MLA-C01 Deployment and Orchestration of ML Workflows Practice Question

A team needs to deploy a model that has compliance requirements to log all inference requests and responses for auditing. The model will be served using a real-time endpoint. How can they achieve this without custom code?

⚠ Common exam trap

Test-takers frequently confuse CloudTrail (which logs API calls) with Data Capture (which logs payloads), or they assume Debugger can be repurposed for inference logging, but Debugger only works during training.

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

✓

Enable SageMaker Data Capture on the endpoint

SageMaker Data Capture is the native, no-code feature that automatically logs inference requests and responses for real-time endpoints. It captures payload data to an S3 bucket without requiring any custom code, directly meeting the compliance requirement for audit logging.

Answer analysis

Option-by-option breakdown

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

✓
Enable SageMaker Data Capture on the endpoint
Why this is correct
SageMaker Data Capture on a real-time endpoint automatically logs inference requests and responses to Amazon S3, satisfying the audit requirement without custom code. It captures input and output payloads at the endpoint level, so compliance logging happens transparently for every invocation.
✗
Add a custom Lambda function using a container
Why it's wrong here
Adding a custom Lambda function with a container introduces custom code, which directly violates the question’s explicit constraint to achieve logging “without custom code.” This option is tempting because Lambda container support is often used to package custom runtimes or dependencies for inference preprocessing; however, the requirement demands a no-code solution, so the correct approach would use an AWS service like CloudTrail or an endpoint-level logging configuration that captures requests and responses automatically, not a developer-written function.
✗
Use SageMaker Debugger to monitor inference
Why it's wrong here
Debugger captures training-job tensors and metrics for model insight, not inference request and response payloads at a real-time endpoint. It is tempting because Debugger is a native SageMaker monitoring feature, and it would be the right tool for diagnosing training convergence, vanishing gradients or resource bottlenecks during model fitting.
✗
Enable CloudTrail for the endpoint
Why it's wrong here
CloudTrail records AWS API control-plane calls, not the request and response payloads flowing through a SageMaker real-time endpoint, so it cannot satisfy the audit requirement. It is tempting because CloudTrail is the default answer for AWS logging, and it would be right for auditing who created, updated or deleted the endpoint itself.

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 Deployment and Orchestration of ML Workflows Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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