics should the company focus on optimizing?

A team is deploying a real-time inference endpoint using Amazon SageMaker. The model is a large deep learning model that requires GPU for inference. The endpoint must handle variable traffic patterns with minimal latency. Which deployment strategy should the team use?

A data scientist is training a deep learning model on Amazon SageMaker using a custom Docker container. The training job fails with an error 'OutOfMemoryError: CUDA out of memory'. The instance type is ml.p3.2xlarge (8 GB GPU memory). The model has 50 million parameters. What is the most likely cause and solution?

A data scientist is training a binary classification model using Amazon SageMaker. The dataset is highly imbalanced (99% negative class, 1% positive class). The model currently achieves 99% accuracy but fails to detect most positive cases. Which metric should the data scientist primarily use to evaluate model performance?

A machine learning team is using SageMaker to train a model with the built-in Linear Learner algorithm. The dataset has 1 million rows and 20 features. The training completes, but the model's mean squared error (MSE) is high. Which parameter adjustment is most likely to reduce MSE?

A machine learning team is using SageMaker to train a custom TensorFlow model on a dataset that fits in memory. The training job is taking too long. The team wants to reduce training time without changing the model architecture. Which approach is most effective?

Refer to the exhibit. An IAM policy is attached to a SageMaker notebook instance role. A data scientist is trying to train a model using the SageMaker built-in XGBoost algorithm with training data in 'my-bucket/training-data/' and expects output in 'my-bucket/output/'. The training job fails with an access denied error. What is the most likely missing permission?

A data scientist is training a gradient boosting model using SageMaker's built-in XGBoost algorithm. The model is overfitting on the training data. Which hyperparameter adjustment is most likely to reduce overfitting?

A machine learning engineer is building a binary classification model to predict customer churn. The dataset is highly imbalanced (5% churn). The engineer wants to use Amazon SageMaker's built-in XGBoost algorithm. Which combination of hyperparameters is most appropriate for this scenario?

A company uses SageMaker to deploy a real-time inference endpoint for a fraud detection model. The model is an XGBoost model trained on 50 features. The endpoint receives 100 requests per second, but latency is higher than the required 200 ms. The team wants to reduce latency without retraining. What should they do?

A machine learning engineer is deploying a PyTorch model to SageMaker. The model requires custom inference logic. Which approach should the engineer use?

Refer to the exhibit. A data scientist runs the above CLI command to create a SageMaker training job. The job fails with an error 'Unable to read data from s3://bucket/train/'. What is the MOST likely cause?

A data scientist is using Amazon SageMaker to train a custom TensorFlow model. The training job is failing with the error: 'OutOfRangeError: End of sequence'. The input data is stored in TFRecord format in S3. What is the most likely cause?

A data scientist is using Amazon SageMaker to train a deep learning model. The training job is taking too long. Which THREE actions can reduce training time?

A data scientist is building a deep learning model using Amazon SageMaker. The model is overfitting the training data. Which THREE actions can help reduce overfitting?

A team is deploying a SageMaker endpoint for a model that was trained with scikit-learn. The endpoint receives spikes in traffic during business hours. The team wants to minimize cost while ensuring availability during spikes. Which endpoint configuration is MOST appropriate?

Which TWO of the following are examples of unsupervised learning tasks?

A company is building a recommender system using matrix factorization. The dataset contains user-item interactions. The model is trained on a large dataset, but the recommendations for new users are poor. Which approach would MOST effectively address this cold-start problem?

A data scientist is training a neural network on image data using TensorFlow with GPU instances on SageMaker. The training is slow because the GPU utilization is low. The data pipeline uses tf.data with a large number of preprocessing operations. Which action would most likely increase GPU utilization?

Refer to the exhibit. A data scientist is using Amazon SageMaker Ground Truth to label a dataset. The output manifest file references S3 objects with metadata. The scientist notices that a training job using the labeled data yields poor accuracy. What is the most likely issue?

A data scientist is training a neural network for a multi-class classification problem with 100 classes. The model uses a softmax output layer and cross-entropy loss. During training, the loss decreases steadily but the accuracy on the validation set plateaus early. Which of the following is the most likely cause?

A machine learning engineer is deploying a model that predicts loan defaults. The model uses features like income, credit score, and debt-to-income ratio. After deployment, the model's performance degrades over time. Which concept best describes this phenomenon?

