AI-300 ML Model Lifecycle And Operations Practice Question
You are deploying a model via a Managed Online Endpoint. You want to implement a Canary deployment where 10% of traffic is sent to a new model version. How do you configure this?
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 the 'traffic' parameter in the 'az ml online-endpoint update' command.
You use the 'update' command on the endpoint to set the 'traffic' parameter, distributing weights across deployment names.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure the 'traffic' parameter in the 'az ml online-endpoint update' command.
Why this is correct
This is the correct way to shift traffic weights.
- ✗
Modify the 'inference_config.json' file to include weights.
Why it's wrong here
Traffic shifting is handled at the endpoint level, not the config.
- ✗
Create two endpoints and use an Azure Load Balancer.
Why it's wrong here
This is not the standard Azure ML pattern.
- ✗
Set the 'replica_count' to 10% of total capacity.
Why it's wrong here
Replica count is for scaling, not traffic routing.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
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