Current Databricks-Generative-AI-Engineer-Associate Exam Dumps [2026] Complete Databricks Exam Smoothly [Q17-Q35]

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Current Databricks-Generative-AI-Engineer-Associate Exam Dumps [2026] Complete Databricks Exam Smoothly

Databricks-Generative-AI-Engineer-Associate Premium PDF & Test Engine Files with 75 Questions & Answers

NO.17 A Generative AI Engineer is tasked with deploying an application that takes advantage of a custom MLflow Pyfunc model to return some interim results.
How should they configure the endpoint to pass the secrets and credentials?

Use spark.conf.set ()

Pass variables using the Databricks Feature Store API

Pass the secrets in plain text

Add credentials using environment variables

NO.18 A Generative Al Engineer has already trained an LLM on Databricks and it is now ready to be deployed.
Which of the following steps correctly outlines the easiest process for deploying a model on Databricks?

Log the model as a pickle object, upload the object to Unity Catalog Volume, register it to Unity Catalog using MLflow, and start a serving endpoint

Log the model using MLflow during training, directly register the model to Unity Catalog using the MLflow API, and start a serving endpoint

Save the model along with its dependencies in a local directory, build the Docker image, and run the Docker container

Wrap the LLM’s prediction function into a Flask application and serve using Gunicorn

NO.19 A Generative Al Engineer is tasked with developing a RAG application that will help a small internal group of experts at their company answer specific questions, augmented by an internal knowledge base. They want the best possible quality in the answers, and neither latency nor throughput is a huge concern given that the user group is small and they’re willing to wait for the best answer. The topics are sensitive in nature and the data is highly confidential and so, due to regulatory requirements, none of the information is allowed to be transmitted to third parties.
Which model meets all the Generative Al Engineer’s needs in this situation?

Dolly 1.5B

OpenAI GPT-4

BGE-large

Llama2-70B

NO.20 A Generative Al Engineer is building a system which will answer questions on latest stock news articles.
Which will NOT help with ensuring the outputs are relevant to financial news?

Implement a comprehensive guardrail framework that includes policies for content filters tailored to the finance sector.

Increase the compute to improve processing speed of questions to allow greater relevancy analysis C Implement a profanity filter to screen out offensive language

Incorporate manual reviews to correct any problematic outputs prior to sending to the users

NO.21 Generative AI Engineer at an electronics company just deployed a RAG application for customers to ask questions about products that the company carries. However, they received feedback that the RAG response often returns information about an irrelevant product.
What can the engineer do to improve the relevance of the RAG’s response?

Assess the quality of the retrieved context

Implement caching for frequently asked questions

Use a different LLM to improve the generated response

Use a different semantic similarity search algorithm

NO.22 A Generative AI Engineer at an automotive company would like to build a question-answering chatbot to help customers answer specific questions about their vehicles. They have:
A catalog with hundreds of thousands of cars manufactured since the 1960s Historical searches with user queries and successful matches Descriptions of their own cars in multiple languages They have already selected an open-source LLM and created a test set of user queries. They need to discard techniques that will not help them build the chatbot. Which do they discard?

Setting chunk size to match the model’s context window to maximize coverage

Implementing metadata filtering based on car models and years

Fine-tuning an embedding model on automotive terminology

Adding few-shot examples for response generation

NO.23 A Generative AI Engineer is developing a chatbot designed to assist users with insurance-related queries. The chatbot is built on a large language model (LLM) and is conversational. However, to maintain the chatbot’s focus and to comply with company policy, it must not provide responses to questions about politics. Instead, when presented with political inquiries, the chatbot should respond with a standard message:
“Sorry, I cannot answer that. I am a chatbot that can only answer questions around insurance.” Which framework type should be implemented to solve this?

Safety Guardrail

Security Guardrail

Contextual Guardrail

Compliance Guardrail

NO.24 A Generative AI Engineer is tasked with deploying an application that takes advantage of a custom MLflow Pyfunc model to return some interim results.
How should they configure the endpoint to pass the secrets and credentials?

