Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Question
When deploying a model to Databricks Model Serving, which THREE of the following are best practices to ensure production reliability?
⚠ Common exam trap
Candidates often overlook 'pinning' to a specific model version, which risks production instability if the 'latest' version is updated or changed unexpectedly during the deployment process.
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
✓
Configuring autoscaling policies based on traffic patterns.
Production reliability for model serving requires careful attention to environment configuration, scalability, and security. Best practices include using specific model versions to prevent unexpected updates, configuring autoscaling to handle variable traffic, and utilizing private connectivity to keep data transmission secure. These steps minimize downtime and ensure that the serving endpoint behaves predictably under various load conditions, which is critical for enterprise-grade generative AI applications.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploying the 'latest' alias for all production endpoints.
Why it's wrong here
Using 'latest' aliases in production is dangerous because it can lead to unexpected model updates without sufficient validation. Production endpoints should always be pinned to a specific model version or a stable production alias to ensure consistency and allow for proper rollback if a new model version fails.
- ✓
Configuring autoscaling policies based on traffic patterns.
Why this is correct
Autoscaling ensures that the model serving endpoint can dynamically adjust its compute resources based on request volume. This improves performance during peak traffic and manages costs during idle times, contributing significantly to the overall stability and operational efficiency of the model serving infrastructure.
- ✗
Using static credentials embedded directly in the deployment YAML.
Why it's wrong here
Embedding credentials in configuration files is a major security vulnerability. It exposes sensitive information to anyone with access to the source code repository. Instead, secrets should be retrieved at runtime using the Databricks Secrets API or managed via environment-specific service principal configurations with limited scopes.
- ✓
Pinning to a specific registered model version.
Why this is correct
Pinning a deployment to a specific version ensures that the production model remains static until a deliberate update is initiated. This prevents unintentional model changes and provides a deterministic environment for users, which is essential for auditability and quality control in machine learning production environments.
- ✓
Implementing VPC peering or Private Link for secure connectivity.
Why this is correct
Network security is critical for enterprise applications. By using private networking options like Private Link, you ensure that traffic between your application and the model serving endpoint never traverses the public internet, significantly reducing the attack surface and complying with stringent corporate data privacy requirements.
About these practice questions
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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.