- A
Use a canary deployment by creating a new production variant with the new model and shifting traffic incrementally
This allows gradual traffic shift and the old variant can be used for rollback if needed.
- B
Use a multi-model endpoint and replace the model file
Why wrong: Replacing the model file on S3 can cause inconsistencies; MME loads models on demand and may serve stale or partial data.
- C
Stop the endpoint, update the model, and restart the endpoint
Why wrong: This causes downtime.
- D
Update the existing endpoint's model directly using UpdateEndpoint
Why wrong: Updating the model directly would cause a brief downtime as the endpoint is updated; traffic may be disrupted.
Question 773 of 1,000
Canary Deployment with SageMaker Real-Time Endpoint
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 update a model in production without any downtime. They currently have a single real-time endpoint serving traffic. Which approach allows them to deploy a new model version and switch traffic gradually while being able to roll back quickly?
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 a canary deployment by creating a new production variant with the new model and shifting traffic incrementally
SageMaker supports production variants with traffic splitting. By creating a new variant with the new model and shifting traffic gradually, the old variant remains available for rollback. Blue/green deployment with a new endpoint and endpoint configuration swap also allows quick rollback. The key is to have both variants active during the transition.
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.
- ✓
Use a canary deployment by creating a new production variant with the new model and shifting traffic incrementally
Why this is correct
This allows gradual traffic shift and the old variant can be used for rollback if needed.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a multi-model endpoint and replace the model file
Why it's wrong here
Replacing the model file on S3 can cause inconsistencies; MME loads models on demand and may serve stale or partial data.
- ✗
Stop the endpoint, update the model, and restart the endpoint
Why it's wrong here
This causes downtime.
- ✗
Update the existing endpoint's model directly using UpdateEndpoint
Why it's wrong here
Updating the model directly would cause a brief downtime as the endpoint is updated; traffic may be disrupted.
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
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Deployment and Orchestration of ML Workflows practice questions
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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 a canary deployment by creating a new production variant with the new model and shifting traffic incrementally — SageMaker supports production variants with traffic splitting. By creating a new variant with the new model and shifting traffic gradually, the old variant remains available for rollback. Blue/green deployment with a new endpoint and endpoint configuration swap also allows quick rollback. The key is to have both variants active during the transition.
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 →
Same concept, more angles
3 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company wants to update an existing SageMaker real-time endpoint to serve a new model version. They need to route a small percentage of traffic to the new version initially and monitor for errors before switching fully. Which deployment pattern supports this?
easy- A.Shadow testing
- B.A/B testing with traffic splitting
- ✓ C.Canary deployment with weighted production variants
- D.Blue/green deployment
Why C: Option C is correct because SageMaker real-time endpoints support canary deployments by configuring multiple production variants with weighted traffic distribution. You can assign a small weight (e.g., 5%) to the new model version variant and 95% to the existing one, then monitor CloudWatch metrics for errors before shifting all traffic to the new variant. This matches the requirement for a gradual, monitored rollout.
Variation 2. 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.)
medium- A.Deploy model B using SageMaker batch transform and compare offline metrics
- ✓ B.Configure a CloudWatch alarm to roll back if error rate exceeds a threshold
- C.Use SageMaker's blue/green deployment and shift 5% traffic initially
- D.Create a new endpoint with model B and use Amazon Route 53 to split 5% of traffic
- ✓ E.Update the existing endpoint to include two production variants: variant A with 95% traffic and variant B with 5% traffic
Why B: 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.
Variation 3. A company is using SageMaker to serve a model for real-time predictions. They want to test a new model version by routing a small percentage of live traffic to it while the rest goes to the current model. They also need to compare performance metrics. Which TWO actions should they take? (Select TWO.)
medium- A.Deploy the new model to a separate endpoint and use Route 53 to split traffic
- B.Compile the new model with SageMaker Neo before deployment
- C.Use SageMaker Batch Transform to evaluate the new model
- ✓ D.Monitor the performance of both variants using SageMaker CloudWatch metrics
- ✓ E.Configure a production variant with the new model and set initial traffic weight to a small percentage
Why D: Option D is correct because Amazon CloudWatch provides built-in metrics for SageMaker endpoints, including latency, invocation counts, and error rates, which can be monitored per production variant. This allows the company to compare the performance of the new model version against the current model in real time. Option E is correct because SageMaker endpoints support multiple production variants, and you can set an initial traffic weight (e.g., 5%) to route a small percentage of live traffic to the new model while the rest goes to the existing variant.
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Last reviewed: Jul 4, 2026
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