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?
⚠ Common exam trap
The trap is thinking 'update the endpoint' is sufficient — candidates must recognize that in-place updates cause downtime and lack gradual traffic control, whereas canary variants provide safe, reversible rollouts.
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
A canary deployment creates a new production variant on the existing endpoint with the new model and shifts a small percentage of traffic to it, allowing gradual validation and quick rollback by shifting traffic back to the original variant. This avoids downtime and provides controlled risk.
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
A canary deployment adds a new production variant holding the new model, then shifts traffic incrementally via variant weights. This satisfies the stem's no-downtime and gradual-shift requirements, and weights can be reverted instantly to roll back.
- ✗
Use a multi-model endpoint and replace the model file
Why it's wrong here
A multi-model endpoint loads several models behind one endpoint and selects by target model, but it offers no traffic-shifting mechanism or version-based rollback, so gradual switching is impossible. It suits hosting many models cost-effectively, not controlled deployment of a new version alongside the existing one.
- ✗
Stop the endpoint, update the model, and restart the endpoint
Why it's wrong here
Stopping the endpoint removes all serving capacity, causing the downtime the scenario explicitly forbids, and restarting gives no gradual traffic shift or fast rollback. It is tempting because it guarantees the new model loads cleanly, and would be acceptable for a development endpoint with no availability requirement.
- ✗
Update the existing endpoint's model directly using UpdateEndpoint
Why it's wrong here
UpdateEndpoint swaps the endpoint's model in place, so the old version is gone and cannot receive traffic or be restored instantly; rollback requires redeployment. It is tempting as the direct API for changing a model, and would be correct for a simple non-production update where downtime and rollback are not concerns.
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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: Option E is correct because SageMaker production variants on a single real-time endpoint are the native mechanism for A/B or canary testing: you update the endpoint configuration to host two variants (model A and model B) and assign each a weight, so variant A gets 95% and variant B gets 5% of live inference traffic while both are served from the same endpoint. Option B is correct because a CloudWatch alarm on the endpoint's invocation metrics (for example, ModelErrorRate or 5XX errors) can trigger automatic rollback to the previous endpoint configuration, which is the safety control that keeps risk minimal during the 24-hour test. Option A is not appropriate because SageMaker batch transform runs offline scoring on stored data and does not route live production traffic. Option C is not appropriate because blue/green deployment shifts traffic between whole environments and does not by itself provide a 5% weighted split of live requests. Option D is not appropriate because creating a separate endpoint and using Route 53 DNS weighting is a coarse, DNS-level split that does not give per-request weighted routing on a single SageMaker endpoint and adds unnecessary complexity.
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 and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.