Databricks Certified Generative AI Engineer Associate - Databricks-Generative-AI-Engineer-Associate Exam Practice Test

Question 1

A Generative Al Engineer is responsible for developing a chatbot to enable their company's internal HelpDesk Call Center team to more quickly find related tickets and provide resolution. While creating the GenAI application work breakdown tasks for this project, they realize they need to start planning which data sources (either Unity Catalog volume or Delta table) they could choose for this application. They have collected several candidate data sources for consideration:
call_rep_history: a Delta table with primary keys representative_id, call_id. This table is maintained to calculate representatives' call resolution from fields call_duration and call start_time.
transcript Volume: a Unity Catalog Volume of all recordings as a *.wav files, but also a text transcript as *.txt files.
call_cust_history: a Delta table with primary keys customer_id, cal1_id. This table is maintained to calculate how much internal customers use the HelpDesk to make sure that the charge back model is consistent with actual service use.
call_detail: a Delta table that includes a snapshot of all call details updated hourly. It includes root_cause and resolution fields, but those fields may be empty for calls that are still active.
maintenance_schedule - a Delta table that includes a listing of both HelpDesk application outages as well as planned upcoming maintenance downtimes.
They need sources that could add context to best identify ticket root cause and resolution.
Which TWO sources do that? (Choose two.)

A. call_rep_history

B. call_detail

C. transcript Volume

D. call_cust_history

E. maintenance_schedule

Correct Answer: B,C

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Question 2

An AI developer team wants to fine-tune an open-weight model to have exceptional performance on a code generation use case. They are trying to choose the best model to start with. They want to minimize model hosting costs and are using Hugging Face model cards and spaces to explore models. Which TWO model attributes and metrics should the team focus on to make their selection?

A. Big Code Models Leaderboard

B. Number of model downloads last month

C. MTEB Leaderboard

D. Number of model parameters

E. Chatbot Arena Leaderboard

Correct Answer: A,D

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Question 3

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

A. Pandas

B. LangChain

C. TensorFlow

D. PySpark

Correct Answer: B

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Question 4

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?

A. 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

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

Correct Answer: B

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Question 5

A Generative AI Engineer just deployed an LLM application at a digital marketing company that assists with answering customer service inquiries.
Which metric should they monitor for their customer service LLM application in production?

A. Final perplexity scores for the training of the model

B. Energy usage per query

C. Number of customer inquiries processed per unit of time

D. HuggingFace Leaderboard values for the base LLM

Correct Answer: C

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Question 6

A Generative AI Engineer developed an LLM application using the provisioned throughput Foundation Model API. Now that the application is ready to be deployed, they realize their volume of requests are not sufficiently high enough to create their own provisioned throughput endpoint. They want to choose a strategy that ensures the best cost-effectiveness for their application.
What strategy should the Generative AI Engineer use?

A. Throttle the incoming batch of requests manually to avoid rate limiting issues

B. Switch to using External Models instead

C. Change to a model with a fewer number of parameters in order to reduce hardware constraint issues

D. Deploy the model using pay-per-token throughput as it comes with cost guarantees

Correct Answer: D

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Question 7

A Generative Al Engineer needs to design an LLM pipeline to conduct multi-stage reasoning that leverages external tools. To be effective at this, the LLM will need to plan and adapt actions while performing complex reasoning tasks.
Which approach will do this?

A. Tram the LLM to generate a single, comprehensive response without interacting with any external tools, relying solely on its pre-trained knowledge.

B. Implement a framework like ReAct which allows the LLM to generate reasoning traces and perform task-specific actions that leverage external tools if necessary.

C. Use a Chain-of-Thought (CoT) prompting technique to guide the LLM through a series of reasoning steps, then manually input the results from external tools for the final answer.

D. Encourage the LLM to make multiple API calls in sequence without planning or structuring the calls, allowing the LLM to decide when and how to use external tools spontaneously.

Correct Answer: B

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Question 8

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?

A. Assess the quality of the retrieved context

B. Implement caching for frequently asked questions

C. Use a different semantic similarity search algorithm

D. Use a different LLM to improve the generated response

Correct Answer: A

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Question 9

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?

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

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

Correct Answer: C

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Question 10

A Generative AI Engineer wants to build an LLM-based solution to help a restaurant improve its online customer experience with bookings by automatically handling common customer inquiries. The goal of the solution is to minimize escalations to human intervention and phone calls while maintaining a personalized interaction. To design the solution, the Generative AI Engineer needs to define the input data to the LLM and the task it should perform.
Which input/output pair will support their goal?

A. Input: Online chat logs; Output: Group the chat logs by users, followed by summarizing each user's interactions

B. Input: Online chat logs; Output: Cancellation options

C. Input: Customer reviews; Output: Classify review sentiment

D. Input: Online chat logs; Output: Buttons that represent choices for booking details

Correct Answer: D

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Databricks Certified Generative AI Engineer Associate - Databricks-Generative-AI-Engineer-Associate Exam Practice Test

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