MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
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.
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.
- ✗
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.
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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: 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: 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.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.