최신 Databricks-Generative-AI-Engineer-Associate 무료덤프 - Databricks Certified Generative AI Engineer Associate

문제1

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

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

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

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제2

A Generative AI Engineer I using the code below to test setting up a vector store:

Assuming they intend to use Databricks managed embeddings with the default embedding model, what should be the next logical function call?

A. vsc.get_index()

B. vsc.similarity_search()

C. vsc.create_delta_sync_index()

D. vsc.create_direct_access_index()

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제3

A Generative AI Engineer received the following business requirements for an external chatbot.
The chatbot needs to know what types of questions the user asks and routes to appropriate models to answer the questions. For example, the user might ask about upcoming event details. Another user might ask about purchasing tickets for a particular event.
What is an ideal workflow for such a chatbot?

A. The chatbot should only process payments

B. The chatbot should only look at previous event information

C. The chatbot should be implemented as a multi-step LLM workflow. First, identify the type of question asked, then route the question to the appropriate model. If it's an upcoming event question, send the query to a text-to-SQL model. If it's about ticket purchasing, the customer should be redirected to a payment platform.

D. There should be two different chatbots handling different types of user queries.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제4

A Generative AI Engineer is building a RAG application that will rely on context retrieved from source documents that are currently in PDF format. These PDFs can contain both text and images. They want to develop a solution using the least amount of lines of code.
Which Python package should be used to extract the text from the source documents?

A. flask

B. unstructured

C. beautifulsoup

D. numpy

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제5

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

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

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

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

정답: A

설명: (DumpTOP 회원만 볼 수 있음)

문제6

A company has a typical RAG-enabled, customer-facing chatbot on its website.

Select the correct sequence of components a user's questions will go through before the final output is returned. Use the diagram above for reference.

A. 1.response-generating LLM, 2.vector search, 3.context-augmented prompt, 4.embedding model

B. 1.context-augmented prompt, 2.vector search, 3.embedding model, 4.response-generating LLM

C. 1.response-generating LLM, 2.context-augmented prompt, 3.vector search, 4.embedding model

D. 1.embedding model, 2.vector search, 3.context-augmented prompt, 4.response-generating LLM

정답: D

설명: (DumpTOP 회원만 볼 수 있음)

최신 Databricks-Generative-AI-Engineer-Associate 무료덤프 - Databricks Certified Generative AI Engineer Associate

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