Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Question
Which CI/CD approach for model deployment best minimizes downtime during a model update?
⚠ Common exam trap
Candidates often suggest 'redeploying' or 'updating in place', which causes temporary downtime. They overlook that blue-green deployment is the specific pattern designed to avoid this service interruption.
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 blue-green deployment pattern.
A blue-green deployment strategy is the gold standard for minimizing downtime. By spinning up a new version of the model (green) alongside the existing one (blue) and switching traffic only after verifying the health of the green endpoint, engineers ensure zero downtime and an immediate rollback path if issues occur. This approach is critical for high-availability AI services where any interruption would negatively impact users.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Delete the old model and immediately deploy the new version.
Why it's wrong here
Deleting the old model before deploying the new one inevitably creates downtime, as the endpoint must be recreated. This is a risky, non-professional approach that is unsuitable for production systems, especially for generative AI applications that users rely on continuously for their daily business tasks and information needs.
- ✓
Use a blue-green deployment pattern.
Why this is correct
Blue-green deployment allows for seamless traffic shifting between model versions. By maintaining two separate environments and routing traffic only when the new version is verified as stable, teams can ensure zero downtime during upgrades, which is essential for maintaining a robust, professional production AI service environment.
- ✗
Deploy all updates directly to the production endpoint.
Why it's wrong here
Directly updating the production endpoint without a staged rollout or blue-green switch risks introducing breaking changes that immediately impact all users. This lack of a safety buffer makes it impossible to perform validation before deployment, leading to potentially poor outcomes if the new model version has issues.
- ✗
Schedule deployments during peak traffic hours.
Why it's wrong here
Deploying during peak hours is the worst possible strategy, as it maximizes the number of users affected by any potential deployment failures or latency spikes. Deployments should always occur during low-traffic windows and be supported by robust, zero-downtime strategies to protect the user experience and service reliability.
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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 Databricks exam blueprint
This Databricks-GenAI-Assoc practice question is part of Courseiva's free Databricks 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 Databricks-GenAI-Assoc exam.