A company is using Amazon SageMaker to train a linear regression model. The data scientist notices that the training loss is decreasing but the validation loss has started to increase after a few epochs. What is the most likely cause?

A company uses Amazon SageMaker to host a model for real-time inference. The model is a large ensemble that takes 2 seconds to load into memory. To reduce cold start latency, the data scientist uses SageMaker's managed warm pools. However, they notice that during a sudden traffic spike, new instances still experience high latency. What is the BEST way to ensure consistently low latency for all requests?

A retail company uses Amazon SageMaker to train a model for product demand forecasting. The dataset contains daily sales data for 10,000 products over 3 years. The data includes features like price, promotions, holidays, and seasonality. The data scientist uses a linear regression model and gets an RMSE of 50 units. However, the business requires more accurate forecasts, especially for products with high variability. The scientist notices that the residuals show a pattern: the model underestimates demand during promotional periods. Which approach should the scientist take to improve the model?

A data scientist is training a binary classification model on a highly imbalanced dataset (99% negative class, 1% positive class). The model currently achieves 99% accuracy but only identifies 0.5% of true positives. Which metric should the data scientist focus on to improve model performance?

A data scientist is setting up a SageMaker training job and has attached this IAM policy to the execution role. The training job fails with an access denied error when trying to write to the output path 's3://my-bucket/output/model.tar.gz'. What additional permission is needed?

A machine learning engineer is using Amazon SageMaker to deploy a model for real-time inference. The model must respond within 100 milliseconds. The initial deployment uses a single ml.m5.large instance, but latency is too high. Which change should the engineer make to reduce latency?

A company is building a binary classifier to detect fraudulent transactions. The dataset is highly imbalanced (99% legitimate, 1% fraudulent). Which metric is most appropriate for evaluating the model?

A company is using Amazon SageMaker to train a model. Which TWO metrics should be used to evaluate a binary classification model?

A machine learning engineer is deploying a model to Amazon SageMaker for real-time inference. The model requires low latency and must handle variable traffic patterns. Which SageMaker feature should the engineer use to automatically scale the number of instances based on demand?

A company is deploying a machine learning model using Amazon SageMaker. The model requires GPUs for inference. Which THREE configurations can the company use to meet this requirement? (Choose THREE.)

Refer to the exhibit. The training job 'my-job' failed with the error 'Unable to pull image from ECR'. What is the most likely cause?

A company uses a SageMaker endpoint for real-time inference. They need to ensure high availability during deployment updates. Which THREE steps achieve this? (Choose 3)

Which THREE evaluation metrics are appropriate for a multi-class classification problem? (Choose 3.)

A data scientist is deploying a SageMaker model using CloudFormation. The stack creation fails with the above error. What is the MOST likely cause?

A CloudFormation stack creation failed. The SageMaker endpoint resource shows CREATE_FAILED. What is the most likely issue?

A machine learning team is using Amazon SageMaker to train a linear regression model. The team notices that the training loss decreases rapidly initially but then plateaus at a high value. What is the MOST likely cause?

A company is deploying a model that predicts customer churn. The model's recall for the churn class is 0.9, but precision is 0.4. The business cost of false positives is high. Which strategy would MOST likely improve precision without significantly harming recall?

A data scientist is building a multi-class classification model with 10 classes. The dataset has 100,000 samples. After training a random forest with 100 trees, the model achieves 85% accuracy on the test set. However, the data scientist notices that for one rare class (1% of data), recall is only 5%. Which technique is MOST likely to improve recall for the rare class without significantly reducing overall accuracy?

A data scientist is building a binary classification model to predict whether a customer will subscribe to a service. The dataset contains 20 features, including categorical variables with high cardinality (e.g., zip code with 10,000 unique values). The scientist uses a logistic regression model and obtains a training AUC of 0.85 and a test AUC of 0.60. The scientist suspects overfitting due to high cardinality features. Which approach should the scientist use to address this issue?

A data scientist is using a decision tree algorithm for a classification task. The tree is very deep and achieves 100% accuracy on the training set but performs poorly on the test set. Which technique should the data scientist use to improve generalization?

