MLA-C01 Practice Test 38 — 15 Questions

Question 1

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

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

Accepted Answer

Use SageMaker Neo to compile the model for the target instance. SageMaker Neo compiles trained models into an optimized binary for the target hardware, applying techniques like operator fusion, memory layout optimization, and quantization. This reduces model size and inference latency while preserving accuracy, making it the correct choice for addressing high latency caused by model size.

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Switch to a compute-optimized instance type

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Reduce the batch size of inference requests

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Convert the model to ONNX format

Question 2

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

Accepted Answer

SageMaker Clarify. SageMaker Clarify is the correct service because it is specifically designed to detect bias in ML model predictions and can be configured for ongoing monitoring. It provides bias metrics (e.g., difference in positive proportion, disparate impact) and can run on a schedule to continuously evaluate predictions against demographic groups, ensuring regulatory compliance over time.

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AWS Audit Manager

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SageMaker Model Monitor

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Amazon Macie

Question 3

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

Accepted Answer

Use the SageMaker UpdateEndpoint API with a linear traffic shift from variant A to variant B over 10 minutes, and configure a CloudWatch alarm on the new variant's error rate that triggers a Lambda function to revert the traffic weights.. The SageMaker UpdateEndpoint API supports a linear traffic shift between production variants, allowing you to gradually route traffic from variant A to variant B over a specified time period (here, 10 minutes). By attaching a CloudWatch alarm on the new variant's error rate that triggers a Lambda function to revert the traffic weights, you achieve automatic rollback with minimal manual intervention when the error rate exceeds the 5% threshold.

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Use AWS Cloud Map to register the new variant and perform a slow rollout.

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Deploy variant B as a separate endpoint and use Route 53 weighted routing to shift traffic.

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Use AWS CodeDeploy with a deployment group to shift traffic and automatically roll back if CloudWatch alarms trigger.

Question 4

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

Accepted Answer

Create an EventBridge rule that listens for SageMaker Model Monitor violation events and triggers a Lambda function that starts the pipeline. It uses an EventBridge rule to listen for SageMaker Model Monitor violation events (e.g., `aws.sagemaker.model-monitoring-violation`), which then triggers a Lambda function that starts the latest approved pipeline version in SageMaker Pipelines. This creates an automated, event-driven retraining pipeline without manual intervention or scheduled polling.

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Use a scheduled EventBridge rule to run the pipeline every day

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Use SageMaker Model Monitor to update the model registry and trigger a deployment

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Configure SageMaker Model Monitor to directly invoke a Lambda function on violation

Question 5

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

Accepted Answer

Define a ConditionStep that checks the evaluation metric and fail the pipeline if the metric is below a threshold.. SageMaker Pipelines natively supports a ConditionStep that can evaluate a metric (e.g., model accuracy) and branch the pipeline execution. By configuring the ConditionStep to check if the evaluation metric falls below a threshold, you can explicitly fail the pipeline and trigger a notification (e.g., via SNS) when the condition is not met. This is the idiomatic, pipeline-native way to halt execution on evaluation failure without external dependencies.

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Use Amazon SageMaker Model Monitor to detect failures in the evaluation step.

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Create an AWS Step Function state machine that monitors the pipeline and stops it on failure.

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Configure an Amazon CloudWatch alarm on the evaluation step's execution time to stop the pipeline.

Question 6

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

Accepted Answer

Explainability reports. SageMaker Autopilot generates explainability reports, including feature importance and model insights, via the 'Explainability' feature. This provides the relationship between features and predictions.

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Data visualizations

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Model tuning results

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Model candidate definitions

Question 7

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

Accepted Answer

The IAM role does not have permission to write to the correct S3 prefix.. The IAM role attached to the SageMaker endpoint must have write permissions to the exact S3 prefix where data capture is configured. The policy shown likely grants access to a broader bucket or a different prefix, but not the specific path (e.g., s3://bucket-name/prefix/) that the endpoint's DataCaptureConfig specifies. Without s3:PutObject on that exact prefix, the captured data fails to upload silently.

Answer

The S3 bucket does not exist.

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The S3 bucket uses AWS KMS encryption and the role lacks kms:Decrypt permission.

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The IAM role does not have s3:ListBucket permission.