Use spark.conf.set ()

Pass variables using the Databricks Feature Store API

Add credentials using environment variables

Pass the secrets in plain text

NO.25 A Generative AI Engineer I using the code below to test setting up a vector store:

Assuming they intend to use Databricks managed embeddings with the default embedding model, what should be the next logical function call?

vsc.get_index()

vsc.create_delta_sync_index()

vsc.create_direct_access_index()

vsc.similarity_search()

NO.26 A Generative Al Engineer is setting up a Databricks Vector Search that will lookup news articles by topic within 10 days of the date specified An example query might be “Tell me about monster truck news around January 5th 1992”. They want to do this with the least amount of effort.
How can they set up their Vector Search index to support this use case?

Split articles by 10 day blocks and return the block closest to the query.

Include metadata columns for article date and topic to support metadata filtering.

pass the query directly to the vector search index and return the best articles.

Create separate indexes by topic and add a classifier model to appropriately pick the best index.

The task is to set up a Databricks Vector Search index for news articles, supporting queries like “monster truck news around January 5th, 1992,” with minimal effort. The index must filter by topic and a 10-day date range. Let’s evaluate the options.
* Option A: Split articles by 10-day blocks and return the block closest to the query
* Pre-splitting articles into 10-day blocks requires significant preprocessing and index management (e.g., one index per block). It’s effort-intensive and inflexible for dynamic date ranges.
* Databricks Reference:”Static partitioning increases setup complexity; metadata filtering is preferred”(“Databricks Vector Search Documentation”).
* Option B: Include metadata columns for article date and topic to support metadata filtering
* Adding date and topic as metadata in the Vector Search index allows dynamic filtering (e.g., date
± 5 days, topic = “monster truck”) at query time. This leverages Databricks’ built-in metadata filtering, minimizing setup effort.
* Databricks Reference:”Vector Search supports metadata filtering on columns like date or category for precise retrieval with minimal preprocessing”(“Vector Search Guide,” 2023).
* Option C: Pass the query directly to the vector search index and return the best articles
* Passing the full query (e.g., “Tell me about monster truck news around January 5th, 1992”) to Vector Search relies solely on embeddings, ignoring structured filtering for date and topic. This risks inaccurate results without explicit range logic.
* Databricks Reference:”Pure vector similarity may not handle temporal or categorical constraints effectively”(“Building LLM Applications with Databricks”).
* Option D: Create separate indexes by topic and add a classifier model to appropriately pick the best index
* Separate indexes per topic plus a classifier model adds significant complexity (index creation, model training, maintenance), far exceeding “least effort.” It’s overkill for this use case.
* Databricks Reference:”Multiple indexes increase overhead; single-index with metadata is simpler”(“Databricks Vector Search Documentation”).
Conclusion: Option B is the simplest and most effective solution, using metadata filtering in a single Vector Search index to handle date ranges and topics, aligning with Databricks’ emphasis on efficient, low-effort setups.

NO.27 A Generative Al Engineer is creating an LLM-based application. The documents for its retriever have been chunked to a maximum of 512 tokens each. The Generative Al Engineer knows that cost and latency are more important than quality for this application. They have several context length levels to choose from.
Which will fulfill their need?

context length 514; smallest model is 0.44GB and embedding dimension 768

context length 2048: smallest model is 11GB and embedding dimension 2560

context length 32768: smallest model is 14GB and embedding dimension 4096

context length 512: smallest model is 0.13GB and embedding dimension 384

NO.28 A Generative Al Engineer is building a system that will answer questions on currently unfolding news topics. As such, it pulls information from a variety of sources including articles and social media posts. They are concerned about toxic posts on social media causing toxic outputs from their system.
Which guardrail will limit toxic outputs?

Use only approved social media and news accounts to prevent unexpected toxic data from getting to the LLM.

Implement rate limiting

Reduce the amount of context Items the system will Include in consideration for its response.

Log all LLM system responses and perform a batch toxicity analysis monthly.