A data scientist is using Amazon SageMaker to train a linear regression model. The training data contains missing values. Which preprocessing step should be applied before training?

A data scientist ran a SageMaker training job that failed with the error shown. The training script expects the data in '/opt/ml/input/data/training/train.csv'. What is the most likely issue?

A machine learning engineer is deploying a model to SageMaker for real-time inference. The model is a TensorFlow SavedModel. Which SageMaker capability should be used to create an endpoint?

A data scientist is using Amazon SageMaker Debugger to monitor training. Which THREE types of issues can Debugger monitor?

A company is using Amazon SageMaker to tune hyperparameters for a gradient boosting model. The objective is to minimize root mean squared error (RMSE). The data scientist wants to explore the hyperparameter space efficiently. Which THREE hyperparameter tuning strategies should the data scientist consider? (Choose 3.)

A data scientist is training a neural network for image classification. The training loss is not decreasing significantly, and the validation loss is high. Which TWO actions should the scientist take to address potential vanishing gradients?

A data scientist is using Amazon SageMaker's built-in BlazingText algorithm for word2vec embeddings. The dataset is a corpus of 10 million documents. After training, the data scientist observes that the learned embeddings do not capture semantic similarity well (e.g., 'king' and 'queen' are not close). Which hyperparameter adjustment is most likely to improve the quality of embeddings?

A data scientist is training a neural network for time series forecasting. The training loss decreases initially but then starts to increase after 20 epochs. Which action should the scientist take to address this?

A data scientist is training a model using Amazon SageMaker. The training job is running on GPU instances, but the GPU utilization is low. Which TWO actions could improve GPU utilization?

A data scientist is training a text classification model using Amazon SageMaker. The dataset consists of 100,000 labeled documents. The data scientist notices that the model performs well on the training set but poorly on the validation set. Which regularization technique should the data scientist apply to reduce overfitting?

A data scientist is using Amazon SageMaker to train a model using a built-in algorithm. The training job fails with an error indicating that the algorithm expects the data to be in recordIO-protobuf format, but the input is CSV. What is the most efficient way to resolve this?

A data scientist runs a training job that fails. The CLI output is shown in the exhibit. What is the MOST likely cause of the failure?

A data scientist is training a neural network on Amazon SageMaker and wants to automatically stop training if the validation loss does not improve for 5 consecutive epochs. Which feature should they use?

Refer to the exhibit. A data scientist is assigned an IAM policy to deploy a SageMaker model. When the scientist tries to create an endpoint, the action fails with an authorization error. What is the missing permission?

A data scientist is using SageMaker to train a deep learning model with a large dataset stored in S3. The training is taking a long time. Which action would most likely reduce training time without sacrificing accuracy?

A data scientist is using Amazon SageMaker to train a large language model with PyTorch. The training job is taking too long. The dataset is stored in S3 and the training script uses the SageMaker PyTorch container. Which change is MOST likely to reduce training time?

A company is using Amazon SageMaker to train a large language model with billions of parameters. The training job uses multiple GPU instances in a distributed fashion. The training is converging but the loss is not decreasing as expected. The data scientist suspects that the learning rate is too high. Which technique should the data scientist use to automatically adjust the learning rate during training?

A company is building a recommendation system using collaborative filtering. The dataset contains implicit feedback (clicks) from users on items. Which algorithm is best suited for this scenario?

A data scientist is tuning a gradient boosting model using Amazon SageMaker Automatic Model Tuning. The objective metric is AUC. The training job converges quickly but the final model has low AUC on the validation set. Which hyperparameter should the data scientist adjust to improve validation AUC?

A company uses Amazon SageMaker to train a model using the built-in Linear Learner algorithm. The training data contains missing values in some features. What is the best practice for handling missing values with this algorithm?

A team is training a neural network for image classification using Amazon SageMaker. The training loss decreases rapidly but the validation loss starts increasing after a few epochs. Which action should the team take?

A company is using SageMaker to deploy a model for real-time inference. The model requires GPU for low latency. Which THREE configurations should the company consider for high availability and cost optimization? (Choose three.)

A data scientist is training a Random Forest model on Amazon SageMaker. The model performs well on the training set but poorly on the test set. Which technique should the data scientist use to address this issue?

