20+ practice questions focused on Application Development — one of the most tested topics on the Databricks Certified Generative AI Engineer Associate exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Application Development PracticeA team is developing a custom application using the Databricks SQL Warehouse. They need to optimize query performance for high-frequency small requests. Which TWO strategies should they implement?
Explanation: For high-frequency small requests, minimizing overhead is key. Serverless SQL Warehouses provide faster startup times, reducing latency for bursty workloads. Additionally, using specific query syntax to avoid full table scans helps keep the execution plan lean. These strategies are fundamental for developers aiming to build responsive applications that rely on Databricks as the backend data engine, ensuring cost efficiency and optimal user experience in production environments.
A GenAI application requires low-latency retrieval of vector embeddings. Which TWO components should the developer prioritize to minimize latency?
Explanation: To minimize retrieval latency, developers must optimize the storage layer and the indexing strategy. Using vector-optimized search indexes within the Databricks environment and keeping data co-located with the compute resources significantly reduces I/O overhead. Furthermore, ensuring that the model serving infrastructure is provisioned with adequate capacity prevents bottlenecking. These architectural choices are vital for building responsive AI applications that meet the strict performance requirements of real-time user-facing services.
Which THREE steps are necessary to ensure a Databricks notebook can be executed as a production job with secure access to data in Unity Catalog?
Explanation: Production jobs require identity management and proper permissioning. By using a Service Principal, you ensure that the job runs under a dedicated, non-user-bound identity, which is essential for stable production environments. Coupling this with proper Grant statements in Unity Catalog ensures the job has the principle of least privilege. Finally, using cluster-level access policies prevents unauthorized configuration changes, creating a secure, repeatable, and compliant production execution environment.
A developer is building a RAG application using Mosaic AI Model Serving. They need to ensure the application remains highly available during model updates. Which deployment strategy should be implemented to minimize downtime while testing the new model version?
Explanation: Blue-green deployment allows teams to run two versions of the serving endpoint simultaneously, ensuring seamless transition. By shifting traffic incrementally, developers can validate performance metrics against the production baseline without affecting end-user requests. This architectural pattern is critical in Databricks for maintaining SLAs while integrating iterative model improvements into live GenAI applications, effectively reducing the risk of downtime or performance degradation during model swaps.
A developer is using MLflow to track a fine-tuning job. After the job completes, they notice the model weights are not appearing in the Unity Catalog. Which requirement must be met to ensure the model is governed and discoverable?
Explanation: To register models in Unity Catalog, the model must be logged with the MLflow flavors compatible with the Databricks Model Registry. Specifically, it must be logged to the Unity Catalog-enabled registry rather than the workspace-local registry. This integration is vital for centralized lineage, permission management, and lifecycle tracking, ensuring that production-ready LLMs are fully compliant with enterprise governance standards before they are consumed by downstream inference applications.
+15 more Application Development questions available
Practice all Application Development questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of Application Development. This tells you whether you need a concept refresher or just practice.
2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
Application Development questions on the Databricks-GenAI-Assoc frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. Application Development is tested as part of the Databricks Certified Generative AI Engineer Associate blueprint. Practicing with targeted Application Development questions ensures you can handle any format or difficulty that appears.
Yes. Courseiva provides free Databricks-GenAI-Assoc practice questions across all exam topics and domains. The platform includes topic-based practice, mock exams, missed-question review, bookmarked questions, and readiness tracking — no account required.
Difficulty is subjective, but Application Development is a high-priority exam concept tested in multiple ways — direct recall, scenario analysis, and command-output interpretation. Consistent practice is the best way to build confidence.
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