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Databricks-Generative-AI-Engineer-Associate Databricks Certified Generative AI Engineer Associate Question and Answers

Question # 4

A Generative Al Engineer is tasked with improving the RAG quality by addressing its inflammatory outputs.

Which action would be most effective in mitigating the problem of offensive text outputs?

A.

Increase the frequency of upstream data updates

B.

Inform the user of the expected RAG behavior

C.

Restrict access to the data sources to a limited number of users

D.

Curate upstream data properly that includes manual review before it is fed into the RAG system

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Question # 5

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

A.

Pandas

B.

TensorFlow

C.

PySpark

D.

LangChain

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Question # 6

A Generative Al Engineer is building a RAG application that answers questions about internal documents for the company SnoPen AI.

The source documents may contain a significant amount of irrelevant content, such as advertisements, sports news, or entertainment news, or content about other companies.

Which approach is advisable when building a RAG application to achieve this goal of filtering irrelevant information?

A.

Keep all articles because the RAG application needs to understand non-company content to avoid answering questions about them.

B.

Include in the system prompt that any information it sees will be about SnoPenAI, even if no data filtering is performed.

C.

Include in the system prompt that the application is not supposed to answer any questions unrelated to SnoPen Al.

D.

Consolidate all SnoPen AI related documents into a single chunk in the vector database.

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Question # 7

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LLM-generated analyses for any users who would like to have live summaries, rather than reading a series of potentially outdated news articles.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?

A.

DatabrickslQ

B.

Foundation Model APIs

C.

Feature Serving

D.

AutoML

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Question # 8

Which TWO chain components are required for building a basic LLM-enabled chat application that includes conversational capabilities, knowledge retrieval, and contextual memory?

A.

(Q)

B.

Vector Stores

C.

Conversation Buffer Memory

D.

External tools

E.

Chat loaders

F.

React Components

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Question # 9

A Generative Al Engineer at an automotive company would like to build a question-answering chatbot for customers to inquire about their vehicles. They have a database containing various documents of different vehicle makes, their hardware parts, and common maintenance information.

Which of the following components will NOT be useful in building such a chatbot?

A.

Response-generating LLM

B.

Invite users to submit long, rather than concise, questions

C.

Vector database

D.

Embedding model

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Question # 10

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.

Number of customer inquiries processed per unit of time

B.

Energy usage per query

C.

Final perplexity scores for the training of the model

D.

HuggingFace Leaderboard values for the base LLM

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Question # 11

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?

A.

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

B.

Implement rate limiting

C.

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

D.

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

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Question # 12

A Generative Al Engineer has created a RAG application to look up answers to questions about a series of fantasy novels that are being asked on the author’s web forum. The fantasy novel texts are chunked and embedded into a vector store with metadata (page number, chapter number, book title), retrieved with the user’s query, and provided to an LLM for response generation. The Generative AI Engineer used their intuition to pick the chunking strategy and associated configurations but now wants to more methodically choose the best values.

Which TWO strategies should the Generative AI Engineer take to optimize their chunking strategy and parameters? (Choose two.)

A.

Change embedding models and compare performance.

B.

Add a classifier for user queries that predicts which book will best contain the answer. Use this to filter retrieval.

C.

Choose an appropriate evaluation metric (such as recall or NDCG) and experiment with changes in the chunking strategy, such as splitting chunks by paragraphs or chapters.

Choose the strategy that gives the best performance metric.

D.

Pass known questions and best answers to an LLM and instruct the LLM to provide the best token count. Use a summary statistic (mean, median, etc.) of the best token counts to choose chunk size.

E.

Create an LLM-as-a-judge metric to evaluate how well previous questions are answered by the most appropriate chunk. Optimize the chunking parameters based upon the values of the metric.

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Question # 13

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?

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

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Question # 14

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?

A.

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

B.

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

C.

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

D.

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

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Question # 15

A Generative Al Engineer is working with a retail company that wants to enhance its customer experience by automatically handling common customer inquiries. They are working on an LLM-powered Al solution that should improve response times while maintaining a personalized interaction. They want to define the appropriate input and LLM task to do this.

Which input/output pair will do this?

A.

Input: Customer reviews; Output Group the reviews by users and aggregate per-user average rating, then respond

B.

Input: Customer service chat logs; Output Group the chat logs by users, followed by summarizing each user's interactions, then respond

C.

Input: Customer service chat logs; Output: Find the answers to similar questions and respond with a summary

D.

Input: Customer reviews: Output Classify review sentiment

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Question # 16

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)

A.

Option A

B.

Option B

C.

Option C

D.

Option D

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Question # 17

A Generative Al Engineer has developed an LLM application to answer questions about internal company policies. The Generative AI Engineer must ensure that the application doesn’t hallucinate or leak confidential data.

Which approach should NOT be used to mitigate hallucination or confidential data leakage?

A.

Add guardrails to filter outputs from the LLM before it is shown to the user

B.

Fine-tune the model on your data, hoping it will learn what is appropriate and not

C.

Limit the data available based on the user’s access level

D.

Use a strong system prompt to ensure the model aligns with your needs.

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Question # 18

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?

A.

Vector Search

B.

Lakeview

C.

DBSQL

D.

Inference Tables

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