The answer is to create a baseline job using the training dataset. This resolves the SageMaker Model Monitor baseline missing error because Model Monitor requires pre-computed statistics and constraints—collectively called a baseline—from the training data to establish a reference for detecting data quality drift. Without this baseline, the monitoring schedule cannot compare live inference data against a known good state, triggering the error. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding that Model Monitor is a two-step process: first generate a baseline with `sagemaker.model_monitor.DefaultModelMonitor.suggest_baseline`, then attach the schedule. A common trap is confusing data capture configuration (which only logs payloads) with baseline generation, or assuming the schedule itself is misconfigured. Remember the memory tip: “No baseline, no baseline—always baseline before schedule.”
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?
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
Option B is correct because Model Monitor requires a baseline (constraints and statistics) generated from the training data. The error indicates the baseline is missing. Option A enables capture but does not resolve baseline. Option C is incorrect because the schedule is fine but baseline missing. Option D is about permissions, not baseline.
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
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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.
What to study next
Got this wrong? Here's your next step.
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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 — Option B is correct because Model Monitor requires a baseline (constraints and statistics) generated from the training data. The error indicates the baseline is missing. Option A enables capture but does not resolve baseline. Option C is incorrect because the schedule is fine but baseline missing. Option D is about permissions, not baseline.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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