MLA-C01 Practice Test 27 — 15 Questions

Question 1

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

Accepted Answer

Use early stopping with validation loss monitoring. Early stopping is the correct action because the validation loss increasing after 10 epochs while training loss continues to decrease is a classic sign of overfitting. By monitoring validation loss and halting training when it stops improving (e.g., using a patience parameter), the engineer prevents the model from memorizing noise in the training data, thereby improving generalization. SageMaker's built-in training job features or the `EarlyStopping` callback in frameworks like TensorFlow or PyTorch can implement this directly.

Answer

Add more layers to the model

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Increase the learning rate

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Decrease the batch size

Question 2

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

Accepted Answer

Amazon SageMaker Pipelines and Amazon SageMaker Model Registry. Option D is correct because Amazon SageMaker Pipelines provides the orchestration for the automated retraining workflow (including weekly scheduling and conditional logic), while SageMaker Model Registry enables versioning, approval, and promotion of models based on performance thresholds. Together, they allow the engineer to define a pipeline that trains a new model, evaluates it against the current production model, and only registers it for deployment if it meets the predefined criteria.

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Amazon SageMaker Debugger and Amazon SageMaker Clarify

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Amazon SageMaker Model Monitor and Amazon SageMaker Ground Truth

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Amazon SageMaker Autopilot and Amazon SageMaker Experiments

Question 3

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

Accepted Answer

Use a scikit-learn container in SageMaker to create a preprocessing step that imputes missing values and include it in the inference pipeline.. Option D is correct because it uses a scikit-learn container within SageMaker to create a preprocessing step that imputes missing values, then includes that step in the inference pipeline. This ensures the same imputation logic (e.g., mean, median, or custom strategy) is applied consistently during both training and inference, preventing data drift and maintaining reproducibility. SageMaker Pipelines or the built-in scikit-learn container allow the preprocessing to be serialized as part of the model artifact, so inference requests automatically undergo the same transformation.

Answer

Impute missing values in a separate Jupyter notebook and save the cleaned data.

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Use SageMaker Autopilot to automatically handle missing values.

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Drop all rows with missing values before training.

Question 4

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

Accepted Answer

Configure the S3 bucket to send events to Amazon EventBridge, and create an EventBridge rule that targets the Step Functions state machine.. Option D is correct because Amazon S3 can send event notifications directly to Amazon EventBridge, and EventBridge rules can target AWS Step Functions state machines as a target. This provides a fully managed, serverless integration that allows the Step Functions workflow to be triggered automatically whenever new data arrives in the S3 bucket, without needing intermediate polling or custom code.

Answer

Configure the S3 bucket to send an event notification directly to the Step Functions state machine.

Answer

Use S3 event notifications to send a message to an Amazon SQS queue, and have a Lambda function poll the queue to start the execution.

Answer

Use a CloudWatch Logs metric filter to trigger the Step Functions execution.

Question 5

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

Accepted Answer

Create a VPC with S3 VPC endpoints, attach a VPC-only policy to the SageMaker execution role, and enable KMS encryption for training jobs.. Option A is correct because it ensures that data in transit between SageMaker and S3 stays within the AWS network and is encrypted. By creating a VPC with S3 VPC endpoints, traffic uses AWS private IPs and never traverses the public internet. Attaching a VPC-only policy to the SageMaker execution role restricts the training job to only use VPC endpoints, and enabling KMS encryption for the training job ensures data is encrypted in transit (via TLS) and at rest.

Answer

Use an S3 bucket with SSE-S3 encryption and restrict bucket access to a VPC.

Answer

Enable default encryption on the S3 bucket and use HTTPS for all SageMaker endpoints.

Answer

Create a VPC with a NAT gateway, and configure SageMaker to use the VPC and enforce HTTPS.

Question 6

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

Accepted Answer

Use dynamic batching with a custom inference script that groups requests by sequence length.. Option C is correct because dynamic batching with a custom inference script that groups requests by sequence length minimizes padding overhead and maximizes hardware utilization. By batching similar-length sequences together, the model avoids excessive padding to the maximum length in the batch, which reduces wasted computation and latency. This approach is particularly effective for variable-length NLP inputs on SageMaker, where the inference container can be customized to implement the grouping logic.

Answer

Reduce the model size by pruning and quantization.

Answer

Pad all input sequences to the maximum length in the batch.

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Process each request individually to avoid padding overhead.

Question 7

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

Accepted Answer

Convert the CSV file to the format of a single file with one instance per line.. Option C is correct because BlazingText expects input data in a single file where each line represents one training instance. This is a specific requirement of the algorithm's input format, not a general SageMaker practice. The CSV file must be converted to this line-per-instance format for BlazingText to process it correctly.

Answer

Convert the text to one-hot encoded vectors.

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Tokenize and remove stop words from the text.

Answer

Upload the data to an Amazon SageMaker notebook instance.

