DatabricksFree level

Databricks Generative AI Engineer Associate exam dumps: 305 free Databricks Generative AI Engineer Associate practice questions

Free Databricks Generative AI Engineer Associate practice questions for the Databricks Certified Generative AI Engineer Associate exam, with the correct answer and a full explanation for every option. Read the first 10 below, browse all 305 by number, or take a timed practice exam.

Question bank last updated April 2025

Start timed practice exam Browse all 305 questions

Free Databricks Generative AI Engineer Associate practice questions

Questions 1 to 10 of 305

Pick an answer before you open the explanation. Each question also has its own page with a permalink.

Databricks Generative AI Engineer Associate Question 1

Select 3

You are designing a generative AI application on Databricks that uses a pre-trained large language model (LLM) to provide text summarization services for customer support tickets. The application needs to ensure scalability, low latency, and compliance with enterprise data governance policies. Which design considerations should you prioritize while building this application on Databricks?

A
Implement model serving with Databricks Model Serving to deploy the LLM for low-latency predictions.
B
Use Unity Catalog to ensure proper data lineage and access control for customer support ticket data.
C
Store customer support tickets in local file systems for faster processing.
D
Leverage Delta Lake for efficient storage and management of support ticket data with ACID transactions.
E
Avoid fine-tuning the pre-trained LLM and use it as-is to minimize model complexity.

Show answer and explanation

Correct answers: A, B, D

Explanation

To design a scalable and efficient generative AI application on Databricks, it is critical to prioritize model serving for low latency, use Unity Catalog for data governance, and leverage Delta Lake for efficient data management. These components ensure the application meets enterprise requirements for scalability, compliance, and performance.

A. Correct.
Correct: Databricks Model Serving provides a scalable and low-latency way to deploy and serve LLMs, which is crucial for real-time applications like text summarization.
B. Correct.
Correct: Unity Catalog ensures compliance with enterprise data governance policies by providing centralized data access control and lineage tracking.
C. Incorrect.
Incorrect: Storing data in local file systems does not align with scalability and enterprise data governance best practices. Databricks recommends cloud-based storage solutions like Delta Lake.
D. Correct.
Correct: Delta Lake enables efficient handling of large-scale data with ACID transactions, ensuring data integrity and scalability for analytics and AI workflows.
E. Incorrect.
Incorrect: While using the pre-trained LLM as-is may simplify the design, it is not a priority for this scenario. Fine-tuning may still be required to improve performance on domain-specific customer support data.

Databricks Generative AI Engineer Associate Question 2

Select 3

You are designing a generative AI application for real-time customer support within a Databricks environment. The application needs to provide accurate and contextually relevant responses while ensuring low latency. Which design considerations should you prioritize to achieve this goal?

A
Use a lightweight pre-trained model fine-tuned on domain-specific data.
B
Deploy the generative AI model to a high-latency batch processing pipeline.
C
Implement a caching mechanism to store frequently asked questions and their responses.
D
Incorporate prompt engineering to improve the response quality of the model.
E
Avoid using any optimization techniques to ensure model output remains unaltered.

Show answer and explanation

Correct answers: A, C, D

Explanation

To design a real-time customer support application with generative AI, prioritizing a lightweight fine-tuned model, implementing caching, and leveraging prompt engineering are critical. These strategies collectively ensure accurate, contextually relevant responses while maintaining low latency. Avoiding optimizations or using high-latency architectures would hinder the application's effectiveness in a real-time scenario.

A. Correct.
Using a lightweight pre-trained model fine-tuned on domain-specific data ensures the application delivers accurate and relevant responses while maintaining low latency. Large models may introduce unnecessary latency and require more computational resources.
B. Incorrect.
Deploying the generative AI model to a high-latency batch processing pipeline is unsuitable for real-time use cases, as it contradicts the requirement for low latency.
C. Correct.
Implementing a caching mechanism can significantly reduce response time for frequently asked questions, aligning with the goal of low latency.
D. Correct.
Incorporating prompt engineering improves the quality of responses by guiding the model more effectively, which is essential for providing contextually relevant answers.
E. Incorrect.
Avoiding optimization techniques is counterproductive, as optimizations (e.g., model distillation, prompt engineering) are often necessary to balance performance and accuracy in production environments.

Databricks Generative AI Engineer Associate Question 3

Select 3

You are tasked with designing a generative AI application on Databricks that will generate personalized product recommendations based on user preferences and purchase history. Which critical considerations should you prioritize during the design phase to ensure scalability, accuracy, and responsible AI deployment?

