Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Question
An organization is deploying a GenAI application using Databricks Model Serving. Which TWO steps are required to ensure the deployment environment handles model governance and observability effectively?
⚠ Common exam trap
Candidates often select only one of the two options, missing that governance (Unity Catalog) and observability (MLflow inference logging) are distinct, mandatory requirements for a production-grade deployment.
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
✓
Enable Unity Catalog for the registered model and its versions.
Effective model governance and observability require integrating Unity Catalog for centralized access control and MLflow for tracking inference payloads. These components allow organizations to monitor model performance, lineage, and bias over time. Mastering these tools ensures that production deployments remain compliant, transparent, and auditable, which is essential for regulated industries using generative AI to make data-driven decisions while minimizing operational risks associated with model drift and unauthorized access.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable Unity Catalog for the registered model and its versions.
Why this is correct
Unity Catalog acts as the central governance layer for all data and AI assets. Enabling it for registered models provides unified access control, lineage tracking, and auditability, ensuring that only authorized users can deploy or modify models, which is a foundational requirement for robust enterprise AI security and governance.
- ✓
Use MLflow to enable inference logging for model endpoints.
Why this is correct
Enabling inference logging via MLflow captures request and response data, which is vital for monitoring model performance and identifying potential hallucinations. This data helps engineers analyze drift, debug production failures, and optimize the generative AI model, providing the necessary visibility into how the model behaves when deployed in production.
- ✗
Hardcode API credentials within the model inference script.
Why it's wrong here
Hardcoding credentials is a severe security risk that exposes sensitive information to anyone with read access to the codebase. Instead, secrets management via Databricks Secrets or Unity Catalog should be used to securely inject credentials at runtime, maintaining a strict separation between code and sensitive configuration data.
- ✗
Disable automatic scaling to maintain consistent latency.
Why it's wrong here
Disabling automatic scaling does not improve observability and often leads to performance bottlenecks during traffic spikes. Proper observability tools allow for dynamic scaling adjustments based on real-time metrics, ensuring high availability and cost-efficiency without manual intervention or the rigid limitations imposed by fixed-size infrastructure configurations in production.
- ✗
Manually deploy the model via the standard cluster user interface.
Why it's wrong here
Manual deployment via cluster UI lacks reproducibility and auditability compared to using CI/CD pipelines and Model Serving APIs. Enterprise deployments should be automated through version-controlled scripts to ensure that every change is documented and tested, reducing human error and configuration drift in the production model serving environment.
About these practice questions
This Databricks-GenAI-Assoc question is part of Courseiva's 330-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.