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ML Solution Monitoring, Maintenance, and Security →hardMultiple ChoiceObjective-mapped

MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security

This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance, and security. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

A team is using SageMaker Clarify to detect bias drift in a deployed model's predictions. They run weekly bias monitoring jobs. The team wants to be notified when the bias metric for a sensitive feature exceeds a threshold. What is the most efficient method to achieve this?

A
Configure the Clarify monitoring job to send results to an SNS topic directly
Why wrong: Clarify does not natively send to SNS; it writes reports to S3.
B
After each Clarify job, run a custom Lambda that parses the report and publishes a custom CloudWatch metric; create an alarm on that metric
This approach translates bias metrics into CloudWatch metrics for alarming.
C
Manually review the bias report in SageMaker Studio each week
Why wrong: Manual review is not automated or efficient.
D
Use SageMaker Model Monitor - Bias Drift Monitor which automatically creates CloudWatch metrics
Why wrong: Model Monitor's bias drift monitor does not automatically create CloudWatch metrics; it writes to S3.

Full breakdown with real-world context →

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

✓

After each Clarify job, run a custom Lambda that parses the report and publishes a custom CloudWatch metric; create an alarm on that metric

Option B is correct because SageMaker Clarify bias monitoring jobs output a JSON report to S3 but do not natively publish CloudWatch metrics. By using a custom Lambda to parse the report and publish a custom CloudWatch metric, you can then create a CloudWatch alarm that triggers notifications when the bias metric exceeds a threshold. This is the most efficient automated method because it leverages CloudWatch's native alarm and notification capabilities without manual intervention.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

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

✗
Configure the Clarify monitoring job to send results to an SNS topic directly
Why it's wrong here
Clarify does not natively send to SNS; it writes reports to S3.
✓
After each Clarify job, run a custom Lambda that parses the report and publishes a custom CloudWatch metric; create an alarm on that metric
Why this is correct
This approach translates bias metrics into CloudWatch metrics for alarming.
Related concept
Read the scenario before looking for a memorised answer.
✗
Manually review the bias report in SageMaker Studio each week
Why it's wrong here
Manual review is not automated or efficient.
✗
Use SageMaker Model Monitor - Bias Drift Monitor which automatically creates CloudWatch metrics
Why it's wrong here
Model Monitor's bias drift monitor does not automatically create CloudWatch metrics; it writes to S3.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates may confuse SageMaker Clarify's bias monitoring with SageMaker Model Monitor's built-in bias drift capabilities, assuming that Clarify automatically creates CloudWatch metrics or integrates with SNS, when in fact it only outputs to S3 and requires a custom pipeline for metric extraction and alerting.

Detailed technical explanation

How to think about this question

SageMaker Clarify bias metrics (e.g., Difference in Positive Proportions, Conditional Demographic Disparity) are computed per job and stored as a JSON file in S3. A Lambda function can parse this JSON, extract the relevant metric value, and publish it as a custom CloudWatch metric using the PutMetricData API. A CloudWatch alarm on that metric can then trigger an SNS notification when the threshold is breached. This pattern is common for custom monitoring where the native service does not expose metrics to CloudWatch automatically.

KKey Concepts to Remember

Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.

TExam Day Tips

Watch for words such as best, first, most likely and least administrative effort.
Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.

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

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this MLA-C01 question test?

ML Solution Monitoring, Maintenance, and Security — This question tests ML Solution Monitoring, Maintenance, and Security — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: After each Clarify job, run a custom Lambda that parses the report and publishes a custom CloudWatch metric; create an alarm on that metric — Option B is correct because SageMaker Clarify bias monitoring jobs output a JSON report to S3 but do not natively publish CloudWatch metrics. By using a custom Lambda to parse the report and publish a custom CloudWatch metric, you can then create a CloudWatch alarm that triggers notifications when the bias metric exceeds a threshold. This is the most efficient automated method because it leverages CloudWatch's native alarm and notification capabilities without manual intervention.

What should I do if I get this MLA-C01 question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Last reviewed: Jul 4, 2026

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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.