- A
Enable SageMaker Model Monitor data capture on each endpoint and stream captured data to Amazon Kinesis for analysis.
Why wrong: Model Monitor captures inference data for drift, not for real-time error monitoring.
- B
Use AWS CloudTrail to audit all API calls to SageMaker and set up alarms on error responses.
Why wrong: CloudTrail audits control plane operations, not inference requests or errors.
- C
Use Amazon CloudWatch Logs to collect logs from each endpoint, and use a Lambda function to parse logs and calculate error rates, then publish custom metrics.
Why wrong: This approach adds custom code and complexity compared to using built-in CloudWatch metrics.
- D
Use Amazon CloudWatch dashboards to aggregate metrics from all endpoints, and create a composite alarm based on the Sum of 5xx error counts across endpoints.
CloudWatch natively aggregates metrics and composite alarms can alert on the combined error rate.
MLA-C01 Practice Question: A large enterprise has multiple SageMaker…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 large enterprise has multiple SageMaker endpoints serving models for different business units. Each endpoint uses a separate instance type and scaling policy. The enterprise wants to implement a unified monitoring and logging solution to track endpoint health, latency, and errors across all endpoints. They also want to set up alerts when the error rate exceeds 5% over a 5-minute period. The solution must be centralized and use AWS-native services. Which solution should the team implement?
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 Amazon CloudWatch dashboards to aggregate metrics from all endpoints, and create a composite alarm based on the Sum of 5xx error counts across endpoints.
Option D is correct because Amazon CloudWatch can natively ingest SageMaker endpoint metrics (e.g., 5xx error counts, latency, invocation counts) without additional configuration. By creating a CloudWatch dashboard, you aggregate metrics from all endpoints into a single view, and a composite alarm using the Sum statistic across endpoints over a 5-minute period directly triggers when the error rate exceeds 5%. This approach is fully centralized, uses only AWS-native services, and requires no custom code or data streaming.
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.
- ✗
Enable SageMaker Model Monitor data capture on each endpoint and stream captured data to Amazon Kinesis for analysis.
Why it's wrong here
Model Monitor captures inference data for drift, not for real-time error monitoring.
- ✗
Use AWS CloudTrail to audit all API calls to SageMaker and set up alarms on error responses.
Why it's wrong here
CloudTrail audits control plane operations, not inference requests or errors.
- ✗
Use Amazon CloudWatch Logs to collect logs from each endpoint, and use a Lambda function to parse logs and calculate error rates, then publish custom metrics.
Why it's wrong here
This approach adds custom code and complexity compared to using built-in CloudWatch metrics.
- ✓
Use Amazon CloudWatch dashboards to aggregate metrics from all endpoints, and create a composite alarm based on the Sum of 5xx error counts across endpoints.
Why this is correct
CloudWatch natively aggregates metrics and composite alarms can alert on the combined error rate.
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 Model Monitor (data quality) with endpoint monitoring (operational health), or assume CloudWatch Logs are required when SageMaker endpoints already emit rich metrics directly to CloudWatch.
Detailed technical explanation
How to think about this question
SageMaker endpoints automatically publish over 20 metrics to CloudWatch, including ModelLatency, Invocation4XXErrors, and Invocation5XXErrors, with a resolution of 1 minute. A composite alarm can combine multiple metrics using math expressions (e.g., SUM(m1, m2) / SUM(invocations) > 0.05) to calculate the exact error rate across endpoints. In a real-world scenario, if one endpoint has high traffic and another low, a simple sum of errors might trigger a false positive; using a CloudWatch metric math expression with a rate calculation ensures the 5% threshold is applied correctly.
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.
Related practice questions
Related MLA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
Deployment and Orchestration of ML Workflows practice questions
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
ML Solution Monitoring, Maintenance, and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance, and Security.
ML Solution Monitoring, Maintenance and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
MLA-C01 fundamentals practice questions
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
MLA-C01 scenario practice questions
Practise MLA-C01 questions linked to MLA-C01 scenario.
MLA-C01 troubleshooting practice questions
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Start a free MLA-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Use Amazon CloudWatch dashboards to aggregate metrics from all endpoints, and create a composite alarm based on the Sum of 5xx error counts across endpoints. — Option D is correct because Amazon CloudWatch can natively ingest SageMaker endpoint metrics (e.g., 5xx error counts, latency, invocation counts) without additional configuration. By creating a CloudWatch dashboard, you aggregate metrics from all endpoints into a single view, and a composite alarm using the Sum statistic across endpoints over a 5-minute period directly triggers when the error rate exceeds 5%. This approach is fully centralized, uses only AWS-native services, and requires no custom code or data streaming.
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.
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 →
Keep practising
More MLA-C01 practice questions
- A team is using SageMaker Pipelines to train a model. The pipeline has multiple steps: data processing, training, evalua…
- A machine learning team deploys a custom container image for an Amazon SageMaker training job. The container needs to ac…
- A machine learning engineer sees the above error in Amazon CloudWatch Logs for a SageMaker endpoint. What is the most li…
- A data scientist has trained a model that achieves 95% accuracy on the training set but only 70% on the test set. Which…
- Refer to the exhibit. A data scientist reviews the output of a SageMaker training job. The model has 95% training accura…
- A team is using Amazon SageMaker to train a neural network. They want to minimize training time while effectively explor…
Last reviewed: Jun 24, 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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.