- A
Deploy the model on an EC2 instance behind an Application Load Balancer and manually update the instance with the new model version.
Why wrong: Manual updates increase downtime and risk of errors.
- B
Deploy the model using AWS Lambda with a container image and trigger via API Gateway.
Why wrong: Lambda is not optimized for large model inference and has cold start issues.
- C
Configure SageMaker endpoints with multiple production variants and use canary deployment to shift traffic gradually.
Canary deployment allows gradual traffic shift, minimizing downtime and enabling rollback.
- D
Use SageMaker hosting with a single production variant and update the endpoint with a new model configuration each week.
Why wrong: Updating the single variant causes downtime during update.
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 data science team needs to deploy a frequently updated PyTorch model for real-time inference. The model is retrained weekly and versioned using SageMaker Model Registry. Which deployment strategy minimizes downtime and allows easy rollback?
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 SageMaker endpoints with multiple production variants and use canary deployment to shift traffic gradually.
Option C is correct because SageMaker endpoints with multiple production variants enable canary deployment, which shifts traffic gradually from the old model to the new one. This minimizes downtime by keeping both variants active during the transition and allows easy rollback by simply redirecting all traffic back to the previous variant if issues arise.
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 the model on an EC2 instance behind an Application Load Balancer and manually update the instance with the new model version.
Why it's wrong here
Manual updates increase downtime and risk of errors.
- ✗
Deploy the model using AWS Lambda with a container image and trigger via API Gateway.
Why it's wrong here
Lambda is not optimized for large model inference and has cold start issues.
- ✓
Configure SageMaker endpoints with multiple production variants and use canary deployment to shift traffic gradually.
Why this is correct
Canary deployment allows gradual traffic shift, minimizing downtime and enabling rollback.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use SageMaker hosting with a single production variant and update the endpoint with a new model configuration each week.
Why it's wrong here
Updating the single variant causes downtime during update.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume a single production variant with endpoint updates is sufficient, overlooking the downtime and rollback limitations, while the canary deployment pattern with multiple variants directly addresses the requirements for minimal downtime and easy rollback.
Detailed technical explanation
How to think about this question
SageMaker's production variants use weighted traffic routing, where you can assign a small percentage (e.g., 5%) to the new model variant while the old variant handles the rest. This allows monitoring of metrics like latency and error rates before shifting 100% traffic, and rollback is as simple as setting the new variant's weight to 0. In practice, this strategy is critical for models retrained weekly, as it ensures continuous availability and rapid recovery from regressions.
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: Configure SageMaker endpoints with multiple production variants and use canary deployment to shift traffic gradually. — Option C is correct because SageMaker endpoints with multiple production variants enable canary deployment, which shifts traffic gradually from the old model to the new one. This minimizes downtime by keeping both variants active during the transition and allows easy rollback by simply redirecting all traffic back to the previous variant if issues arise.
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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