A company is building a real-time fraud detection system using Amazon SageMaker. The model is a gradient boosting classifier trained on 500 GB of transactional data. The inference endpoint is deployed as a SageMaker real-time endpoint using an ml.c5.9xlarge instance. The model is serialized using the native format of the framework (XGBoost). The endpoint receives about 100 requests per second with an average payload size of 10 KB. The company observes that the endpoint's latency is around 200 ms, but they need under 100 ms. The data scientist profiles the endpoint and finds that the model inference time is 50 ms, but the remaining time is spent on data preprocessing and serialization/deserialization. The preprocessing involves converting JSON input to a NumPy array and then to a DMatrix. Which action is most likely to reduce latency to meet the requirement?

A data scientist is building a regression model to predict house prices. The dataset includes features such as square footage, number of bedrooms, year built, and location. After training a linear regression model, the data scientist notices that the residuals have a clear pattern when plotted against predicted values: they increase with predicted values. The model also has high RMSE. Which action should the data scientist take to improve the model?

A company has a time series dataset of daily sales for the past 5 years. They want to forecast sales for the next 30 days. The data shows weekly seasonality and a slight upward trend. Which Amazon SageMaker algorithm is most appropriate for this task?

A machine learning team is deploying a model for real-time fraud detection. The model must make predictions with less than 100ms latency. The team uses SageMaker and the model is a large ensemble of decision trees. Which SageMaker hosting option is MOST suitable?

A company uses Amazon SageMaker to train a linear regression model. After training, the model shows high bias on the training set. Which action is MOST likely to reduce bias?

Which THREE techniques can help reduce overfitting in a neural network? (Select THREE.)

A company uses Amazon SageMaker to deploy a model that predicts customer churn. The model is retrained weekly. The data scientist notices that the model's accuracy remains high, but the business reports that the model is not capturing new churn patterns. What is the most likely cause?

A data scientist is using SageMaker to train a random forest model. The dataset has 100 features and 1 million rows. The training job fails with a 'ResourceLimitExceeded' error. What is the MOST likely cause?

A data scientist is training a deep learning model on Amazon SageMaker for image classification. The training is taking a long time and the GPU utilization is consistently below 30%. What should the data scientist do to improve GPU utilization and reduce training time?

A data scientist is training a binary classification model on a dataset with 100 features and 10,000 samples. The model achieves 99% accuracy on the training set but only 65% on the test set. Which technique should be applied first to address this issue?

A data scientist is building a text classification model using a bag-of-words approach. The dataset contains 100,000 documents with a vocabulary of 50,000 unique words. The model is overfitting. Which THREE techniques can help reduce overfitting? (Choose THREE.)

Which TWO of the following are valid techniques to handle missing data in a dataset?

A company uses Amazon SageMaker to train a linear regression model. During evaluation, they observe that the model has high bias (underfitting). Which THREE actions can reduce bias?

A data scientist is training a text classification model using Amazon SageMaker's BlazingText algorithm. The dataset consists of 1 million documents, each labeled with one of 10 categories. The model achieves 92% accuracy on a held-out test set. However, when deployed, the model performs poorly on documents containing slang and typos. What should the data scientist do to improve model robustness?

A team is building a product recommendation system using matrix factorization in Amazon SageMaker. They notice that the model's training loss decreases steadily but validation loss starts increasing after 5 epochs. What is the most likely cause?

A company is deploying a machine learning model for real-time fraud detection. The model must have extremely low latency (<10 ms) and high throughput. Which THREE design choices meet these requirements? (Choose 3.)

Which TWO of the following are techniques used to reduce overfitting in a neural network?

A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents only 1% of the data. The model achieves 99% accuracy but fails to identify most positive cases. Which metric should the data scientist use to evaluate model performance?

A company is building a binary classifier to predict customer churn. The dataset has 10,000 samples with 500 churners (5% positive class). After training a logistic regression model, the precision is 0.8 and recall is 0.2. Which metric should the data scientist focus on to improve the model's ability to identify churners while minimizing false positives?

A machine learning engineer is deploying a PyTorch model on SageMaker for real-time inference. The model requires GPU for low latency. Which instance type and configuration should the engineer choose?