The system answers questions on unfolding news topics using articles and social media, with a concern about toxic outputs from toxic inputs. A guardrail must limit toxicity in the LLM’s responses. Let’s evaluate the options.
Option A: Use only approved social media and news accounts to prevent unexpected toxic data from getting to the LLM Curating input sources (e.g., verified accounts) reduces exposure to toxic content at the data ingestion stage, directly limiting toxic outputs. This is a proactive guardrail aligned with data quality control.
Databricks Reference: “Control input data quality to mitigate unwanted LLM behavior, such as toxicity” (“Building LLM Applications with Databricks,” 2023).
Option B: Implement rate limiting
Rate limiting controls request frequency, not content quality. It prevents overload but doesn’t address toxicity in social media inputs or outputs.
Databricks Reference: Rate limiting is for performance, not safety: “Use rate limits to manage compute load” (“Generative AI Cookbook”).
Option C: Reduce the amount of context items the system will include in consideration for its response Reducing context might limit exposure to some toxic items but risks losing relevant information, and it doesn’t specifically target toxicity. It’s an indirect, imprecise fix.
Databricks Reference: Context reduction is for efficiency, not safety: “Adjust context size based on performance needs” (“Databricks Generative AI Engineer Guide”).
Option D: Log all LLM system responses and perform a batch toxicity analysis monthly Logging and analyzing responses is reactive, identifying toxicity after it occurs rather than preventing it. Monthly analysis doesn’t limit real-time toxic outputs.
Databricks Reference: Monitoring is for auditing, not prevention: “Log outputs for post-hoc analysis, but use input filters for safety” (“Building LLM-Powered Applications”).
Conclusion: Option A is the most effective guardrail, proactively filtering toxic inputs from unverified sources, which aligns with Databricks’ emphasis on data quality as a primary safety mechanism for LLM systems.

NO.29 When developing an LLM application, it’s crucial to ensure that the data used for training the model complies with licensing requirements to avoid legal risks.
Which action is NOT appropriate to avoid legal risks?

Reach out to the data curators directly before you have started using the trained model to let them know.

Use any available data you personally created which is completely original and you can decide what license to use.

Only use data explicitly labeled with an open license and ensure the license terms are followed.

Reach out to the data curators directly after you have started using the trained model to let them know.

NO.30 A Generative AI Engineer has been reviewing issues with their company’s LLM-based question-answering assistant and has determined that a technique called prompt chaining could help alleviate some performance concerns. However, to suggest this to their team, they have to clearly explain how it works and how it can benefit their question-answering assistant. Which explanation do they communicate to the team?

It allows you to break down complex tasks into multiple independent subtasks. This enables the assistant to generate more comprehensive and accurate responses.

It allows you to reduce the latency of your applications. By having multiple chains participating in the response as a chain, you increase the rate at which the response is generated.

It allows you to decrease the effort involved in crafting a prompt. Chains make it possible to reuse prompt text across multiple different use cases.

It reduces the average cost of a typical request. Chains make more efficient use of the tokens produced to generate higher quality responses with fewer tokens.

NO.31 A small and cost-conscious startup in the cancer research field wants to build a RAG application using Foundation Model APIs.
Which strategy would allow the startup to build a good-quality RAG application while being cost-conscious and able to cater to customer needs?

Limit the number of relevant documents available for the RAG application to retrieve from

Pick a smaller LLM that is domain-specific

Limit the number of queries a customer can send per day

Use the largest LLM possible because that gives the best performance for any general queries

NO.32 A Generative AI Engineer has created a RAG application which can help employees retrieve answers from an internal knowledge base, such as Confluence pages or Google Drive. The prototype application is now working with some positive feedback from internal company testers. Now the Generative Al Engineer wants to formally evaluate the system’s performance and understand where to focus their efforts to further improve the system.
How should the Generative AI Engineer evaluate the system?

Use cosine similarity score to comprehensively evaluate the quality of the final generated answers.

Curate a dataset that can test the retrieval and generation components of the system separately. Use MLflow’s built in evaluation metrics to perform the evaluation on the retrieval and generation components.

Benchmark multiple LLMs with the same data and pick the best LLM for the job.

Use an LLM-as-a-judge to evaluate the quality of the final answers generated.

NO.33 A Generative Al Engineer is ready to deploy an LLM application written using Foundation Model APIs. They want to follow security best practices for production scenarios Which authentication method should they choose?

