MLA-C01 Practice Test 8 — 10 Questions

Question 1

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

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

Accepted Answer

Canary deployment with CloudWatch alarms. Canary deployment with CloudWatch alarms is correct because SageMaker production variants allow you to split traffic between the old and new model (e.g., 90/10), and CloudWatch alarms can monitor the new variant's error rate and trigger automatic rollback when it exceeds 5%. This pattern is purpose-built for gradual, monitored traffic shifting with automated safety nets.

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A/B testing with traffic splitting

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Blue/green deployment

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Shadow testing

Question 2

A machine learning team is developing a deep learning model for image classification. They observe that the training loss decreases rapidly but the validation loss starts increasing after a few epochs. Which strategy should they implement to address this issue?

Accepted Answer

Apply dropout regularization. The scenario describes overfitting, where the model memorizes training data but fails to generalize to validation data. Dropout regularization randomly deactivates a fraction of neurons during training, which prevents co-adaptation and forces the network to learn more robust features, thereby reducing the validation loss increase.

Answer

Increase the batch size

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Add more convolutional layers

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Increase the learning rate

Question 3

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

Accepted Answer

Walk-forward validation (time-series split). Walk-forward validation (time-series split) preserves the temporal order of observations by training on past data and testing on subsequent future periods, which mirrors how a forecasting model is actually used in production. Because daily sales data has trend, seasonality, and autocorrelation, random or stratified splits leak future information into training and produce optimistically biased metrics. Walk-forward validation with a 30-day horizon directly simulates predicting the next 30 days from prior history.

Answer

Leave-one-out cross-validation

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Random 80/20 train-test split

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Stratified k-fold cross-validation

Question 4

Refer to the exhibit. A data scientist tries to deploy a model from an S3 bucket encrypted with SSE-KMS. What should the administrator do to resolve this?

Accepted Answer

Add kms:Decrypt permission to the SageMaker execution role for the KMS key.. When a model artifact is stored in an S3 bucket encrypted with SSE-KMS, the SageMaker execution role must have the kms:Decrypt permission for the specific KMS key to allow SageMaker to decrypt the artifact during model deployment. Without this permission, the deployment fails because SageMaker cannot read the encrypted object. Option B correctly adds the required KMS permission to the execution role.

Answer

Change the model artifact encryption to SSE-S3.

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Re-upload the model artifact without encryption.

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Attach the AWS managed policy 'AmazonSageMakerFullAccess' to the role.

Question 5

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

Accepted Answer

Asynchronous Inference. SageMaker Asynchronous Inference is purpose-built for large payloads (up to 1 GB) and long processing times (up to 15 minutes), queuing requests and writing results to S3. It natively supports SNS notifications on completion or failure, matching the requirement exactly. This makes it the correct fit for asynchronous, large-payload processing with S3 output and SNS alerts.

Answer

Batch Transform

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Real-time endpoint

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Serverless Inference

Question 6

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

Accepted Answer

AWS Step Functions. Amazon EventBridge (C) is the correct event-routing service to detect the arrival of new data (e.g., an S3 PutObject event) and trigger the pipeline, since it can match events and invoke targets such as Step Functions. AWS Step Functions (A) is correct because it orchestrates the multi-step ML retraining workflow, including the conditional logic that checks whether model performance has degraded below the threshold before proceeding. AWS Lambda (B) is correct because it provides the serverless compute to evaluate the performance metric against the threshold and return a decision that Step Functions uses in its Choice state to branch into retraining or stop. Amazon CloudWatch Logs (D) is not correct because it is a logging/monitoring service, not an event trigger or orchestration component for this pipeline. SageMaker Model Registry (E) is not correct because, while it tracks model versions and approval status, it does not itself trigger retraining based on incoming data or performance degradation.

Answer

Amazon CloudWatch Logs

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SageMaker Model Registry

Question 7

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

Accepted Answer

SageMaker ML Lineage Tracking. SageMaker ML Lineage Tracking automatically captures relationships among datasets, training jobs, model artifacts, and endpoints as the pipeline runs, producing a queryable lineage graph for reproducibility and audit. It records entities and associations without custom instrumentation, which is exactly what the team needs. SageMaker Experiments tracks runs and metrics but does not build the full artifact relationship graph.

Answer

Amazon S3 object versioning

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SageMaker Experiments

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SageMaker Model Registry

Question 8

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

Accepted Answer

Target tracking scaling. Target tracking scaling is correct because it adjusts capacity to keep a specified metric, such as CPU utilization, at a target value. Application Auto Scaling for SageMaker endpoints supports target tracking, which automatically creates and manages the necessary CloudWatch alarms and scaling policies. This is the recommended approach for maintaining a utilization target without manually defining step adjustments.

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Step scaling

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Predictive scaling

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Simple scaling

Question 9

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

Accepted Answer

CloudWatch Alarm + SNS. CloudWatch Alarms evaluate metrics over specified periods and can trigger SNS notifications when a threshold is breached for a defined number of consecutive periods. SageMaker endpoints automatically publish inference latency metrics to CloudWatch, so an alarm on the latency metric with a 500 ms threshold and 2 evaluation periods, wired to an SNS topic, delivers exactly the required notification.

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SageMaker Model Monitor + SNS

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EventBridge + Lambda

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SageMaker Clarify + SNS

Question 10

A company uses Amazon SageMaker Data Wrangler for data preparation. The data science team wants to automatically detect potential bias in their dataset before training a model. Which feature of Data Wrangler should they use?

Accepted Answer

Built-in bias detection with SageMaker Clarify. Amazon SageMaker Data Wrangler includes built-in bias detection powered by SageMaker Clarify. This feature allows data scientists to analyze datasets for potential bias before model training, directly within the Data Wrangler visual interface. Option D is correct because it is the only option that provides automated bias detection at the data preparation stage.

Answer

Feature importance analysis

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Amazon Rekognition

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Model Monitor