MLA-C01 Practice Test 12 — 15 Questions

Question 1

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

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Amazon CloudWatch Events (Amazon EventBridge) rule that captures Model Monitor outcome.. Amazon EventBridge (formerly CloudWatch Events) is the native AWS service for reacting to state changes in AWS resources. SageMaker Model Monitor publishes data drift alerts as events to EventBridge, so a rule can be configured to match those specific events and trigger the SageMaker Pipeline execution as a target. This provides a fully managed, event-driven architecture without polling or custom integrations.

Answer

AWS Lambda function that polls CloudWatch logs.

Answer

S3 event notification on the monitoring output bucket.

Answer

SageMaker model monitor webhook.

Question 2

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

Accepted Answer

Deploy as a serverless endpoint. Serverless endpoints in SageMaker automatically scale to zero when not in use, so you pay only for the compute time consumed during inference requests. This makes them the most cost-effective option for intermittent traffic, as you avoid paying for idle compute capacity.

Answer

Purchase SageMaker Savings Plans for the endpoint

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Use a batch transform job for each request

Answer

Deploy as a real-time endpoint with a multi-model endpoint

Question 3

A data scientist is building a text classification model using Amazon SageMaker. The dataset is large and includes imbalanced classes. Which three techniques can help improve model performance? (Choose three.)

Accepted Answer

Using cost-sensitive learning. Option B (cost-sensitive learning) is correct because assigning higher misclassification costs to the minority class penalizes errors on rare classes more heavily, which directly counteracts the bias a model develops toward majority classes in imbalanced datasets. Option C (oversampling the minority class) is correct because duplicating or generating additional minority-class examples balances the class distribution, giving the learning algorithm more signal to learn minority patterns. Option E (SMOTE) is correct because Synthetic Minority Over-sampling Technique interpolates new synthetic samples between existing minority-class neighbors, reducing overfitting compared to naive duplication while improving minority-class recall. Option A (TF-IDF) is not among the marked answers because it is a feature-weighting method for text representation, not an imbalance-handling technique, and does not by itself correct class skew. Option D (using a linear classifier only) is not marked because restricting the algorithm to a linear model is an arbitrary constraint that neither addresses class imbalance nor guarantees better performance.

Answer

Performing feature extraction using TF-IDF

Answer

Using a linear classifier only

Question 4

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

Accepted Answer

SageMaker Debugger. SageMaker Debugger is the correct feature because it provides real-time monitoring and analysis of training metrics, including loss values, gradients, and weights. It can automatically detect issues like oscillating or non-converging loss by setting rules (e.g., loss not decreasing) and emit alerts or capture tensors for later analysis, directly addressing the data scientist's need to debug training instability.

Answer

SageMaker Profiler

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SageMaker Gradient Descent optimization

Answer

SageMaker Automatic Model Tuning

Question 5

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

Accepted Answer

AWS CloudFormation or AWS CDK. AWS CloudFormation and AWS CDK are infrastructure-as-code (IaC) tools that allow you to define, provision, and manage AWS resources declaratively. For this use case, they can model the entire SageMaker Studio domain, user profiles, and endpoint configuration in templates or code, ensuring reproducibility and version control. This aligns directly with the requirement to deploy ML infrastructure as code.

Answer

SageMaker Pipelines

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AWS Step Functions

Answer

SageMaker Studio

Question 6

A machine learning engineer is preparing a dataset for binary classification. The target variable has a severe class imbalance (95% negative, 5% positive). Which technique can help address this imbalance during data preparation?

Accepted Answer

SMOTE (Synthetic Minority Over-sampling Technique). SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic samples for the minority class, which directly addresses class imbalance by creating more balanced training data. L1 Regularization (Lasso) is a feature selection method, not for imbalance. StandardScaler normalizes features and does not affect class distribution. PCA is a dimensionality reduction technique and does not address imbalance.

Answer

L1 Regularization (Lasso)

Answer

StandardScaler

Answer

Principal Component Analysis (PCA)

Question 7

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

Accepted Answer

Model parallelism with SageMaker distributed. Model parallelism with SageMaker distributed is correct because a 10-billion-parameter model cannot fit into the memory of a single GPU, so the model itself must be partitioned across multiple GPUs/devices. SageMaker's distributed model parallelism library shards model layers and parameters across instances, enabling training of models that exceed single-device memory. This directly addresses the memory bottleneck that prevents scaling with data parallelism alone.

Answer

Data parallelism with Horovod

Answer

Single GPU training

Answer

Use a larger instance type without parallelism

Question 8

A data scientist needs to ingest streaming clickstream data from a website into an S3 data lake for ML training. The data must be processed in near real-time and partitioned by hour. Which AWS service combination should be used?

