- A
Automatically roll back to a previous model version upon drift detection.
Why wrong: Rolling back does not address concept drift; the previous model may also be outdated.
- B
Monitor prediction quality using ground truth labels when available.
Correct. Ground truth labels enable direct accuracy monitoring.
- C
Retrain the model on a fixed schedule regardless of performance.
Why wrong: Fixed schedules are inefficient and may not align with drift patterns.
- D
Incrementally update the model with new data using SageMaker Pipelines.
Correct. Incremental learning adapts to new patterns without full retraining.
- E
Use SageMaker Model Monitor to detect drift and trigger retraining.
Correct. Model Monitor can automate drift detection and initiate retraining pipelines.
Question 275 of 1,000
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. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 data science team detects that a deployed model's prediction accuracy is degrading over time due to concept drift. They need to implement a retraining strategy. Which THREE actions are recommended best practices for handling concept drift?
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
✓
Monitor prediction quality using ground truth labels when available.
Option B is correct because monitoring prediction quality using ground truth labels is a fundamental best practice for detecting concept drift. When ground truth labels are available, you can directly measure the model's accuracy over time, which provides the most reliable signal for drift. SageMaker Model Monitor can be configured to capture ground truth data and compare it against predictions to generate quality metrics.
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.
- ✗
Automatically roll back to a previous model version upon drift detection.
Why it's wrong here
Rolling back does not address concept drift; the previous model may also be outdated.
- ✓
Monitor prediction quality using ground truth labels when available.
Why this is correct
Correct. Ground truth labels enable direct accuracy monitoring.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Retrain the model on a fixed schedule regardless of performance.
Why it's wrong here
Fixed schedules are inefficient and may not align with drift patterns.
- ✓
Incrementally update the model with new data using SageMaker Pipelines.
Why this is correct
Correct. Incremental learning adapts to new patterns without full retraining.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use SageMaker Model Monitor to detect drift and trigger retraining.
Why this is correct
Correct. Model Monitor can automate drift detection and initiate retraining pipelines.
Clue confirmation
The clue word "best" in the question point toward this answer.
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 may confuse 'detecting drift' with 'responding to drift' and incorrectly choose automatic rollback (Option A) as a best practice, when in reality rollback is a risky operation that should be evaluated carefully, not automated blindly.
Detailed technical explanation
How to think about this question
Concept drift detection often relies on statistical tests like the Kolmogorov-Smirnov test or Population Stability Index (PSI) applied to feature distributions, but prediction quality monitoring using ground truth provides a direct accuracy-based signal. In SageMaker, you can use Model Monitor's ground truth ingestion endpoint to send actual labels, and then set up a schedule to check metrics like accuracy or F1 score against a threshold. This approach is more responsive than fixed retraining because it triggers action only when performance degrades, saving compute and ensuring the model stays relevant.
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.
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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: Monitor prediction quality using ground truth labels when available. — Option B is correct because monitoring prediction quality using ground truth labels is a fundamental best practice for detecting concept drift. When ground truth labels are available, you can directly measure the model's accuracy over time, which provides the most reliable signal for drift. SageMaker Model Monitor can be configured to capture ground truth data and compare it against predictions to generate quality metrics.
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
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.
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