A
Ensure the training dataset includes diverse user demographics to minimize bias in recommendations.
B
Design the application to leverage only pre-trained models without fine-tuning to save on computational resources.
C
Implement monitoring mechanisms to detect and address data drift in user preferences over time.
D
Incorporate explainability techniques to provide insights into why specific recommendations are generated.
E
Focus solely on optimizing latency and throughput while ignoring ethical AI principles.

Show answer and explanation

Correct answers: A, C, D

Explanation

Designing a generative AI application requires balancing performance, accuracy, and ethical considerations. Prioritizing diverse data, monitoring for data drift, and incorporating explainability ensures the application remains fair, scalable, and trustworthy. Ignoring these principles or relying solely on pre-trained models without fine-tuning can lead to suboptimal outcomes.

A. Correct.
Including diverse user demographics in the training dataset is critical to minimizing bias and ensuring fairness in personalized recommendations.
B. Incorrect.
Using only pre-trained models without fine-tuning may reduce computational costs, but it could result in suboptimal accuracy for the specific use case, especially when user preferences are domain-specific.
C. Correct.
Monitoring for data drift is essential for maintaining model accuracy over time, as user preferences and purchase patterns can evolve.
D. Correct.
Explainability is important for responsible AI deployment, as it helps build trust with users by providing transparency into the recommendation process.
E. Incorrect.
While optimizing latency and throughput is important for performance, ignoring ethical AI principles can lead to biased or harmful recommendations, undermining the application's reliability and user trust.

Databricks Generative AI Engineer Associate Question 4

Select 3

You are tasked with designing a generative AI application that uses a fine-tuned large language model (LLM) to assist customer service agents by generating real-time responses. The application will be deployed on Databricks. Which considerations are most critical to ensuring the design is scalable, efficient, and secure?

A
Use a feature store to manage and serve the fine-tuned model's embeddings for faster inference.
B
Implement token-level filtering to prevent sensitive information from being included in generated responses.
C
Design the application to use Databricks' MLflow integration for experiment tracking and model versioning.
D
Optimize the application for batch inference rather than real-time inference to reduce infrastructure costs.
E
Ensure the Databricks workspace is configured with role-based access control (RBAC) to limit unauthorized access to the model and data.

Show answer and explanation

Correct answers: B, C, E

Explanation

When designing applications on Databricks, considerations for scalability, efficiency, and security are crucial. For this scenario, token-level filtering addresses privacy concerns, MLflow integration ensures robust model management, and RBAC enhances security. While cost optimization is important, batch inference is not suitable for real-time applications, and feature stores are unrelated to serving LLM embeddings.

A. Incorrect.
Incorrect: A feature store is used to manage and serve features for training and inference, not embeddings of a fine-tuned model. This is not directly relevant to the design of a scalable and secure LLM-based application.
B. Correct.
Correct: Token-level filtering is critical for ensuring the application adheres to data security and privacy requirements by preventing sensitive information from being included in responses.
C. Correct.
Correct: MLflow integration is essential for managing the lifecycle of machine learning models, including tracking experiments and maintaining model versions, which is key to a reliable and scalable application.
D. Incorrect.
Incorrect: Optimizing for batch inference would reduce costs but is unsuitable for a real-time application where low-latency responses are critical.
E. Correct.
Correct: Configuring role-based access control (RBAC) in the Databricks workspace ensures that only authorized users can access sensitive resources like the model and data, enhancing security.

Databricks Generative AI Engineer Associate Question 5

Select 3

You are designing a generative AI application on Databricks that generates personalized product recommendations for users. The application needs to process user behavior data in real-time, utilize a fine-tuned large language model (LLM), and serve recommendations with low latency. Which components should you include in your design to meet these requirements?

A
A Databricks Delta Live Tables pipeline to process and prepare real-time user behavior data
B
A fine-tuned LLM hosted in a Databricks Model Serving endpoint
C
A batch processing job to periodically update recommendations
D
A distributed feature store to manage and serve user features for the model
E
A Databricks SQL endpoint to serve recommendations directly to users

Show answer and explanation

Correct answers: A, B, D

Explanation

To design a low-latency generative AI application for personalized recommendations, you need to process user behavior data in real-time using Delta Live Tables, host the fine-tuned LLM on a Databricks Model Serving endpoint for efficient inference, and use a distributed feature store to manage and serve user-specific features. Batch processing and SQL endpoints are not suitable for the real-time and low-latency requirements specified in this scenario.

