Question 786 of 1,000
ML Solution Monitoring, Maintenance and SecurityhardMultiple 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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.

Exhibit

2024-01-01 12:00:00 ERROR - Baseline configuration is missing for data quality monitoring. Unable to evaluate constraints.
2024-01-01 12:00:01 ERROR - Monitoring job failed.

Refer to the exhibit. A team receives an error when running a SageMaker Model Monitor schedule for data quality. What should they do to resolve this issue?

Exhibit

2024-01-01 12:00:00 ERROR - Baseline configuration is missing for data quality monitoring. Unable to evaluate constraints.
2024-01-01 12:00:01 ERROR - Monitoring job failed.

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

Create a baseline job using the training dataset

The error occurs because SageMaker Model Monitor requires a baseline to compare against live data. Without a baseline job created from the training dataset, the monitoring schedule fails. Option D resolves this by generating the necessary statistics and constraints that define expected data quality.

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.

  • Update the IAM role to allow S3 access

    Why it's wrong here

    The error is about missing baseline, not permissions.

  • Restart the monitoring schedule

    Why it's wrong here

    Restarting will encounter the same error.

  • Enable data capture on the endpoint

    Why it's wrong here

    Data capture is needed for monitoring but does not provide the baseline.

  • Create a baseline job using the training dataset

    Why this is correct

    A baseline must be generated from training data to compare inference data against.

    Related concept

    Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often assume the error is a permissions or configuration issue (S3 access or data capture), but the root cause is the mandatory prerequisite of a baseline job before a monitoring schedule can run.

Detailed technical explanation

How to think about this question

SageMaker Model Monitor uses a baseline job to compute statistics (e.g., mean, stddev) and constraints (e.g., min, max) from the training dataset, stored in S3 under the monitoring output path. The monitoring schedule then compares live inference data against these constraints using a built-in container that runs statistical tests like z-score or distribution distance. Without this baseline, the schedule has no reference to detect drift, causing the error.

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.

Visual reference

Client Recursive Resolver Root DNS (13 root servers) TLD DNS (.com, .org, …) Authoritative example.com query IP addr answer

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: Create a baseline job using the training dataset — The error occurs because SageMaker Model Monitor requires a baseline to compare against live data. Without a baseline job created from the training dataset, the monitoring schedule fails. Option D resolves this by generating the necessary statistics and constraints that define expected data quality.

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.