MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A hospital's ML team deploys a diagnostic model to a SageMaker real-time endpoint. Compliance requires that every inference request and response be recorded for later auditing, and the records must be retrievable months later. The team needs a low-effort way to capture this data. Which approach should they use?
⚠ Common exam trap
Test-takers frequently confuse request-level API auditing via CloudTrail or numeric monitoring via CloudWatch metrics with full payload capture, which only data capture provides.
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 data capture on the endpoint with a sampling percentage of 100 and an S3 destination, and apply an S3 lifecycle policy to retain the objects for the required period.
SageMaker data capture is the purpose-built feature for recording inference request and response payloads to Amazon S3, and a 100 percent sampling rate ensures every invocation is stored for audit. Pairing the capture destination with an S3 lifecycle policy retains records for the required period at low cost. Metrics, CloudTrail, and application-level logging do not capture full payloads as directly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Turn on AWS CloudTrail data events for the endpoint and deliver them to an S3 bucket.
Why it's wrong here
CloudTrail data events log API activity such as who called InvokeEndpoint, not the contents of the request or response payloads. It records the fact of an invocation and the caller identity, which is useful for access auditing but insufficient for reviewing model inputs and outputs. Payload-level capture must come from data capture, not CloudTrail.
- ✓
Enable data capture on the endpoint with a sampling percentage of 100 and an S3 destination, and apply an S3 lifecycle policy to retain the objects for the required period.
Why this is correct
Data capture records request and response payloads to Amazon S3 automatically, and setting the sampling percentage to 100 captures every invocation, satisfying an audit requirement for complete records. Pointing capture at an S3 prefix and applying a lifecycle policy retains the objects for the mandated duration. This requires minimal application changes and is the purpose-built feature for this need.
- ✗
Enable Amazon CloudWatch metrics on the endpoint and export the metrics to Amazon S3 for archival.
Why it's wrong here
CloudWatch metrics capture numeric aggregates such as invocation count, latency, and error rate, not the actual request and response payloads. Exporting metrics to S3 preserves statistics, not the clinical content needed for an audit. Metrics cannot substitute for payload-level records, so this does not meet the compliance requirement.
- ✗
Add application code that writes each request and response to Amazon CloudWatch Logs and set the log group retention to the audit period.
Why it's wrong here
CloudWatch Logs can hold request and response text, but it is not designed for bulk payload retention and incurs higher storage cost at volume, and the application must be modified to emit every payload. Log group retention also caps at a limited number of days, which may fall short of a months-long audit window. Data capture is the lower-effort, purpose-built alternative.
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
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.