Question 8

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

Accepted Answer

Perform a stratified split on the target variable to ensure each set has the same fraud ratio.. A stratified split preserves the original 0.1% fraud ratio across training, validation, and test sets, which is critical for imbalanced datasets. This ensures each subset is representative of the population, allowing SageMaker's XGBoost to be evaluated fairly without data leakage. Random splits (Option A) could accidentally create a validation or test set with zero fraud cases, making evaluation meaningless.

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Randomly shuffle the entire dataset and then split into 80% training, 10% validation, 10% test.

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Use k-fold cross-validation on the entire dataset and average the results.

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Apply SMOTE to balance the dataset first, then split randomly into training, validation, and test sets.

Question 9

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

Accepted Answer

SageMaker real-time endpoint with Multi-Model Endpoint (MME) on an ml.g4dn instance. SageMaker Multi-Model Endpoint (MME) allows multiple PyTorch models to share a single GPU instance (e.g., ml.g4dn), reducing cost while meeting sub-100ms latency requirements. MME dynamically loads and unloads models into GPU memory based on traffic, enabling real-time inference with GPU acceleration without dedicating a full instance per model.

Answer

SageMaker real-time endpoint with a single model per ml.g4dn instance

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SageMaker Serverless Inference

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SageMaker Asynchronous Inference

Question 10

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

Accepted Answer

Configure SageMaker endpoints with multiple production variants and use canary deployment to shift traffic gradually.. SageMaker endpoints with multiple production variants enable canary deployment, which shifts traffic gradually from the old model to the new one. This minimizes downtime by keeping both variants active during the transition and allows easy rollback by simply redirecting all traffic back to the previous variant if issues arise.

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Deploy the model on an EC2 instance behind an Application Load Balancer and manually update the instance with the new model version.

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Deploy the model using AWS Lambda with a container image and trigger via API Gateway.

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Use SageMaker hosting with a single production variant and update the endpoint with a new model configuration each week.

Question 11

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

Accepted Answer

Increase the instance type in the pipeline step configuration. SageMaker Pipelines allows you to specify the instance type for each training step in the pipeline definition. By increasing the instance type (e.g., from ml.m5.large to ml.m5.xlarge or a memory-optimized instance like ml.r5.large), you allocate more memory to the training container without modifying the training script or algorithm. This directly resolves the out-of-memory error while preserving the existing code.

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Modify the training algorithm to use less memory

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Reduce the batch size in the training script

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Enable managed spot training

Question 12

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

Accepted Answer

CloudWatch Alarm → SNS topic → Lambda function. CloudWatch Alarms monitor metrics and trigger actions. SNS can notify and invoke Lambda for automated remediation. EventBridge is used for scheduled events, not direct alarm actions.

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EventBridge rule to trigger Lambda

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CloudWatch Logs subscription filter → Lambda function

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SageMaker Model Monitor → Lambda function

Question 13

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

Accepted Answer

Configure the endpoint to route a percentage of traffic to each variant using initial variant weight. SageMaker endpoints use `initial variant weight` to distribute traffic among production variants. By setting this weight, you can route a specific percentage of inference requests to each model version, enabling A/B testing without changing the endpoint configuration.

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Enable shadow variants to capture traffic for the new model without affecting users

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Set up a batch transform job to compare performance offline

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Register both models in SageMaker Model Registry

Question 14

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

Accepted Answer

Configure an auto-scaling policy with target tracking based on the Invocations metric. Target tracking scaling policies automatically adjust instance count based on a metric like invocation count or CPU utilization. Step scaling can also be used but target tracking is simpler and more cost-effective for predictable patterns.

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Use a single large instance type that can handle peak traffic at all times

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Set up a scheduled scaling policy that manually adjusts instances at fixed times

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Use SageMaker Inference Recommender to find the optimal instance type and then manually set instance count

Question 15

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

Accepted Answer

Use SageMaker Model Monitor in each account and publish custom metrics to a central CloudWatch account using cross-account observability.. It uses SageMaker Model Monitor in each account to detect data drift and model degradation locally, then publishes custom metrics to a central CloudWatch account via cross-account observability. This approach centralizes monitoring without moving raw inference data across accounts, satisfying the compliance requirement of not exposing data.

Answer

Establish VPC peering between accounts and call the endpoints from a central monitoring service.

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Replicate the inference data to a central S3 bucket in the management account using cross-account replication, then run Model Monitor centrally.

Answer

Create a shared SageMaker Model Registry across accounts and aggregate monitoring.