- A
Blue/green deployment
Why wrong: Blue/green switches all traffic at once, not a gradual percentage.
- B
Shadow testing
Why wrong: Shadow testing sends duplicate traffic to a shadow endpoint for comparison, but does not serve real traffic.
- C
Canary deployment with production variants
Production variants allow traffic splitting; setting initial weight to 5% on the new variant achieves a canary.
- D
Multi-model endpoint
Why wrong: MME hosts many models, but does not inherently provide traffic shifting for deployment.
Question 363 of 1,000
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 company needs to deploy a new model version to a SageMaker real-time endpoint. They want to route 5% of traffic to the new version initially to monitor for errors before full rollout. Which deployment strategy should they use?
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
✓
Canary deployment with production variants
Option C is correct because a canary deployment with production variants allows you to route a specific percentage of traffic (e.g., 5%) to the new model version by adjusting the `InitialVariantWeight` parameter in the production variant configuration. This enables gradual traffic shifting while monitoring errors, and you can later increase the weight to 100% for full rollout. SageMaker real-time endpoints support this natively by hosting multiple model variants behind the same endpoint.
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.
- ✗
Blue/green deployment
Why it's wrong here
Blue/green switches all traffic at once, not a gradual percentage.
- ✗
Shadow testing
Why it's wrong here
Shadow testing sends duplicate traffic to a shadow endpoint for comparison, but does not serve real traffic.
- ✓
Canary deployment with production variants
Why this is correct
Production variants allow traffic splitting; setting initial weight to 5% on the new variant achieves a canary.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Multi-model endpoint
Why it's wrong here
MME hosts many models, but does not inherently provide traffic shifting for deployment.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse canary deployment with shadow testing, mistakenly thinking shadow testing also routes live user traffic, when in fact shadow testing only duplicates traffic for validation without affecting the user experience.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker production variants use a `VariantWeight` parameter that determines the proportion of inference requests each variant receives, with weights normalized across all variants. When you update the endpoint with a new variant at weight 0.05 (5%), SageMaker automatically distributes traffic based on these weights without downtime. A subtle behavior is that the endpoint must be in the 'InService' state before updating variants, and the total weight must sum to 1.0; otherwise, the API call fails. In a real-world scenario, you might combine this with CloudWatch alarms to automatically shift more traffic if error rates remain low.
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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Deployment and Orchestration of ML Workflows — study guide chapter
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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: Canary deployment with production variants — Option C is correct because a canary deployment with production variants allows you to route a specific percentage of traffic (e.g., 5%) to the new model version by adjusting the `InitialVariantWeight` parameter in the production variant configuration. This enables gradual traffic shifting while monitoring errors, and you can later increase the weight to 100% for full rollout. SageMaker real-time endpoints support this natively by hosting multiple model variants behind the same endpoint.
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: 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.
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