The answer is to enable data capture on the SageMaker endpoint, configure baseline constraints from training data, and create a monitoring schedule. Enabling data capture is the prerequisite because SageMaker Model Monitor needs a record of actual inference requests and responses to compare against a statistical baseline; without this captured data, there is nothing to analyze for drift. Configuring baseline constraints from the training data establishes the expected distribution and range of features, which the monitoring schedule then checks against new data at regular intervals. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding that monitoring is not automatic—you must explicitly turn on data capture and define a baseline before scheduling analysis. A common trap is assuming Model Monitor works out of the box on any endpoint, but it requires these three deliberate steps. Memory tip: Capture, Baseline, Schedule—the three pillars of proactive drift detection.
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. 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 is running a SageMaker endpoint serving multiple models. They need to monitor for data drift and model quality. Which THREE actions are necessary? (Choose three.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable data capture on the endpoint
Option B is correct because enabling data capture on the SageMaker endpoint is a prerequisite for monitoring data drift and model quality. Data capture automatically records input requests and output responses from the endpoint, which SageMaker Model Monitor later analyzes against a baseline to detect drift. Without data capture, there is no data to compare against the baseline constraints.
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 a shadow endpoint for comparison
Why it's wrong here
Shadow endpoint is for traffic shifting, not monitoring drift.
✓
Enable data capture on the endpoint
Why this is correct
Data capture logs inference requests for monitoring.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use SageMaker Debugger for monitoring
Why it's wrong here
Debugger is for training debugging, not production monitoring.
✓
Create a SageMaker Model Monitor schedule
Why this is correct
Schedule defines how often to run monitoring jobs.
Related concept
Read the scenario before looking for a memorised answer.
✓
Configure baseline constraints from training data
Why this is correct
Baseline constraints define expected statistical properties for drift detection.
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 confuse SageMaker Debugger (for training) with SageMaker Model Monitor (for inference), leading them to select Debugger instead of the correct monitoring schedule and baseline configuration.
Detailed technical explanation
How to think about this question
SageMaker Model Monitor works by first creating a baseline from training data using the `sagemaker.model_monitor.DefaultModelMonitor.suggest_baseline()` API, which computes statistics and constraints (e.g., min, max, mean, and allowed drift thresholds). Once deployed, it runs a scheduled monitoring job (e.g., hourly) that compares captured inference data against these baseline constraints using a built-in container that performs statistical tests like z-score or L-infinity distance. A real-world scenario is a financial services company monitoring a credit risk model for drift in feature distributions (e.g., income suddenly shifting) to ensure regulatory compliance.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable data capture on the endpoint — Option B is correct because enabling data capture on the SageMaker endpoint is a prerequisite for monitoring data drift and model quality. Data capture automatically records input requests and output responses from the endpoint, which SageMaker Model Monitor later analyzes against a baseline to detect drift. Without data capture, there is no data to compare against the baseline constraints.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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