Accepted Answer

Amazon Kinesis Data Firehose with S3 as destination and dynamic partitioning enabled. Amazon Kinesis Data Firehose can directly stream data to S3 with dynamic partitioning, enabling automatic hourly partitioning for near real-time ingestion. Option A is correct. Option B (S3 Transfer Acceleration) is for speeding up large uploads over long distances, not for streaming or partitioning. Option C requires a custom consumer to write to S3, adding complexity without automatic partitioning. Option D uses a batch-oriented ETL job, not designed for near real-time streaming.

Answer

Amazon S3 Transfer Acceleration with direct uploads from the website

Answer

Amazon Kinesis Data Streams with a custom consumer writing to S3

Answer

AWS Glue ETL job reading from Kinesis Data Streams and writing to S3

Question 9

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

Accepted Answer

AWS Glue. AWS Glue is the most appropriate service because it is a fully managed, serverless ETL service designed specifically for data transformation tasks like converting CSV to Parquet. It automatically handles schema inference, data partitioning, and optimization for ML training workloads without requiring infrastructure management.

Answer

Amazon EMR

Answer

Amazon Athena

Answer

Amazon Redshift

Question 10

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

Accepted Answer

Concept drift in the model. Feature attribution drift monitoring compares SHAP value distributions between baseline and current data. If the input distribution of a feature is unchanged but its average SHAP value shifts significantly, the model's learned relationship between that feature and the target has changed — this is the definition of concept drift. The model itself is unchanged, but the underlying mapping from inputs to outputs in the real world has shifted, so the same input values now contribute differently to predictions.

Answer

The feature is no longer relevant to predictions

Answer

A bug in the SHAP computation

Answer

Data drift in that feature

Question 11

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

Accepted Answer

The inference container does not include the transformers library; the team should use a pre-built Hugging Face container.. The error indicates that the inference container cannot find the `transformers` library, which is required to load and run the Hugging Face model. By using a pre-built Hugging Face container from AWS, the team ensures that all necessary dependencies (like `transformers`, `tokenizers`, and `torch`) are pre-installed and compatible with the SageMaker inference environment. Option A is correct because the most likely missed step was selecting a generic container instead of the purpose-built Hugging Face container.

Answer

The IAM role does not have permissions to download additional libraries.

Answer

The model artifact was not packaged correctly; the inference script is missing.

Answer

The endpoint configuration specifies the wrong instance type.

Question 12

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

Accepted Answer

Package the model artifacts into a tar.gz file.. Option C is correct because SageMaker requires model artifacts to be packaged as a tar.gz archive (typically containing the model files and an inference script in a directory structure like model.tar.gz) before they can be deployed to a hosting container. Option D is correct because deploying a real-time inference endpoint requires creating an endpoint configuration that specifies the production variant, including the instance type (e.g., ml.m5.large) and the model to serve, which is then used to create the endpoint. Option A is incorrect because uploading training data to S3 is part of the training workflow, not a required step for deploying an already-trained PyTorch model for inference. Option B is incorrect because the SageMaker Model Registry is optional for governance and versioning; you can deploy a model directly without registering it. Option E is incorrect because a SageMaker Notebook instance is only a development tool and is not required to host a real-time inference endpoint.

Answer

Upload the training data to an S3 bucket.

Answer

Register the model in the SageMaker Model Registry.

Answer

Set up a SageMaker Notebook instance.

Question 13

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

Accepted Answer

Use SageMaker Inference Recommender to test different instance types and optimizations.. SageMaker Inference Recommender is designed specifically to automate load testing and benchmarking across various instance types and model optimizations (e.g., Elastic Inference, GPU acceleration, serialization formats). It provides latency and throughput metrics to identify the optimal configuration for reducing inference latency while maintaining accuracy, making it the correct choice for a large deep learning model with high latency.

Answer

Increase the batch size for inference.

Answer

Use a smaller instance type to reduce inference time.

Answer

Enable SageMaker Model Monitor to detect performance issues.

Question 14

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

Accepted Answer

SageMaker Experiments. SageMaker Experiments allows you to log hyperparameters as parameters. They can be viewed and compared across runs in the SageMaker Studio UI.

Answer

SageMaker Debugger

Answer

SageMaker Autopilot

Answer

SageMaker Model Monitor

Question 15

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

Accepted Answer

Data quality monitoring. Data quality monitoring in SageMaker Model Monitor compares the statistical distribution of each input feature (both numerical and categorical) against a baseline computed from the training data, detecting drift in feature distributions over time. It supports categorical and numerical columns and is the correct feature for detecting per-feature distribution changes.

Answer

Bias drift monitoring

Answer

Model quality monitoring

Answer

Feature attribution drift monitoring