- A
Deploy an A/B test to compare the current model with a baseline.
Why wrong: A/B testing compares model versions but does not inherently detect drift.
- B
Use SageMaker Model Monitor to track prediction distribution and trigger retraining.
Correct. Model Monitor continuously checks for drift and can initiate automated retraining.
- C
Manually review model accuracy each month.
Why wrong: Manual review is slow and not automated, missing early drift detection.
- D
Set up a weekly batch transform job to compute accuracy against historical data.
Why wrong: Weekly batch is reactive and may not catch drift quickly; also accuracy may not be available without ground truth.
- E
Increase the number of instances for the endpoint.
Why wrong: Scaling instances does not address model degradation.
Quick Answer
The answer is to use SageMaker Model Monitor to track prediction distribution and trigger retraining. This is the most effective approach because concept drift, where the statistical properties of the target variable change due to shifts in customer behavior (like a marketing campaign), directly alters the model’s prediction distribution. SageMaker Model Monitor detects this by continuously comparing live inference data against a baseline, alerting you when the distribution deviates beyond a threshold, and can automatically invoke a retraining pipeline to adapt the model. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of monitoring strategies versus retraining triggers—a common trap is choosing manual retraining or only tracking data quality (e.g., missing values), which misses the core drift signal. Remember the mnemonic: “Drift in distribution demands detection and retraining.”
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.
An e-commerce company uses a machine learning model to predict customer churn. They notice that the model's performance degrades after a major marketing campaign changes customer behavior. Which approach is MOST effective to detect and respond to this type of concept drift?
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 Model Monitor to track prediction distribution and trigger retraining.
SageMaker Model Monitor can automatically detect drift in prediction distributions and trigger retraining pipelines.
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.
- ✗
Deploy an A/B test to compare the current model with a baseline.
Why it's wrong here
A/B testing compares model versions but does not inherently detect drift.
- ✓
Use SageMaker Model Monitor to track prediction distribution and trigger retraining.
Why this is correct
Correct. Model Monitor continuously checks for drift and can initiate automated retraining.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Manually review model accuracy each month.
Why it's wrong here
Manual review is slow and not automated, missing early drift detection.
- ✗
Set up a weekly batch transform job to compute accuracy against historical data.
Why it's wrong here
Weekly batch is reactive and may not catch drift quickly; also accuracy may not be available without ground truth.
- ✗
Increase the number of instances for the endpoint.
Why it's wrong here
Scaling instances does not address model degradation.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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 — study guide chapter
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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: Use SageMaker Model Monitor to track prediction distribution and trigger retraining. — SageMaker Model Monitor can automatically detect drift in prediction distributions and trigger retraining pipelines.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Refer to the exhibit. A data scientist reviews the CloudWatch Logs from an Amazon SageMaker real-time endpoint. What is the MOST likely root cause of the NaN output?
easy- A.The model weights became corrupted due to a disk write error.
- ✓ B.The input data contains out-of-range values not seen during training, causing the model to output NaN.
- C.The endpoint is overloaded and returning a default NaN response.
- D.The model artifact failed to load correctly, resulting in NaN weights.
Why B: Option B is correct. The unusual input value (-9999.0) suggests data drift or out-of-range input that could cause the model to produce NaN. Option A is wrong because there is no memory error. Option C is wrong because no latency issue is indicated. Option D is wrong because the log shows the error during inference, not during model loading.
Last reviewed: Jun 23, 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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