MLA-C01 Practice Question: Which TWO of the following are best practices for…

Which TWO of the following are best practices for deploying machine learning models on SageMaker? (Select TWO.)

⚠ Common exam trap

The trap is selecting cost-saving or manual options (disabling logs, manual tagging) that sound pragmatic but violate observability and governance best practices — the exam rewards automated, auditable, and versioned deployment patterns.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Use separate production and staging endpoints to test new models before full rollout

Option B is correct because using separate staging and production endpoints lets you validate a new model version against real traffic patterns and roll back safely before promoting it to the production endpoint, which is a core SageMaker deployment best practice. Option E is correct because SageMaker Data Capture on endpoints logs request/response payloads to Amazon S3, enabling auditing, model monitoring, and drift detection via Model Monitor. Option A is wrong because model artifacts should be stored in Amazon S3 and loaded by the endpoint, not on EBS volumes attached to instances. Option C is wrong because SageMaker Model Registry does exist and is the recommended way to version and track models, not manual tags. Option D is wrong because disabling CloudWatch Logs removes observability and is not a best practice for production inference.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Store model artifacts in Amazon EBS volumes attached to the endpoint instances
Why it's wrong here
Endpoint instances are ephemeral and stateless; EBS volume contents do not persist across instance replacement or scaling events, so artifacts would vanish on redeployment. Model artifacts belong in Amazon S3, which SageMaker pulls at endpoint creation. EBS suits instance-local scratch data, not durable model storage.
✓
Use separate production and staging endpoints to test new models before full rollout
Why this is correct
Staging endpoints let you validate a new model version against test traffic before shifting production traffic, isolating deployment risk. This satisfies the best-practise requirement for safe rollout, since production remains unaffected until the staged model is verified.
✗
Manually track model versions using tags because SageMaker Model Registry is not available for deployment
Why it's wrong here
SageMaker Model Registry is available and provides versioned model groups, approval status and lineage; manual tagging cannot enforce approval workflows or deployment gates. Tags suit cost allocation and resource organisation, not lifecycle governance, so this would only fit where no registry or CI/CD integration exists.
✗
Disable CloudWatch Logs to reduce costs during inference
Why it's wrong here
Disabling CloudWatch Logs removes the endpoint metrics and error logs needed to monitor inference health, latency and failures, so it is not a best practice. It tempts as a cost-saving measure, but SageMaker monitoring, debugging and autoscaling decisions depend on those logs and metrics.
✓
Enable data capture on endpoints to log predictions for auditing and model monitoring
Why this is correct
Data capture logs request and response payloads from the endpoint to S3, providing the prediction records needed for auditing and for detecting drift or bias during model monitoring. This directly satisfies the stem's requirement for ongoing oversight of deployed models rather than one-off validation before deployment.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

Go deeper

MLA-C01 Practice Question: Which TWO of the following are best practices for…

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

Related to this question