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

문제1

A Generative AI Engineer needs to allocate costs for an agent deployed via Agent Framework behind AI Gateway. The finance team requires daily reporting by workspace and endpoint, including token usage and request counts. The engineer wants to query this data with SQL and join it to an internal cost-center mapping table.
Which table type best serves their needs?

A. AI Gateway usage table.

B. System Serving, Served Entities table.

C. Inference Tables.

D. MLflow experiment run tables.

정답: A

설명: (DumpTOP 회원만 볼 수 있음)

문제2

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. 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.

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

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

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.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제3

A Generative AI Engineer is building an interactive catalog for a company's inventory system that allows users to search for any item using a plain-text description. There are currently about 17,000 items, and new items are not frequently added. They need a solution that will be the most cost-effective and easy for the company to maintain.
Which solution should the engineer choose?

A. Standard vector search with self-managed embeddings and a Delta Sync index, continuous sync.

B. Storage-optimized vector search with a Direct Vector Access index, triggered sync.

C. Standard vector search with Databricks-managed embeddings and a Delta Sync index, continuous sync.

D. Standard vector search with Databricks-managed embeddings and a Delta Sync index, triggered sync.

정답: D

설명: (DumpTOP 회원만 볼 수 있음)

문제4

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. Energy usage per query

B. HuggingFace Leaderboard values for the base LLM

C. Final perplexity scores for the training of the model

D. Number of customer inquiries processed per unit of time

정답: D

설명: (DumpTOP 회원만 볼 수 있음)

문제5

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. MPT-30B-lnstruct

B. DBRX

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

D. whisper-large-v3 (1.6B)

정답: D

설명: (DumpTOP 회원만 볼 수 있음)

문제6

A Generative Al Engineer has already trained an LLM on Databricks and it is now ready to be deployed.
Which of the following steps correctly outlines the easiest process for deploying a model on Databricks?

A. Log the model using MLflow during training, directly register the model to Unity Catalog using the MLflow API, and start a serving endpoint

B. Log the model as a pickle object, upload the object to Unity Catalog Volume, register it to Unity Catalog using MLflow, and start a serving endpoint

C. Wrap the LLM's prediction function into a Flask application and serve using Gunicorn

D. Save the model along with its dependencies in a local directory, build the Docker image, and run the Docker container

정답: A

설명: (DumpTOP 회원만 볼 수 있음)

문제7

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. Lakeview

B. Inference Tables

C. Vector Search

D. DBSQL

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

문제8

A Generative AI Engineer is experimenting with using parameters to configure an agent in Mosaic Agent Framework. However, they are struggling to get the agent to respond with relevant information with this configuration:
config = { " prompt_template " : " You are a trivia bot. Generate a question based on the user ' s input:
{user_input} " , " input_vars " : [ " user_input " ], " parameters " : { " temperature " : 0.01, " max_tokens " :
500}}
Which error is causing the problem?

A. The prompt does not list available agents for the LLM to call

B. The prompt does not set the retriever schema

C. The prompt is not wrapped in ChatModel

D. The prompt does not parse the user ' s input vars

정답: D

설명: (DumpTOP 회원만 볼 수 있음)

문제9

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

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

C. 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

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

정답: A

설명: (DumpTOP 회원만 볼 수 있음)

문제10

A Generative AI Engineer is deploying an agent using Mosaic AI Model Serving. The agent needs to access various Databricks resources, including Vector Search, Databricks SQL, and Functions. They need to find the easiest and best-practice way to authenticate the deployed agent to access these resources.
What approach should they choose?

A. Define resource dependencies while logging the agent and deploy it with the Agent Framework.

B. Embed authentication credentials within the agent's code to access the required resources.

C. Set appropriate permissions on the Model Serving endpoint for the agent, as these permissions will be used when connecting to other resources.

D. Log the authentication token while logging the agent; this token will be automatically used for authentication.

정답: A

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

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