Use an access token belonging to service principals

Use a frequently rotated access token belonging to either a workspace user or a service principal

Use OAuth machine-to-machine authentication

Use an access token belonging to any workspace user

The task is to deploy an LLM application using Foundation Model APIs in a production environment while adhering to security best practices. Authentication is critical for securing access to Databricks resources, such as the Foundation Model API. Let’s evaluate the options based on Databricks’ security guidelines for production scenarios.
* Option A: Use an access token belonging to service principals
* Service principals are non-human identities designed for automated workflows and applications in Databricks. Using an access token tied to a service principal ensures that the authentication is scoped to the application, follows least-privilege principles (via role-based access control), and avoids reliance on individual user credentials. This is a security best practice for production deployments.
* Databricks Reference:”For production applications, use service principals with access tokens to authenticate securely, avoiding user-specific credentials”(“Databricks Security Best Practices,”
2023). Additionally, the “Foundation Model API Documentation” states:”Service principal tokens are recommended for programmatic access to Foundation Model APIs.”
* Option B: Use a frequently rotated access token belonging to either a workspace user or a service principal
* Frequent rotation enhances security by limiting token exposure, but tying the token to a workspace user introduces risks (e.g., user account changes, broader permissions). Including both user and service principal options dilutes the focus on application-specific security, making this less ideal than a service-principal-only approach. It also adds operational overhead without clear benefits over Option A.
* Databricks Reference:”While token rotation is a good practice, service principals are preferred over user accounts for application authentication”(“Managing Tokens in Databricks,” 2023).
* Option C: Use OAuth machine-to-machine authentication
* OAuth M2M (e.g., client credentials flow) is a secure method for application-to-service communication, often using service principals under the hood. However, Databricks’ Foundation Model API primarily supports personal access tokens (PATs) or service principal tokens over full OAuth flows for simplicity in production setups. OAuth M2M adds complexity (e.g., managing refresh tokens) without a clear advantage in this context.
* Databricks Reference:”OAuth is supported in Databricks, but service principal tokens are simpler and sufficient for most API-based workloads”(“Databricks Authentication Guide,” 2023).
* Option D: Use an access token belonging to any workspace user
* Using a user’s access token ties the application to an individual’s identity, violating security best practices. It risks exposure if the user leaves, changes roles, or has overly broad permissions, and it’s not scalable or auditable for production.
* Databricks Reference:”Avoid using personal user tokens for production applications due to security and governance concerns”(“Databricks Security Best Practices,” 2023).
Conclusion: Option A is the best choice, as it uses a service principal’s access token, aligning with Databricks’ security best practices for production LLM applications. It ensures secure, application-specific authentication with minimal complexity, as explicitly recommended for Foundation Model API deployments.

NO.34 A Generative AI Engineer is building a RAG application that will rely on context retrieved from source documents that are currently in PDF format. These PDFs can contain both text and images. They want to develop a solution using the least amount of lines of code.
Which Python package should be used to extract the text from the source documents?

flask

beautifulsoup

unstructured

numpy

NO.35 After changing the response generating LLM in a RAG pipeline from GPT-4 to a model with a shorter context length that the company self-hosts, the Generative AI Engineer is getting the following error:

What TWO solutions should the Generative AI Engineer implement without changing the response generating model? (Choose two.)

Use a smaller embedding model to generate

Reduce the maximum output tokens of the new model

Decrease the chunk size of embedded documents

Reduce the number of records retrieved from the vector database

Retrain the response generating model using ALiBi

Databricks Databricks-Generative-AI-Engineer-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal licensing requirements in this topic.
Topic 2	Data Preparation: Generative AI Engineers covers a chunking strategy for a given document structure and model constraints. The topic also focuses on filter extraneous content in source documents. Lastly, Generative AI Engineers also learn about extracting document content from provided source data and format.
Topic 3	Application Development: In this topic, Generative AI Engineers learn about tools needed to extract data, Langchain similar tools, and assessing responses to identify common issues. Moreover, the topic includes questions about adjusting an LLM’s response, LLM guardrails, and the best LLM based on the attributes of the application.

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Current Databricks-Generative-AI-Engineer-Associate Exam Dumps [2026] Complete Databricks Exam Smoothly [Q17-Q35]

Databricks Databricks-Generative-AI-Engineer-Associate Exam Syllabus Topics:

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