- A
Deploy model B using SageMaker batch transform and compare offline metrics
Why wrong: Batch transform is offline and cannot simulate live traffic conditions; the requirement is to test in production with live traffic.
- B
Configure a CloudWatch alarm to roll back if error rate exceeds a threshold
CloudWatch alarms can be set on endpoint metrics (e.g., error rate, latency) to trigger automatic rollback or alert the team.
- C
Use SageMaker's blue/green deployment and shift 5% traffic initially
Why wrong: Blue/green deployment creates a new endpoint and shifts all traffic at once; it does not support gradual traffic splitting.
- D
Create a new endpoint with model B and use Amazon Route 53 to split 5% of traffic
Why wrong: Route 53 splits traffic at the DNS level, not at the application level, and is not the recommended approach for SageMaker A/B testing.
- E
Update the existing endpoint to include two production variants: variant A with 95% traffic and variant B with 5% traffic
SageMaker endpoints support multiple production variants with traffic weights, enabling canary testing.
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 wants to test a new ML model in production with minimal risk before shifting full traffic. They have an existing real-time endpoint serving model version A. They need to route 5% of live traffic to model version B and monitor performance for 24 hours. Which TWO steps should they take? (Choose TWO.)
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
Configure a CloudWatch alarm to roll back if error rate exceeds a threshold
Blue/green deployment creates a new endpoint with the new model and swaps all traffic at once, not a gradual shift. Canary deployment routes a small percentage of traffic to the new version for testing. SageMaker supports canary deployments by updating the endpoint with multiple production variants and specifying initial traffic weights. The existing endpoint should be updated to include both variants.
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 model B using SageMaker batch transform and compare offline metrics
Why it's wrong here
Batch transform is offline and cannot simulate live traffic conditions; the requirement is to test in production with live traffic.
- ✓
Configure a CloudWatch alarm to roll back if error rate exceeds a threshold
Why this is correct
CloudWatch alarms can be set on endpoint metrics (e.g., error rate, latency) to trigger automatic rollback or alert the team.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use SageMaker's blue/green deployment and shift 5% traffic initially
Why it's wrong here
Blue/green deployment creates a new endpoint and shifts all traffic at once; it does not support gradual traffic splitting.
- ✗
Create a new endpoint with model B and use Amazon Route 53 to split 5% of traffic
Why it's wrong here
Route 53 splits traffic at the DNS level, not at the application level, and is not the recommended approach for SageMaker A/B testing.
- ✓
Update the existing endpoint to include two production variants: variant A with 95% traffic and variant B with 5% traffic
Why this is correct
SageMaker endpoints support multiple production variants with traffic weights, enabling canary testing.
Related concept
Read the scenario before looking for a memorised answer.
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 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 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.
- →
Deployment and Orchestration of ML Workflows — study guide chapter
Learn the concepts, then practise the questions
- →
Deployment and Orchestration of ML Workflows practice questions
Targeted practice on this topic area only
- →
All MLA-C01 questions
1,000 questions across all exam domains
- →
AWS Certified Machine Learning Engineer Associate MLA-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLA-C01 practice test guide
How to use practice tests most effectively before exam day
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?
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: Configure a CloudWatch alarm to roll back if error rate exceeds a threshold — Blue/green deployment creates a new endpoint with the new model and swaps all traffic at once, not a gradual shift. Canary deployment routes a small percentage of traffic to the new version for testing. SageMaker supports canary deployments by updating the endpoint with multiple production variants and specifying initial traffic weights. The existing endpoint should be updated to include both variants.
What should I do if I get this MLA-C01 question wrong?
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.
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 →
Last reviewed: Jul 4, 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.