A. Correct.
This is correct. Databricks Delta Live Tables can process real-time data streams, making it suitable for handling user behavior data in real-time.
B. Correct.
This is correct. Hosting a fine-tuned LLM on a Databricks Model Serving endpoint allows for low-latency inference, which is critical for serving recommendations in real-time.
C. Incorrect.
This is incorrect. While batch jobs can update recommendations, they do not meet the real-time processing requirement outlined in the scenario.
D. Correct.
This is correct. A distributed feature store enables real-time management and retrieval of user features, which is necessary for generating personalized recommendations efficiently.
E. Incorrect.
This is incorrect. While Databricks SQL is useful for querying structured data, it is not optimized for real-time recommendations or LLM inference.

Databricks Generative AI Engineer Associate Question 6

Select 3

A company wants to design a generative AI application using Databricks that generates product descriptions based on customer reviews. The application must scale for high user demand, ensure low latency, and maintain cost efficiency. Which combination of strategies would be most appropriate for designing this application?

A
Use a pre-trained large language model (LLM) hosted on Databricks and fine-tune it with domain-specific data.
B
Deploy the model on a single-node cluster to minimize infrastructure costs.
C
Leverage Databricks AutoML to perform hyperparameter tuning for the fine-tuned model.
D
Implement model serving on Databricks to enable real-time inference with scalable endpoints.
E
Store customer reviews in a Delta Lake table and integrate it with Databricks Feature Store for streamlined data preprocessing.

Show answer and explanation

Correct answers: A, D, E

Explanation

The correct strategies involve fine-tuning a pre-trained LLM to adapt it to the domain, using Databricks model serving for scalable real-time inference, and leveraging Delta Lake and Databricks Feature Store for efficient data preparation. Together, these approaches address the requirements of scalability, low latency, and cost efficiency.

A. Correct.
Using a pre-trained LLM and fine-tuning it with domain-specific data allows the model to better understand the product domain while saving time and resources compared to training from scratch. This is a best practice in generative AI application design.
B. Incorrect.
Deploying the model on a single-node cluster may reduce costs but will fail to meet the application's scaling and low-latency requirements. It is not suitable for high user demand.
C. Incorrect.
While Databricks AutoML is useful for tasks like classification or regression, in this case, it is not necessary for fine-tuning a pre-trained LLM. Fine-tuning can be done using other specialized libraries and techniques.
D. Correct.
Model serving on Databricks provides scalable endpoints for real-time inference, which is critical for handling high user demand and ensuring low latency.
E. Correct.
Using Delta Lake to store customer reviews and integrating with Databricks Feature Store allows for efficient data preprocessing and feature engineering, which is essential for a robust generative AI application.

Databricks Generative AI Engineer Associate Question 7

Single answer

You are designing a prompt for a generative AI model in Databricks to extract specific details from customer reviews, including the customer's name, product purchased, and a sentiment score formatted as JSON. Which of the following prompts is most likely to elicit a correctly formatted response?

A
Extract the details from the customer review and return the information in any format.
B
Please summarize the review.
C
Extract the customer's name, product purchased, and sentiment score from the review. Format the response as: {"name": "", "product": "", "sentiment": }.
D
Identify key entities in the review and provide a sentiment analysis score.

Show answer and explanation

Correct answer: C

Explanation

When designing prompts for generative AI models, it is important to clearly specify both the information required and the format for the response. Option 3 is the only prompt that provides both the details to extract and the exact JSON format, ensuring a structured and accurate response.

A. Incorrect.
This option does not specify the required format for the response, making it unlikely to elicit the desired JSON output.
B. Incorrect.
Summarizing the review does not fulfill the requirement to extract and format the specific details as JSON.
C. Correct.
This option explicitly specifies the required details to extract and provides the exact JSON format for the response, making it the best choice to elicit the desired output.
D. Incorrect.
While this option asks for key entities and sentiment analysis, it does not specify a structured format for the output, which may lead to unstructured responses.

Databricks Generative AI Engineer Associate Question 8

Single answer

You are working on a Databricks project to generate JSON-formatted responses from a generative AI model. The output must include a status field (either 'success' or 'failure'), a message field describing the result, and a data field containing relevant information. Which of the following prompts would best elicit this specifically formatted response?

A
Generate a JSON response with any information relevant to the query.
B
Provide a response in JSON format with fields status, message, and data. Ensure status is either 'success' or 'failure', message explains the result, and data contains relevant information.
C
Return a JSON object containing a summary of the query results.
D
Write a response in plain text with a summary and the relevant data.

Show answer and explanation

Correct answer: B

Explanation

To elicit a specifically formatted response from a generative AI model, the prompt must clearly define the desired structure and required fields. Option 2 provides the most explicit guidance, ensuring the model generates a JSON response with the required status, message, and data fields, while the other options are either too vague or do not meet the requirements.

