DATABRICKS-GAIE Practice Questions: Application Development Domain

Published: September 29, 2025 | 20 min read

Test your DATABRICKS-GAIE knowledge with 10 practice questions from the Application Development domain. Includes detailed explanations and answers.

DATABRICKS-GAIE Practice Questions

Master the Application Development Domain

Test your knowledge in the Application Development domain with these 10 practice questions. Each question is designed to help you prepare for the DATABRICKS-GAIE certification exam with detailed explanations to reinforce your learning.

Question 1

For a generative AI application on Databricks, you need to optimize the user experience by integrating with external APIs. Which design pattern would be most suitable for this integration?

A) Monolithic architecture

B) Microservices architecture

C) Event-driven architecture

D) Batch processing architecture

Show Answer & Explanation

Correct Answer: B

Explanation: Microservices architecture is suitable for integrating with external APIs as it allows for modular design and independent scaling of services, optimizing user experience. Monolithic architecture is less flexible, event-driven is for asynchronous processing, and batch processing is not suitable for real-time API integration.

Question 2

You are tasked with integrating a generative AI model into a Databricks notebook for a customer support application. Which approach would you take to ensure efficient interaction between the model and the application using LangChain?

A) Directly call the model API from the notebook without using any intermediary.

B) Use LangChain to create a chain that handles prompt generation, model invocation, and response parsing.

C) Deploy the model on a local server and access it via HTTP requests.

D) Use Databricks Connect to execute model calls directly from a local Python environment.

Show Answer & Explanation

Correct Answer: B

Explanation: LangChain is designed to streamline the process of interacting with language models by managing prompts, model calls, and responses efficiently. This makes it the best choice for integrating a generative AI model in a Databricks notebook.

Question 3

You are deploying a Generative AI model as part of a Databricks application. Which best practice should you follow to ensure the model can be easily updated and maintained over time?

A) Deploy the model directly on a production cluster without version control.

B) Use MLflow to register and version the model before deployment.

C) Hard-code model parameters in the application code for stability.

D) Deploy the model using a custom-built container image without CI/CD.

Show Answer & Explanation

Correct Answer: B

Explanation: Using MLflow to register and version the model ensures that updates and maintenance can be managed efficiently over time. Deploying without version control (A) and hard-coding parameters (C) make updates difficult. Custom containers without CI/CD (D) lack the automation needed for efficient deployment and updates.

Question 4

When integrating LangChain with a Databricks application to enhance natural language processing capabilities, which feature of LangChain is most beneficial?

A) LangChain's ability to store large datasets in Delta Lake.

B) LangChain's pre-built connectors for various vector databases.

C) LangChain's prompt engineering tools for optimizing Generative AI model interactions.

D) LangChain's built-in machine learning model training capabilities.

Show Answer & Explanation

Correct Answer: C

Explanation: Option C is correct because LangChain provides prompt engineering tools that are crucial for optimizing interactions with Generative AI models. Option A is incorrect because LangChain does not focus on data storage. Option B is incorrect as LangChain's primary benefit is not its connectors. Option D is incorrect because LangChain is not primarily used for training models.

Question 5

How does Unity Catalog enhance data governance in a Databricks Generative AI application?

A) By providing a distributed file system for data storage.

B) By offering a centralized governance solution with fine-grained access controls and audit trails.

C) By integrating directly with vector databases for optimized data retrieval.

D) By automatically tuning model hyperparameters based on data characteristics.

Show Answer & Explanation

Correct Answer: B

Explanation: Option B is correct because Unity Catalog provides a centralized governance solution that includes fine-grained access controls and audit trails, essential for managing data governance. Option A is incorrect as Unity Catalog is not a file system. Option C is incorrect because Unity Catalog does not integrate with vector databases for retrieval. Option D is incorrect as Unity Catalog is not involved in model hyperparameter tuning.

Question 6

In a Databricks generative AI application, how would you implement continuous integration and continuous deployment (CI/CD) for model updates?

A) Use MLflow for version control and deployment

B) Integrate with external CI/CD tools like Jenkins

C) Leverage Delta Lake for CI/CD processes

D) Use Unity Catalog to automate CI/CD

Show Answer & Explanation

Correct Answer: B

Explanation: Integrating with external CI/CD tools like Jenkins allows for automated testing and deployment of model updates. MLflow is for model management, Delta Lake is for data processing, and Unity Catalog is for governance, not directly for CI/CD automation.

Question 7

You are developing a Generative AI application on Databricks and need to ensure that the application can be easily monitored and updated. Which set of practices would best achieve this goal?

A) Use Unity Catalog for model versioning and implement manual deployment processes.

B) Leverage MLflow for model tracking and implement a CI/CD pipeline for automated deployment.

C) Deploy models using Databricks Jobs and rely on manual monitoring.

D) Use Delta Lake for model storage and implement a custom monitoring script.

Show Answer & Explanation

Correct Answer: B

Explanation: MLflow provides robust model tracking capabilities, and integrating it with a CI/CD pipeline ensures automated and consistent deployment processes, making it easier to monitor and update applications. Unity Catalog is not used for model versioning, and options C and D lack automation and comprehensive monitoring.

Question 8

For a Databricks application, you need to integrate a generative AI model with a legacy system that uses a different data format. What is the best approach to ensure seamless integration?

A) Use Databricks SQL to convert data formats in real-time.

B) Implement a data transformation pipeline using Delta Lake.

C) Manually convert data before feeding it to the model.

D) Deploy a separate service to handle data conversion outside Databricks.

Show Answer & Explanation

Correct Answer: B

Explanation: Option B is correct because Delta Lake provides robust data transformation capabilities that can be used to convert and prepare data in a format compatible with the generative AI model, ensuring seamless integration. Option A may not handle complex transformations. Option C and D involve unnecessary manual effort and complexity.

Question 9

You are using LangChain in a Databricks notebook to create a conversational agent. What is a critical step to ensure that the agent maintains context across multiple user interactions?

A) Store each user query in a separate Delta Lake table.

B) Implement a session management system to track user interactions.

C) Use Unity Catalog to log all conversations.

D) Reset the conversation state after each interaction.

Show Answer & Explanation

Correct Answer: B

Explanation: Implementing a session management system is crucial for maintaining context across multiple user interactions, allowing the agent to provide coherent and contextually relevant responses. Storing queries in Delta Lake or logging with Unity Catalog does not inherently maintain conversational context.

Question 10

In developing a Generative AI application on Databricks, you need to implement robust access controls and audit trails for compliance. Which Databricks feature should you leverage?

A) Delta Lake

B) Unity Catalog

C) MLflow

D) Spark SQL

Show Answer & Explanation

Correct Answer: B

Explanation: Unity Catalog provides centralized access controls and audit trails, making it ideal for ensuring compliance in a Databricks environment. Delta Lake is more focused on data storage and management, MLflow is for managing the ML lifecycle, and Spark SQL is for querying data.

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About DATABRICKS-GAIE Certification

The DATABRICKS-GAIE certification validates your expertise in application development and other critical domains. Our comprehensive practice questions are carefully crafted to mirror the actual exam experience and help you identify knowledge gaps before test day.