A company uses Amazon SageMaker to train a custom TensorFlow model for image classification. The training job runs on a single ml.p3.2xlarge instance. The dataset contains 500,000 images stored in S3. The training time is too long (over 24 hours). The data scientist wants to reduce training time without changing the model architecture. The dataset is already in TFRecord format. The training script uses the default TensorFlow data pipeline. Which change will MOST significantly reduce training time?

A media company uses SageMaker to train a neural network for content recommendation. The model uses embeddings for users and items. Training is slow and they want to reduce time. The dataset has 10 million users and 1 million items. They have a cluster of 8 p3.16xlarge instances. Which strategy is most likely to reduce training time?

A machine learning team is deploying a real-time inference endpoint for a fraud detection model using Amazon SageMaker. The model is a LightGBM classifier trained on 1 GB of tabular data. The endpoint must respond within 100 ms for 99% of requests, with a throughput of 10 requests per second. During load testing, the team observes that the 99th percentile latency is 250 ms and the endpoint CPU utilization is consistently above 90%. The team has already selected an ml.c5.xlarge instance with auto scaling enabled. Which combination of actions should the team take to meet the latency requirement? (Choose 3.)

A data scientist is using Amazon SageMaker to train a linear regression model. The training data contains 100 features and 1 million rows. The scientist notices that the model is overfitting, with training R² of 0.99 and validation R² of 0.65. The scientist has already tried adding L2 regularization and reducing the number of features. Which additional technique should the scientist try to reduce overfitting?

A data scientist is using SageMaker to train a linear learner algorithm. After training, the evaluation shows that the model has high bias. Which action is most likely to reduce bias?

A data science team is using Amazon SageMaker to train a deep learning model for object detection using the built-in SSD algorithm. The dataset consists of 100,000 labeled images stored in a SageMaker Pipe Mode input. The training job uses a single ml.p3.2xlarge instance. After 2 hours, the training job fails with the error 'ResourceLimitExceeded: The account-level service limit for ml.p3.2xlarge for training job usage is 1. Contact AWS Support to request a limit increase'. However, the team has already submitted a limit increase request and it was approved for 5 instances. What is the most likely cause of the error?

A data scientist is using Amazon SageMaker to train a deep learning model on a large dataset stored in S3. The training job is taking too long. The data scientist wants to reduce training time without changing the model architecture. Which action should they take?

A training job fails with the error shown. The training script expects a file named 'train.csv' in the 'training' channel. What is the most likely cause?

A company is using Amazon SageMaker to deploy a model for real-time inference. The model has a latency requirement of less than 100 milliseconds. During testing, the latency is around 150 milliseconds. Which action can most likely reduce the latency to meet the requirement?

A company is deploying a machine learning model for real-time fraud detection. The model must have low latency (under 100 ms) and high throughput. The data scientist trains a gradient boosting model and deploys it to a SageMaker endpoint with a single ml.c5.xlarge instance. During load testing, the endpoint exceeds the latency threshold. Which change is MOST likely to reduce latency?

A data scientist needs to evaluate a binary classification model. The dataset is balanced. Which metric is most appropriate to compare model performance?

Which TWO options are best practices for training machine learning models using SageMaker? (Choose TWO.)

A company is using Amazon SageMaker to train a model. The training job is using a large dataset stored in S3. The data scientist notices that the training job is spending a significant amount of time reading data from S3. Which approach would BEST reduce data loading time?

A data scientist is using SageMaker to train an XGBoost model for regression. The training data contains categorical features with high cardinality (e.g., zip code with over 10,000 unique values). Which feature engineering approach is MOST appropriate to avoid overfitting while preserving predictive power?

A machine learning engineer is deploying a model for real-time inference using Amazon SageMaker. The model is a large ensemble that requires 8 GB of memory and 4 vCPUs. The expected traffic is 100 requests per second with a 200 ms latency requirement. Which instance configuration should they choose?

A machine learning engineer is training a model using Amazon SageMaker. The training data is stored in S3 and is 10 TB. The engineer wants to use Pipe input mode to stream data from S3. Which algorithms support Pipe mode? (Select all that apply)

A company uses Amazon SageMaker to train a deep learning model using TensorFlow. The training job is failing with an 'OutOfMemory' error. The instance type is ml.p3.2xlarge with 16 GB GPU memory. The model has 10 million parameters. Which THREE actions should be taken to resolve the memory issue? (Choose THREE.)

