- A
Retrain the model using the latest training data from the last month and deploy a new endpoint to replace the current one.
Why wrong: Retraining without confirming drift is premature; the issue may be a data ingestion problem rather than drift.
- B
Use the Model Monitor's built-in baseline drift analysis on the captured inference data stored in Amazon S3, and run an Amazon CloudWatch Logs Insights query on the endpoint logs to identify specific input features that have changed distribution.
This directly analyzes for data drift using the already-captured data and logs, enabling precise diagnosis.
- C
Increase the endpoint's instance count and enable auto-scaling to handle the increased latency and errors.
Why wrong: Scaling addresses capacity but does not fix the underlying data quality issue and may not reduce errors from conflicting predictions.
- D
Enable SageMaker Debugger on the endpoint to capture inference tensors and compare them to training tensor distributions.
Why wrong: Debugger is for training, not inference; it cannot run on an existing endpoint without redeployment.
Quick Answer
The answer is to use Model Monitor’s built-in baseline drift analysis on the captured inference data in S3, paired with a CloudWatch Logs Insights query on the endpoint logs to pinpoint which input features have shifted. This is correct because detecting data drift causing SageMaker endpoint latency and errors requires comparing live inference data against a precomputed baseline—Model Monitor’s drift analysis directly identifies whether the distribution has changed, while the log query reveals the specific features responsible for the longer processing times and timeouts. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your ability to distinguish between monitoring tools: SageMaker Debugger is for training-time debugging, not inference drift, and simply scaling the endpoint or retraining without diagnosis addresses symptoms, not root cause. A common trap is assuming Model Monitor failures appear in its schedule status—they don’t; drift is reported via CloudWatch metrics and S3 analysis. Memory tip: “Baseline + Logs = Drift diagnosis; Debugger is for training, not inference.”
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.
A financial services company has deployed a machine learning model using Amazon SageMaker to predict loan default risk. The model is hosted on a real-time endpoint and uses a SageMaker Model Monitor schedule to check for data drift every hour. The monitoring schedule has been running for a month without issues. Starting last week, the data science team noticed that the endpoint's invocation latency has increased by 300% and error rates have spiked to 5% from a baseline of 0.1%. The team suspects the model is receiving out-of-distribution data that is causing longer processing times and occasional timeouts. They have active CloudWatch alarms on latency and error rates but no alarms on data drift. The Model Monitor schedule shows no failures in its status. The team needs to quickly identify whether data drift is the root cause and take corrective action. Which course of action should the team take to diagnose and address the issue?
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 the Model Monitor's built-in baseline drift analysis on the captured inference data stored in Amazon S3, and run an Amazon CloudWatch Logs Insights query on the endpoint logs to identify specific input features that have changed distribution.
Option A is correct because analyzing the captured inference data against the baseline using Model Monitor's built-in drift analysis will directly determine if data drift exists, and the log insights query can pinpoint which features have changed. Option B is wrong because SageMaker Debugger is for training-time debugging, not for inference data drift. Option C is wrong because retraining without diagnosing wastes resources if drift is not the cause. Option D is wrong because increasing endpoint capacity addresses symptoms but not the root cause, and may not fix errors due to out-of-distribution data.
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 using the latest training data from the last month and deploy a new endpoint to replace the current one.
Why it's wrong here
Retraining without confirming drift is premature; the issue may be a data ingestion problem rather than drift.
- ✓
Use the Model Monitor's built-in baseline drift analysis on the captured inference data stored in Amazon S3, and run an Amazon CloudWatch Logs Insights query on the endpoint logs to identify specific input features that have changed distribution.
Why this is correct
This directly analyzes for data drift using the already-captured data and logs, enabling precise diagnosis.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the endpoint's instance count and enable auto-scaling to handle the increased latency and errors.
Why it's wrong here
Scaling addresses capacity but does not fix the underlying data quality issue and may not reduce errors from conflicting predictions.
- ✗
Enable SageMaker Debugger on the endpoint to capture inference tensors and compare them to training tensor distributions.
Why it's wrong here
Debugger is for training, not inference; it cannot run on an existing endpoint without redeployment.
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.
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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 the Model Monitor's built-in baseline drift analysis on the captured inference data stored in Amazon S3, and run an Amazon CloudWatch Logs Insights query on the endpoint logs to identify specific input features that have changed distribution. — Option A is correct because analyzing the captured inference data against the baseline using Model Monitor's built-in drift analysis will directly determine if data drift exists, and the log insights query can pinpoint which features have changed. Option B is wrong because SageMaker Debugger is for training-time debugging, not for inference data drift. Option C is wrong because retraining without diagnosing wastes resources if drift is not the cause. Option D is wrong because increasing endpoint capacity addresses symptoms but not the root cause, and may not fix errors due to out-of-distribution data.
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. A machine learning team at a retail company has deployed a product recommendation model using Amazon SageMaker. The model is updated weekly with new data. Recently, the team noticed that the model's accuracy on a holdout evaluation set has been declining over the past month. The data pipeline that feeds the training job has not changed. The team suspects data drift. They have SageMaker Model Monitor enabled on the inference endpoint and have set up Amazon CloudWatch metrics for feature distribution distances. Upon reviewing the CloudWatch dashboards, they see that the feature distribution distance metric for the most important feature 'product_category' has increased significantly. However, the team is unsure if this is the root cause. Which remediation step should the team take FIRST?
easy- A.Retrain the model using the most recent week of data and redeploy to the endpoint
- ✓ B.Investigate the data pipeline that feeds the training job to ensure consistent data collection and encoding of the 'product_category' feature
- C.Rebuild the SageMaker endpoint with a different instance type to improve performance
- D.Reduce the number of features in the model by removing 'product_category'
Why B: Before retraining the model or deploying a new endpoint, the team should investigate the source of the data drift by checking the input data pipeline. The data pipeline might have introduced a systematic error, such as a change in how 'product_category' is encoded or collected. Option A (retrain the model with more recent data) might not help if the data itself is corrupted. Option B (reduce the number of features) could ignore the problem. Option D (rebuild the endpoint) would not address the data drift. Therefore, the first step is to investigate the data pipeline.
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Last reviewed: Jun 23, 2026
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