Databricks-Generative-AI-Engineer-Associate問題集、Databricks実際の試験問題

質問 1

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

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

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

正解: A

解説: (PassTest メンバーにのみ表示されます)

質問 2

A Generative AI Engineer is testing a simple prompt template in LangChain using the code below, but is getting an error:
Python
from langchain.chains import LLMChain
from langchain_community.llms import OpenAI
from langchain_core.prompts import PromptTemplate
prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(input_variables=["adjective"], template=prompt_template)
# ... (Error-prone section)
Assuming the API key was properly defined, what change does the Generative AI Engineer need to make to fix their chain?

A. prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(input_variables=["adjective"], template=prompt_template) llm = OpenAI() llm_chain = LLMChain(prompt=prompt, llm=llm) llm_chain.generate([{"adjective": "funny"}])

B. (Incorrect structure)

C. (Incorrect structure)

D. (Incorrect structure)

正解: A

解説: (PassTest メンバーにのみ表示されます)

質問 3

A team wants to serve a code generation model as an assistant for their software developers. It should support multiple programming languages. Quality is the primary objective.
Which of the Databricks Foundation Model APIs, or models available in the Marketplace, would be the best fit?

A. Llama2-70b

B. MPT-7b

C. CodeLlama-34B

D. BGE-large

正解: C

解説: (PassTest メンバーにのみ表示されます)

質問 4

A Generative Al Engineer has built an LLM-based system that will automatically translate user text between two languages. They now want to benchmark multiple LLM's on this task and pick the best one. They have an evaluation set with known high quality translation examples. They want to evaluate each LLM using the evaluation set with a performant metric.
Which metric should they choose for this evaluation?

A. ROUGE metric

B. NDCG metric

C. RECALL metric

D. BLEU metric

正解: D

解説: (PassTest メンバーにのみ表示されます)

質問 5

A team uses Mosaic AI Vector Search to retrieve documents for their Retrieval-Augmented Generation (RAG) pipeline. The search query returns five relevant documents, and the first three are added to the prompt as context. Performance evaluation with Agent Evaluation shows that some lower-ranked retrieved documents have higher context relevancy scores than higher-ranked documents. Which option should the team consider to optimize this workflow?

A. Use a different embedding model for computing document embeddings.

B. Use a reranker to order the documents based on the relevance scores.

C. Increase the number of documents added to the prompt to improve context relevance.

D. Modify the prompt to instruct the LLM to order the documents based on the relevance scores.

正解: B

解説: (PassTest メンバーにのみ表示されます)

質問 6

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. Limit the data available based on the user's access level

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

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

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

正解: D

解説: (PassTest メンバーにのみ表示されます)

質問 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. AutoML

B. DatabrickslQ

C. Foundation Model APIs

D. Feature Serving

正解: D

解説: (PassTest メンバーにのみ表示されます)

質問 8

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. Switch to using External Models instead

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

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

正解: D

解説: (PassTest メンバーにのみ表示されます)

Databricks Certified Generative AI Engineer Associate - Databricks-Generative-AI-Engineer-Associate 模擬練習