A machine learning engineer is training a deep learning model on Amazon SageMaker. The training job is taking a long time. Which THREE actions can reduce training time? (Choose 3.)

A company is building a recommendation system using Amazon SageMaker Factorization Machines. The dataset includes user IDs, item IDs, and implicit feedback (clicks). The data is sparse with millions of users and items. The model needs to capture interactions between users and items. Which hyperparameter tuning strategy should be used to improve model performance?

A data scientist is training a deep learning model on SageMaker using a custom container. The training job fails with an 'OutOfMemory' error. Which THREE actions could resolve this issue? (Choose 3.)

Refer to the exhibit. A custom training job using Pipe input mode fails. The logs indicate the algorithm cannot read the data. What is the most likely issue?

A company wants to use Amazon SageMaker to train a model using a custom algorithm packaged in a Docker container. Which approach should they use?

A company is deploying a fraud detection model using Amazon SageMaker. The model is a linear learner trained on 100 GB of data. For inference, the model receives individual transactions and must return a prediction within 100 ms. Which endpoint configuration should the team use to meet the latency requirement?

A healthcare company needs to predict patient readmission risk using clinical notes. Which AWS service can be used to preprocess the text data into numerical features for a machine learning model?

Which TWO of the following are valid approaches to handle missing values in a dataset for a machine learning model?

A machine learning engineer is using Amazon SageMaker to train a model. The training data is stored in an S3 bucket encrypted with AWS KMS. The SageMaker training job fails with an AccessDenied error when trying to read the data. Which IAM policy addition should resolve the issue?

A data scientist is training a binary classifier on an imbalanced dataset where the positive class represents 1% of the data. The model currently achieves 99% accuracy but a recall of only 10% on the positive class. Which metric combination should the data scientist prioritize to evaluate model improvements?

Refer to the exhibit. A data scientist creates a SageMaker model using the configuration above. When deploying the model to an endpoint, the endpoint status remains 'Creating' for a long time and then fails. What is the most likely cause?

A data scientist needs to implement a recommendation system for an e-commerce website. Which Amazon service is specifically designed for building and deploying recommendation models?

A data scientist is using Amazon SageMaker to train a linear regression model. The training data has 10 features and 100,000 observations. The model's training loss is decreasing, but the validation loss starts increasing after a few epochs. Which step should the data scientist take first to address this issue?

Refer to the exhibit. A data scientist runs the above AWS CLI command to create a SageMaker training job using the built-in Linear Learner algorithm. The training job fails with an error. What is the most likely cause?

A machine learning engineer is deploying a model using Amazon SageMaker. The model requires preprocessing steps (e.g., scaling, encoding) that were applied during training. Which TWO options can ensure the same preprocessing is applied at inference?

Which TWO of the following are true about the bias-variance tradeoff?

A team is building a model to predict customer churn. They have 50 features, including categorical variables with high cardinality (e.g., zip code with 10,000 unique values). Which feature engineering technique is most appropriate?

A data scientist is training a deep learning model on a GPU instance. The training loss is decreasing, but the validation loss starts increasing after a few epochs. Which action should the data scientist take to address this?

A company is using Amazon SageMaker to train a linear learner model for predicting customer lifetime value. The target variable is right-skewed with a long tail. The data scientist applies a log transformation to the target variable and trains the model. The model achieves a low root mean squared error (RMSE) on the log scale. However, when the predictions are exponentiated back to the original scale, the RMSE is much higher. Which step should the data scientist take to improve the model's performance on the original scale?

Which THREE of the following are valid strategies to reduce overfitting in a deep neural network? (Choose 3)

Which TWO of the following are appropriate use cases for using Amazon SageMaker BlazingText? (Choose 2)

A company is building a recommendation system for an e-commerce platform. The data includes user-item interactions and features such as user demographics and item categories. Which algorithm would be most appropriate for generating personalized recommendations?

Refer to the exhibit. A data scientist is trying to create a SageMaker training job but receives an access denied error. The IAM policy shown is attached to their role. What is the most likely reason for the error?

A data scientist is training a neural network using a custom loss function. The training process converges, but the model's performance on the validation set is poor. The data scientist suspects that the model is overfitting. Which action should the data scientist take to diagnose overfitting?