- A
Retrain the model daily based on recent data without evaluation.
Why wrong: Retraining without monitoring may reduce quality if data changes.
- B
Use SageMaker Clarify for bias monitoring and feature importance drift.
Clarify can monitor bias and explainability over time.
- C
Enable SageMaker Model Monitor to capture data drift and model quality metrics.
Model Monitor provides automated monitoring for production endpoints.
- D
Set up a CloudWatch alarm on the endpoint's Invocations metric.
Why wrong: Invocations metric tracks request count, not model performance degradation.
- E
Manually compare prediction distributions weekly.
Why wrong: Manual comparison is error-prone and not automated.
Question 818 of 1,000
mediumMultiple SelectObjective-mapped
MLA-C01 Practice Question: Which TWO options are recommended best practices…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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.
Which TWO options are recommended best practices for monitoring model performance in production on SageMaker? (Choose 2.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
✓
Use SageMaker Clarify for bias monitoring and feature importance drift.
SageMaker Clarify is a recommended best practice for monitoring model performance because it provides automated bias detection and feature importance drift analysis, helping to identify when model predictions become unfair or when the relationships between features and predictions change over time. This is critical for maintaining model integrity and compliance in production.
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.
- ✗
Retrain the model daily based on recent data without evaluation.
Why it's wrong here
Retraining without monitoring may reduce quality if data changes.
- ✓
Use SageMaker Clarify for bias monitoring and feature importance drift.
Why this is correct
Clarify can monitor bias and explainability over time.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Enable SageMaker Model Monitor to capture data drift and model quality metrics.
Why this is correct
Model Monitor provides automated monitoring for production endpoints.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set up a CloudWatch alarm on the endpoint's Invocations metric.
Why it's wrong here
Invocations metric tracks request count, not model performance degradation.
- ✗
Manually compare prediction distributions weekly.
Why it's wrong here
Manual comparison is error-prone and not automated.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse operational metrics (like invocation count or latency) with model performance monitoring, leading them to select CloudWatch alarms on Invocations instead of the specialized drift and bias detection tools.
Detailed technical explanation
How to think about this question
SageMaker Model Monitor automatically captures baseline statistics from training data and compares them against live inference data to detect data drift (e.g., feature distribution shifts) and model quality degradation (e.g., accuracy drops) using statistical tests like Kolmogorov-Smirnov or chi-squared. SageMaker Clarify extends this by analyzing SHAP-based feature importance over time and detecting bias metrics such as demographic parity or equal opportunity, which is essential for regulated industries like finance or healthcare.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Use SageMaker Clarify for bias monitoring and feature importance drift. — SageMaker Clarify is a recommended best practice for monitoring model performance because it provides automated bias detection and feature importance drift analysis, helping to identify when model predictions become unfair or when the relationships between features and predictions change over time. This is critical for maintaining model integrity and compliance in production.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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