- 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.
Quick Answer
The answer is to use Amazon CloudWatch dashboards with a composite alarm based on the Sum of 5xx error counts across endpoints. This solution is correct because CloudWatch natively ingests SageMaker endpoint metrics like latency, invocation counts, and 5xx errors without any custom code, allowing you to aggregate health data from multiple instance types and scaling policies into a single centralized view. The composite alarm, using the Sum statistic over a 5-minute period, directly triggers when the error rate exceeds 5%, meeting the requirement for centralized monitoring and alerting for multiple SageMaker endpoints using only AWS-native services. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this tests your understanding of CloudWatch’s ability to handle cross-endpoint aggregation without third-party tools or streaming services—a common trap is reaching for CloudWatch Logs Insights or Kinesis, which add unnecessary complexity. Remember the memory tip: “Sum the 5xx, dash the view, composite alarm sees it through.”
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. 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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
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: 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.
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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.
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