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Databricks Certified Generative AI Engineer Associate Question and Answers

Databricks Certified Generative AI Engineer Associate

Last Update Feb 14, 2025
Total Questions : 45

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Questions 1

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?

Options:

A.  

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

B.  

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

C.  

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

D.  

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

Discussion 0
Questions 2

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?

Options:

A.  

Use spark.conf.set ()

B.  

Pass variables using the Databricks Feature Store API

C.  

Add credentials using environment variables

D.  

Pass the secrets in plain text

Discussion 0
Questions 3

A Generative AI Engineer is developing an LLM application that users can use to generate personalized birthday poems based on their names.

Which technique would be most effective in safeguarding the application, given the potential for malicious user inputs?

Options:

A.  

Implement a safety filter that detects any harmful inputs and ask the LLM to respond that it is unable to assist

B.  

Reduce the time that the users can interact with the LLM

C.  

Ask the LLM to remind the user that the input is malicious but continue the conversation with the user

D.  

Increase the amount of compute that powers the LLM to process input faster

Discussion 0
Questions 4

A Generative AI Engineer is building an LLM to generate article summaries in the form of a type of poem, such as a haiku, given the article content. However, the initial output from the LLM does not match the desired tone or style.

Which approach will NOT improve the LLM’s response to achieve the desired response?

Options:

A.  

Provide the LLM with a prompt that explicitly instructs it to generate text in the desired tone and style

B.  

Use a neutralizer to normalize the tone and style of the underlying documents

C.  

Include few-shot examples in the prompt to the LLM

D.  

Fine-tune the LLM on a dataset of desired tone and style

Discussion 0
Questions 5

A Generative AI Engineer is designing a chatbot for a gaming company that aims to engage users on its platform while its users play online video games.

Which metric would help them increase user engagement and retention for their platform?

Options:

A.  

Randomness

B.  

Diversity of responses

C.  

Lack of relevance

D.  

Repetition of responses

Discussion 0
Questions 6

A Generative AI Engineer has been asked to design an LLM-based application that accomplishes the following business objective: answer employee HR questions using HR PDF documentation.

Which set of high level tasks should the Generative AI Engineer's system perform?

Options:

A.  

Calculate averaged embeddings for each HR document, compare embeddings to user query to find the best document. Pass the best document with the user query into an LLM with a large context window to generate a response to the employee.

B.  

Use an LLM to summarize HR documentation. Provide summaries of documentation and user query into an LLM with a large context window to generate a response to the user.

C.  

Create an interaction matrix of historical employee questions and HR documentation. Use ALS to factorize the matrix and create embeddings. Calculate the embeddings of new queries and use them to find the best HR documentation. Use an LLM to generate a response to the employee question based upon the documentation retrieved.

D.  

Split HR documentation into chunks and embed into a vector store. Use the employee question to retrieve best matched chunks of documentation, and use the LLM to generate a response to the employee based upon the documentation retrieved.

Discussion 0
Questions 7

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?

Options:

A.  

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

B.  

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

C.  

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

D.  

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

Discussion 0
Questions 8

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?

Options:

A.  

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

B.  

Increase the compute to improve processing speed of questions to allow greater relevancy analysis

C Implement a profanity filter to screen out offensive language

C.  

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

Discussion 0
Questions 9

A Generative AI Engineer has been asked to build an LLM-based question-answering application. The application should take into account new documents that are frequently published. The engineer wants to build this application with the least cost and least development effort and have it operate at the lowest cost possible.

Which combination of chaining components and configuration meets these requirements?

Options:

A.  

For the application a prompt, a retriever, and an LLM are required. The retriever output is inserted into the prompt which is given to the LLM to generate answers.

B.  

The LLM needs to be frequently with the new documents in order to provide most up-to-date answers.

C.  

For the question-answering application, prompt engineering and an LLM are required to generate answers.

D.  

For the application a prompt, an agent and a fine-tuned LLM are required. The agent is used by the LLM to retrieve relevant content that is inserted into the prompt which is given to the LLM to generate answers.

Discussion 0
Questions 10

A Generative Al Engineer is creating an LLM system that will retrieve news articles from the year 1918 and related to a user's query and summarize them. The engineer has noticed that the summaries are generated well but often also include an explanation of how the summary was generated, which is undesirable.

Which change could the Generative Al Engineer perform to mitigate this issue?

Options:

A.  

Split the LLM output by newline characters to truncate away the summarization explanation.

B.  

Tune the chunk size of news articles or experiment with different embedding models.

C.  

Revisit their document ingestion logic, ensuring that the news articles are being ingested properly.

D.  

Provide few shot examples of desired output format to the system and/or user prompt.

Discussion 0
Questions 11

A Generative Al Engineer has successfully ingested unstructured documents and chunked them by document sections. They would like to store the chunks in a Vector Search index. The current format of the dataframe has two columns: (i) original document file name (ii) an array of text chunks for each document.

What is the most performant way to store this dataframe?

Options:

A.  

Split the data into train and test set, create a unique identifier for each document, then save to a Delta table

B.  

Flatten the dataframe to one chunk per row, create a unique identifier for each row, and save to a Delta table

C.  

First create a unique identifier for each document, then save to a Delta table

D.  

Store each chunk as an independent JSON file in Unity Catalog Volume. For each JSON file, the key is the document section name and the value is the array of text chunks for that section

Discussion 0
Questions 12

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?

Options:

A.  

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

B.  

Pick a smaller LLM that is domain-specific

C.  

Limit the number of queries a customer can send per day

D.  

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

Discussion 0
Questions 13

A Generative AI Engineer is testing a simple prompt template in LangChain using the code below, but is getting an error.

Assuming the API key was properly defined, what change does the Generative AI Engineer need to make to fix their chain?

A)

B)

C)

D)

Options:

A.  

Option A

B.  

Option B

C.  

Option C

D.  

Option D

Discussion 0