Databricks인증 실제 시험문제, Databricks-Generative-AI-Engineer-Associate 덤프문제집

문제1
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?

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

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

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

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

정답: A

설명: (ITDumpsKR 회원만 볼 수 있음)

문제2
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. 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.

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

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.

정답: C

설명: (ITDumpsKR 회원만 볼 수 있음)

문제3
A Generative Al Engineer is building an LLM-based application that has an important transcription (speech-to-text) task. Speed is essential for the success of the application Which open Generative Al models should be used?

A. L!ama-2-70b-chat-hf

B. DBRX

C. MPT-30B-lnstruct

D. whisper-large-v3 (1.6B)

정답: D

설명: (ITDumpsKR 회원만 볼 수 있음)

문제4
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?

A. 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.

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

C. 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.

D. 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.

정답: B

설명: (ITDumpsKR 회원만 볼 수 있음)

문제5
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. First create a unique identifier for each document, then save to a Delta table

B. 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

C. Flatten the dataframe to one chunk per row, create a unique identifier for each row, and 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

정답: C

설명: (ITDumpsKR 회원만 볼 수 있음)

문제6
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?

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

정답: B

설명: (ITDumpsKR 회원만 볼 수 있음)

문제7
A Generative AI Engineer is using LangGraph to define multiple tools in a single agentic application. They want to enable the main orchestrator LLM to decide on its own which tools are most appropriate to call for a given prompt. To do this, they must determine the general flow of the code. Which sequence will do this?

A. 1. Define the tools inside the agents 2. Load the agents into the LLM 3. Instruct the LLM to use COT reasoning to determine the appropriate agent

B. 1. Define or import the tools 2. Define the agent 3. Initialize the agent with ReAct, the LLM, and the tools

C. 1. Define or import the tools 2. Add tools and LLM to the agent 3. Create the ReAct agent

D. 1. Define the tools 2. Load each tool into a separate agent 3. Instruct the LLM to use ReAct to call the appropriate agent

정답: B

설명: (ITDumpsKR 회원만 볼 수 있음)

문제8
A Generative Al Engineer is helping a cinema extend its website ' s chat bot to be able to respond to questions about specific showtimes for movies currently playing at their local theater. They already have the location of the user provided by location services to their agent, and a Delta table which is continually updated with the latest showtime information by location. They want to implement this new capability In their RAG application.
Which option will do this with the least effort and in the most performant way?

A. Create a Feature Serving Endpoint from a FeatureSpec that references an online store synced from the Delta table. Query the Feature Serving Endpoint as part of the agent logic / tool implementation.

B. implementation. Write the Delta table contents to a text column.then embed those texts using an embedding model and store these in the vector index Look up the information based on the embedding as part of the agent logic / tool implementation.

C. Set up a task in Databricks Workflows to write the information in the Delta table periodically to an external database such as MySQL and query the information from there as part of the agent logic / tool implementation.

D. Query the Delta table directly via a SQL query constructed from the user ' s input using a text-to-SQL LLM in the agent logic / tool

정답: A

설명: (ITDumpsKR 회원만 볼 수 있음)

문제9
A Generative AI Engineer is managing prompt templates using MLflow v3.x for a document summarization pipeline. A regulatory audit requires the team to demonstrate exactly which prompt version was used to generate outputs on a specific date three months ago, including the exact prompt text and any variables used at that time.
Which combination of MLflow v3.x capabilities allows the engineer to satisfy this audit requirement?

A. MLflow Prompt Registry version history and logged runs that reference the prompt name and version used during inference.

B. MLflow autologging and Delta Lake time travel on the inference table.

C. MLflow experiment tags and an automatically scripted changelog stored in a Databricks notebook.

D. MLflow Model Registry webhooks and a downstream audit log stored in an external database.

정답: A

설명: (ITDumpsKR 회원만 볼 수 있음)

문제10
A company selling gourmet mushroom-growing supplies has a script that runs once per day to scrape various social media platforms for posts that mention its name. The scraped text data is loaded into a Delta table each night for a downstream processing task that summarizes each post and its sentiment for internal use. Given the small size of the company, it only receives a couple hundred posts per day.
Which solution best optimizes for cost and ease of implementation?

A. Schedule a nightly job that runs a notebook to download the model locally, process the records, and terminate the cluster.

B. Schedule a nightly SQL query with ai_query() calling a provisioned-throughput endpoint.

C. Schedule a nightly SQL query with ai_query() calling a pay-per-token endpoint.

D. Schedule a nightly job to call OpenAI's batch inference API, save the results, and terminate the cluster.

정답: C

설명: (ITDumpsKR 회원만 볼 수 있음)

최신Databricks Certified Generative AI Engineer Associate - Databricks-Generative-AI-Engineer-Associate무료샘플문제

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