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Databricks-Generative-AI-Engineer-Associate Exam Dumps - Databricks Generative AI Engineer Questions and Answers

Question # 4

A Generative AI Engineer is designing an LLM-powered live sports commentary platform. The platform provides real-time updates and LLM-generated analyses for any users who would like to have live summaries, rather than reading a series of potentially outdated news articles.

Which tool below will give the platform access to real-time data for generating game analyses based on the latest game scores?

Options:

A.

DatabrickslQ

B.

Foundation Model APIs

C.

Feature Serving

D.

AutoML

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Question # 5

A Generative AI Engineer has a provisioned throughput model serving endpoint as part of a RAG application and would like to monitor the serving endpoint’s incoming requests and outgoing responses. The current approach is to include a micro-service in between the endpoint and the user interface to write logs to a remote server.

Which Databricks feature should they use instead which will perform the same task?

Options:

A.

Vector Search

B.

Lakeview

C.

DBSQL

D.

Inference Tables

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Question # 6

A Generative AI Engineer is creating an LLM-powered application that will need access to up-to-date news articles and stock prices.

The design requires the use of stock prices which are stored in Delta tables and finding the latest relevant news articles by searching the internet.

How should the Generative AI Engineer architect their LLM system?

Options:

A.

Use an LLM to summarize the latest news articles and lookup stock tickers from the summaries to find stock prices.

B.

Query the Delta table for volatile stock prices and use an LLM to generate a search query to investigate potential causes of the stock volatility.

C.

Download and store news articles and stock price information in a vector store. Use a RAG architecture to retrieve and generate at runtime.

D.

Create an agent with tools for SQL querying of Delta tables and web searching, provide retrieved values to an LLM for generation of response.

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Question # 7

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.

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Question # 8

What is the most suitable library for building a multi-step LLM-based workflow?

Options:

A.

Pandas

B.

TensorFlow

C.

PySpark

D.

LangChain

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Question # 9

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

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Question # 10

A Generative AI Engineer is designing a RAG application for answering user questions on technical regulations as they learn a new sport.

What are the steps needed to build this RAG application and deploy it?

Options:

A.

Ingest documents from a source –> Index the documents and saves to Vector Search –> User submits queries against an LLM –> LLM retrieves relevant documents –> Evaluate model –> LLM generates a response –> Deploy it using Model Serving

B.

Ingest documents from a source –> Index the documents and save to Vector Search –> User submits queries against an LLM –> LLM retrieves relevant documents –> LLM generates a response -> Evaluate model –> Deploy it using Model Serving

C.

Ingest documents from a source –> Index the documents and save to Vector Search –> Evaluate model –> Deploy it using Model Serving

D.

User submits queries against an LLM –> Ingest documents from a source –> Index the documents and save to Vector Search –> LLM retrieves relevant documents –> LLM generates a response –> Evaluate model –> Deploy it using Model Serving

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Question # 11

A Generative Al Engineer is using an LLM to classify species of edible mushrooms based on text descriptions of certain features. The model is returning accurate responses in testing and the Generative Al Engineer is confident they have the correct list of possible labels, but the output frequently contains additional reasoning in the answer when the Generative Al Engineer only wants to return the label with no additional text.

Which action should they take to elicit the desired behavior from this LLM?

Options:

A.

Use few snot prompting to instruct the model on expected output format

B.

Use zero shot prompting to instruct the model on expected output format

C.

Use zero shot chain-of-thought prompting to prevent a verbose output format

D.

Use a system prompt to instruct the model to be succinct in its answer

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Question # 12

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?

Options:

A.

Assess the quality of the retrieved context

B.

Implement caching for frequently asked questions

C.

Use a different LLM to improve the generated response

D.

Use a different semantic similarity search algorithm

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Question # 13

A Generative AI Engineer is building a Generative AI system that suggests the best matched employee team member to newly scoped projects. The team member is selected from a very large team. The match should be based upon project date availability and how well their employee profile matches the project scope. Both the employee profile and project scope are unstructured text.

How should the Generative Al Engineer architect their system?

Options:

A.

Create a tool for finding available team members given project dates. Embed all project scopes into a vector store, perform a retrieval using team member profiles to find the best team member.

B.

Create a tool for finding team member availability given project dates, and another tool that uses an LLM to extract keywords from project scopes. Iterate through available team members’ profiles and perform keyword matching to find the best available team member.

C.

Create a tool to find available team members given project dates. Create a second tool that can calculate a similarity score for a combination of team member profile and the project scope. Iterate through the team members and rank by best score to select a team member.

D.

Create a tool for finding available team members given project dates. Embed team profiles into a vector store and use the project scope and filtering to perform retrieval to find the available best matched team members.

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Exam Name: Databricks Certified Generative AI Engineer Associate
Last Update: Apr 2, 2025
Questions: 61
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