Which statement best describes the core setup of a blue-green deployment?

Two identical environments are maintained: one serving live traffic (blue) and one idle with the new version (green).

Traffic is split evenly between the old and new versions to compare performance in real time.

A single environment is used, and the new version is gradually rolled out to a small percentage of users.

The new version is deployed to a subset of servers, and the old version is decommissioned immediately.

In a high-traffic scenario, which deployment strategy generally presents a lower risk of widespread user impact?

Both strategies have identical risk because they both use two environments.

Canary, because only a small percentage of users are exposed to the new version initially.

Blue-green, because the new version is fully tested before any traffic is shifted.

Neither strategy reduces risk; risk is solely determined by the quality of the code.

In Databricks Model Serving, how can you implement a canary rollout for a new model version?

Use the serving endpoint's traffic configuration to assign a percentage of traffic to the new version.

Deploy the new version to a separate endpoint and use a load balancer to split traffic.

Replace the existing model version with the new one and rely on automatic rollback.

Create a new serving endpoint and manually redirect clients to it.

A company has a Databricks Model Serving endpoint serving a loan-approval model. They need to deploy a new model version and want to be able to roll back instantly if the new version performs poorly. They have sufficient resources to run both versions simultaneously. What is the most efficient way to achieve this using Databricks Model Serving?

Create a new endpoint for the new version and update the application to point to the new endpoint

Use a separate endpoint for the new version and use a load balancer to switch traffic

Add the new version to the existing endpoint and set its traffic percentage to 100%, keeping the old version available for rollback

Delete the old version and deploy the new version as the only version on the endpoint

Compared to canary deployment, what is a key trade-off of blue-green deployment?

Blue-green provides slower rollback but uses fewer resources than canary.

Blue-green requires more complex traffic routing than canary because traffic must be split gradually.

Blue-green provides faster rollback but requires running two full environments, increasing resource usage.

Blue-green is riskier than canary because it exposes all users to the new version immediately.

DatabricksCertified Machine Learning Professional

Domain 3Objective 1

Deployment Strategies MACHINE-LEARNING-PROFESSIONAL Practice Questions (Page 1)

Part of the Model Deployment domain, which accounts for 12% of the MACHINE-LEARNING-PROFESSIONAL exam. Databricks does not publish an official question count, but from its 120-minute exam (~50–80 total, ~6–10 in this domain), expect 3–5 from this objective — we provide 15 practice questions to prepare you well beyond it. (estimate)

15questions here

3free pages

4concepts

12%of the exam

Questions 1–5

1foundation · easy
Which statement best describes the core setup of a blue-green deployment?
Select an answer first
2foundation · easy
In a high-traffic scenario, which deployment strategy generally presents a lower risk of widespread user impact?
Select an answer first
3foundation · easy
In Databricks Model Serving, how can you implement a canary rollout for a new model version?
Select an answer first
4application · medium
A company has a Databricks Model Serving endpoint serving a loan-approval model. They need to deploy a new model version and want to be able to roll back instantly if the new version performs poorly. They have sufficient resources to run both versions simultaneously. What is the most efficient way to achieve this using Databricks Model Serving?
Select an answer first
5foundation · easy
Compared to canary deployment, what is a key trade-off of blue-green deployment?
Select an answer first

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