What is the purpose of managing multiple model versions in a Databricks Model Serving endpoint?

To allow different clients to use different model versions based on their subscription tier.

To reduce the number of endpoints needed for different model types.

To automatically select the best-performing version based on real-time metrics.

To enable gradual rollout and rollback by controlling the traffic percentage for each version.

What is the primary advantage of blue-green deployment regarding rollback?

Rollback is automatic when the new version fails health checks.

Rollback is instant by switching traffic back to the previous environment.

Rollback requires restoring a database snapshot before traffic can be switched.

Rollback is achieved by redeploying the previous version from source code.

Which statement best describes how traffic is managed in a canary deployment?

The new version is deployed to a separate environment and never receives production traffic.

A small percentage of traffic is routed to the new version, then gradually increased as confidence grows.

Traffic is split 50/50 between the old and new versions for the entire deployment.

All traffic is shifted to the new version at once after a brief testing period.

An online retailer must deploy a new pricing model. The team requires the ability to roll back within seconds if the model causes revenue to drop. They also want to validate the model on a small percentage of traffic before full rollout. They have ample compute resources. Which deployment strategy should they choose?

Blue-green deployment with canary-like traffic splitting

A data science team is using Databricks Model Serving to deploy a new version of a churn-prediction model. They want to gradually shift traffic from the current production version to the new version, starting with 5% and increasing to 100% over a week. What should they do in Databricks Model Serving?

Deploy the new version as a separate endpoint and switch DNS to the new endpoint after validation

Update the existing endpoint's traffic configuration to route 5% to the new version, then gradually increase the percentage

Create a new serving endpoint for the new version and use a load balancer to distribute traffic

Replace the current version with the new version immediately and monitor for issues

DatabricksCertified Machine Learning Professional

Domain 3Objective 1

Deployment Strategies MACHINE-LEARNING-PROFESSIONAL Practice Questions (Page 2)

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 6–10

6foundation · easy
What is the purpose of managing multiple model versions in a Databricks Model Serving endpoint?
Select an answer first
7foundation · easy
What is the primary advantage of blue-green deployment regarding rollback?
Select an answer first
8foundation · easy
Which statement best describes how traffic is managed in a canary deployment?
Select an answer first
9expert · hard
An online retailer must deploy a new pricing model. The team requires the ability to roll back within seconds if the model causes revenue to drop. They also want to validate the model on a small percentage of traffic before full rollout. They have ample compute resources. Which deployment strategy should they choose?
Select an answer first
10application · medium
A data science team is using Databricks Model Serving to deploy a new version of a churn-prediction model. They want to gradually shift traffic from the current production version to the new version, starting with 5% and increasing to 100% over a week. What should they do in Databricks Model Serving?
Select an answer first

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Deployment Strategies MACHINE-LEARNING-PROFESSIONAL Practice Questions (Page 2)

Questions 6–10

What is the purpose of managing multiple model versions in a Databricks Model Serving endpoint?

What is the primary advantage of blue-green deployment regarding rollback?

Which statement best describes how traffic is managed in a canary deployment?

Keep studying — Model Deployment