Quick Answer
The answer is Amazon SageMaker Model Monitor, the correct choice because it is specifically designed for data drift detection model monitor tasks by continuously comparing inference data against a statistical baseline derived from the training data. This service automatically tracks feature distributions and alerts you when drift exceeds defined thresholds, enabling timely retraining decisions. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your ability to distinguish between monitoring, compute, and storage services—a common trap is confusing SageMaker Model Monitor with SageMaker Processing (an ETL tool) or Athena (a query service). Remember that Model Monitor is the only option purpose-built for drift detection, not data transformation or serverless execution. A useful memory tip: think “Monitor for Drift” as a direct pairing—if the task involves comparing live data to a training baseline, Model Monitor is your go-to service.
This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance and security. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 company trains a model daily using Amazon SageMaker and uses the model for real-time inference. They want to detect data drift between the training data and the inference data to decide when to retrain. Which AWS service should they use for this purpose?
Amazon Athena
Why wrong: Amazon Athena is a query service for data in S3, not for drift monitoring.
Amazon SageMaker Model Monitor
SageMaker Model Monitor is designed to detect data drift and model quality degradation.
AWS Glue
Why wrong: AWS Glue is used for ETL jobs, not for monitoring data drift.
AWS Lambda
Why wrong: AWS Lambda is for serverless compute, not drift detection.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Amazon SageMaker Model Monitor
Option B is correct because Amazon SageMaker Model Monitor can detect data drift by comparing inference data against a baseline created from training data. Option A is for ETL, not drift detection. Option C is for serverless compute. Option D is for querying data, not monitoring drift.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Amazon Athena
Why it's wrong here
Amazon Athena is a query service for data in S3, not for drift monitoring.
Amazon SageMaker Model Monitor
Why this is correct
SageMaker Model Monitor is designed to detect data drift and model quality degradation.
Related concept
Read the scenario before looking for a memorised answer.
AWS Glue
Why it's wrong here
AWS Glue is used for ETL jobs, not for monitoring data drift.
AWS Lambda
Why it's wrong here
AWS Lambda is for serverless compute, not drift detection.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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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FAQ
ML Solution Monitoring, Maintenance and Security — This question tests ML Solution Monitoring, Maintenance and Security — Read the scenario before looking for a memorised answer..
The correct answer is: Amazon SageMaker Model Monitor — Option B is correct because Amazon SageMaker Model Monitor can detect data drift by comparing inference data against a baseline created from training data. Option A is for ETL, not drift detection. Option C is for serverless compute. Option D is for querying data, not monitoring drift.
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.
Read the scenario before looking for a memorised answer.
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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
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 company uses Amazon SageMaker to train and deploy a machine learning model. After deployment, they notice that the model's accuracy drops significantly over time due to changes in the underlying data distribution. Which monitoring solution should they implement to detect this issue automatically?
mediumWhy A: Option D is correct because Amazon SageMaker Model Monitor can monitor data quality and model quality drift. Option A (CloudWatch Logs) is for logs, not drift detection. Option B (CloudTrail) tracks API calls. Option C (AWS Config) tracks resource configuration.
Variation 2. A company uses Amazon SageMaker Model Monitor to track data quality. The monitoring job triggers an alert indicating that the data distribution has shifted beyond the configured threshold. Which TWO actions should the team take? (Choose TWO.)
mediumWhy A: Options A and B are correct because the team should retrain the model on the new data distribution if the drift is significant, or update the baseline if the drift is acceptable and represents expected behavior. Options C, D, and E are not appropriate immediate actions.
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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