Question 8

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

Accepted Answer

Use the SageMaker distributed data parallelism library. The SageMaker distributed data parallelism library (option B) automatically partitions training data and synchronizes gradients across multiple GPUs, reducing training time without manual data splitting. Configuring the SageMaker estimator with a distribution parameter (option D) enables this library by specifying the distribution strategy (e.g., 'torch_distributed' or 'tensorflow_distributed'), which is required to activate distributed training.

Answer

Reduce the number of epochs to match the number of GPUs

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Manually split the training data into shards and upload to S3

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Set the instance count to 1 with a multi-GPU instance

Question 9

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

Accepted Answer

Use the F1 score as the evaluation metric and adjust the classification threshold based on the precision-recall curve.. Option D is correct because in a severely imbalanced dataset (95% negative, 5% positive), accuracy is misleading. The F1 score balances precision and recall, and adjusting the classification threshold based on the precision-recall curve allows the model to prioritize recall for the minority class, directly improving detection of positive cases. This approach is recommended in SageMaker when using built-in algorithms or custom models with imbalanced data.

Answer

Switch to a logistic regression model with balanced class weights.

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Use accuracy as the evaluation metric and retrain the model.

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Apply SMOTE (Synthetic Minority Over-sampling Technique) to the training data.

Question 10

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

Accepted Answer

Enable early stopping for the tuning job. Option D is correct because enabling early stopping in SageMaker Automatic Model Tuning (AMT) terminates poorly performing training jobs before they complete, which reduces wasted compute time and speeds up the tuning process. This is especially effective when using Bayesian optimization, as it allows the algorithm to focus on promising hyperparameter regions and avoid evaluating combinations that are unlikely to improve the objective metric.

Answer

Switch the strategy from Bayesian to random search

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Use a smaller instance type for each training job

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Increase the maximum number of training jobs

Question 11

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

Accepted Answer

Amazon SageMaker Debugger. Amazon SageMaker Debugger is the correct choice because it provides real-time monitoring and visualization of training metrics, including loss values, gradients, and weights. The repeated 'loss not decreasing' warnings indicate a training issue (e.g., vanishing gradients or learning rate problems), and Debugger can capture these tensors and emit alerts or trigger actions (like stopping the job) via built-in or custom rules. It also integrates with SageMaker Studio for interactive visualization of the training progress.

Answer

Amazon SageMaker Clarify

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Amazon SageMaker Experiments

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Amazon SageMaker Model Monitor

Question 12

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

Accepted Answer

A CloudFormation template to deploy the endpoint infrastructure, enabling rollback via stack update.. Option B is correct because using a CloudFormation template to deploy the SageMaker endpoint infrastructure enables rollback via stack update. If a deployment fails, CloudFormation can automatically roll back the stack to the previous known good state, ensuring infrastructure consistency and reducing downtime.

Answer

A step to register the model in SageMaker Model Registry.

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A step to run SageMaker Debugger to monitor training.

Question 13

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

Accepted Answer

Amazon SageMaker Model Registry. Amazon SageMaker Model Registry is correct because it provides a centralized catalog for managing, versioning, and approving ML models. It enables automated deployment by triggering CI/CD pipelines when a model version is approved, and supports automatic rollback by allowing you to revert to a previous approved version if performance degrades, as detected by monitoring metrics.

Answer

Amazon SageMaker Ground Truth

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AWS CloudTrail

Question 14

A company is deploying a real-time inference endpoint for a natural language processing model using Amazon SageMaker. The model requires GPU acceleration and must handle variable traffic patterns, including sudden spikes. The team wants to minimize costs while maintaining low latency during spikes. Which endpoint configuration strategy should they use?

Accepted Answer

Use a multi-model endpoint on a GPU instance with Auto Scaling based on invocation count.. Option D is correct because a multi-model endpoint on a GPU instance with Auto Scaling based on invocation count allows multiple models to share a single GPU, maximizing utilization and reducing cost. Auto Scaling based on invocation count dynamically adjusts the number of instances to handle traffic spikes while maintaining low latency, as it scales out quickly when the invocation count exceeds a threshold.

Answer

Use a single large GPU instance with provisioned concurrency.

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Use a serverless endpoint with GPU support.

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Use a single GPU instance in multiple Availability Zones with an Application Load Balancer.

Question 15

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

Accepted Answer

Package the model artifacts and use the SageMaker built-in scikit-learn container for inference. Option D is correct because SageMaker provides a pre-built, optimized Docker container for scikit-learn that supports inference. By packaging the model artifacts (e.g., a joblib or pickle file) and deploying them using the built-in container, the team avoids the overhead of custom container creation while ensuring compatibility with SageMaker's hosting infrastructure, including automatic scaling and load balancing.

Answer

Create a Jupyter notebook that loads the model and runs predictions on the SageMaker notebook instance

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Create a custom Docker container with scikit-learn and deploy it on SageMaker

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Launch a SageMaker training job with the model and use the training instance as an endpoint