The answer is to enable Amazon SageMaker Model Monitor to capture inference data and compare it against a baseline dataset. This is the most effective way to detect data drift because SageMaker Model Monitor is purpose-built for continuously analyzing real-time inference requests against a statistical baseline, automatically flagging when the distribution of incoming data deviates from the training data. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of managed monitoring services versus generic alternatives; a common trap is choosing CloudWatch Logs Insights or Athena queries, which require manual setup and lack automated drift detection. The key distinction is that SageMaker Model Monitor provides built-in constraints and alerts specifically for data drift detection, while other options are batch-oriented or not ML-aware. Memory tip: think “Baseline + Baseline = Drift Detection” — SageMaker Model Monitor compares live data against a saved baseline to catch drift automatically.
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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.
A company runs a real-time fraud detection system using Amazon SageMaker. The model is deployed as a SageMaker endpoint and receives predictions within milliseconds. Recently, the model's accuracy has degraded due to data drift. The data scientists want to monitor the model's performance continuously. What is the most effective way to detect data drift?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable Amazon SageMaker Model Monitor to capture inference data and compare it against a baseline dataset
Option C is correct because SageMaker Model Monitor can automatically detect data drift by comparing incoming data against a baseline. Option A is wrong because CloudWatch Logs Insights can query logs but not automatically detect drift. Option B is wrong because storing predictions in S3 and using Athena is batch-oriented and not automated. Option D is wrong because CloudWatch anomaly detection is generic and not specialized for ML model drift.
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.
✗
Store all incoming requests in Amazon S3 and use Amazon Athena to run periodic SQL queries for drift detection
Why it's wrong here
Why B is wrong
✗
Set up Amazon CloudWatch anomaly detection on the endpoint's invocation count and latency metrics
Why it's wrong here
Why D is wrong
✓
Enable Amazon SageMaker Model Monitor to capture inference data and compare it against a baseline dataset
Why this is correct
Why C is correct
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Amazon CloudWatch Logs Insights to analyze inference logs and set custom alarms
Why it's wrong here
Why A is wrong
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 MLS-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.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable Amazon SageMaker Model Monitor to capture inference data and compare it against a baseline dataset — Option C is correct because SageMaker Model Monitor can automatically detect data drift by comparing incoming data against a baseline. Option A is wrong because CloudWatch Logs Insights can query logs but not automatically detect drift. Option B is wrong because storing predictions in S3 and using Athena is batch-oriented and not automated. Option D is wrong because CloudWatch anomaly detection is generic and not specialized for ML model drift.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-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.
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This MLS-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 MLS-C01 exam.
Question Discussion
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