A. Incorrect.
This prompt is vague and does not specify the required structure or fields. It may result in inconsistent or incomplete responses.
B. Correct.
This prompt is explicit about the required JSON format and fields, ensuring the model generates a response that meets the specified requirements.
C. Incorrect.
This prompt requests a JSON object but does not specify the required fields or structure, which may lead to unexpected or incomplete outputs.
D. Incorrect.
This prompt asks for a plain text response, which does not align with the requirement for a JSON-formatted response.

Databricks Generative AI Engineer Associate Question 9

Single answer

You are designing a prompt for a generative AI model to provide a JSON response containing a user's name, age, and a list of their favorite hobbies. Which of the following prompts is most likely to elicit a correctly formatted JSON response?

A
Generate a response about the user's details, including their name, age, and hobbies.
B
Provide a structured JSON object with the following keys: 'name' (string), 'age' (integer), and 'hobbies' (list of strings). Ensure the response is valid JSON.
C
Tell me about the user's name, age, and hobbies in a complete sentence.
D
Write a list of key details about the user, including their name, age, and hobbies, separated by commas.

Show answer and explanation

Correct answer: B

Explanation

To ensure a generative AI model produces a specifically formatted response, the prompt should clearly define the desired structure, including the format (e.g., JSON), required keys, and data types. Option 2 accomplishes this effectively by explicitly describing the structure and format, making it the correct choice.

A. Incorrect.
This prompt is too generic and does not specify the desired output format, making it unlikely to elicit a well-formatted JSON response.
B. Correct.
This prompt explicitly requests a structured JSON object with specific keys and data types, ensuring the model understands the required format and generates a valid JSON response.
C. Incorrect.
This prompt asks for a response in sentence format rather than JSON, so it is unlikely to result in the desired output structure.
D. Incorrect.
This prompt does not specify JSON as the required format, so the response is likely to be a plain text list rather than a valid JSON object.

Databricks Generative AI Engineer Associate Question 10

Single answer

You are designing a prompt for a generative AI model that summarizes customer reviews into a structured format containing a 'Summary', 'Positive Aspects', and 'Negative Aspects' sections. Which of the following prompts is most likely to elicit the desired structured response?

A
Summarize the customer reviews.
B
Summarize the customer reviews into three sections: 'Summary', 'Positive Aspects', and 'Negative Aspects'. Use clear headings for each section.
C
Generate a summary of the customer reviews and list the good and bad points.
D
Write a detailed explanation of the customer reviews.

Show answer and explanation

Correct answer: B

Explanation

The correct prompt must explicitly define the required output structure and format, including any specific headings or sections. This ensures that the generative AI model understands the expectations and produces the desired response. Option 2 provides this level of specificity and clarity, making it the best choice.

A. Incorrect.
This prompt is too vague and does not specify the structured format required, making it unlikely to elicit the desired response.
B. Correct.
This prompt clearly specifies the desired structure, including the section headings, which makes it most likely to produce a response in the required format.
C. Incorrect.
This prompt is somewhat clearer than the first option, but it does not explicitly mention the need for specific section headings, which may lead to inconsistent formatting.
D. Incorrect.
This prompt is unrelated to the structured format and is more likely to produce a lengthy, unstructured explanation rather than the desired output.

Timed practice exam

Take a Databricks Generative AI Engineer Associate practice test under exam conditions

45 questions in 90 minutes, drawn from this bank, with a score report and a per-question review when you finish.

Start timed exam

Databricks Generative AI Engineer Associate practice questions 1 to 100 of 305

Every question has a page with the answer and explanation. Numbers are stable, so you can bookmark or share them. The bank is split into 4 pages of up to 100 questions.

Databricks Generative AI Engineer Associate exam dumps FAQ

Are these Databricks Generative AI Engineer Associate dumps real exam questions?

No. These are original practice questions written to the Databricks Certified Generative AI Engineer Associate exam objectives, not questions copied from a live exam. Memorising leaked questions violates Databricks's candidate agreement and stops working the moment the question pool rotates. Use this bank to check your understanding of each domain and to find the topics you still need to study.

How many Databricks Generative AI Engineer Associate practice questions are there?

305 questions, each with the correct answer, an explanation of the answer, and a note on why every other option is wrong. The first 10 are on this page and every question has its own page linked below.

Are the Databricks Generative AI Engineer Associate exam dumps free?

Yes. Every question, answer and explanation on this page and the linked question pages is free to read without an account. A free HydraNode account adds timed practice exams, scoring and progress tracking across attempts.

How do I take a timed Databricks Generative AI Engineer Associate practice test?

Sign in and start the Databricks Certified Generative AI Engineer Associate exam on HydraNode. A session gives you 45 questions drawn from this bank in 90 minutes, then a score report with a per-question review.