Databricks Generative AI Engineer Databricks-Generative-AI-Engineer-Associate

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Databricks Certified Generative AI Engineer Associate Databricks-Generative-AI-Engineer-Associate Prüfungsfragen mit Lösungen

Frage #1
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.

Frage #2
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.

Frage #3
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. Use any available data you personally created which is completely original and you can decide what license to use.
B. Reach out to the data curators directly before you have started using the trained model to let them know.
C. Reach out to the data curators directly after you have started using the trained model to let them know.
D. Only use data explicitly labeled with an open license and ensure the license terms are followed.

Frage #4
A Generative AI Engineer has been asked to design an LLM-based application that accomplishes the following business objective: answer employee HR questions using HR PDF documentation.
Which set of high level tasks should the Generative AI Engineer ' s system perform?

A. Calculate averaged embeddings for each HR document, compare embeddings to user query to find the best document. Pass the best document with the user query into an LLM with a large context window to generate a response to the employee.
B. Use an LLM to summarize HR documentation. Provide summaries of documentation and user query into an LLM with a large context window to generate a response to the user.
C. Create an interaction matrix of historical employee questions and HR documentation. Use ALS to factorize the matrix and create embeddings. Calculate the embeddings of new queries and use them to find the best HR documentation. Use an LLM to generate a response to the employee question based upon the documentation retrieved.
D. Split HR documentation into chunks and embed into a vector store. Use the employee question to retrieve best matched chunks of documentation, and use the LLM to generate a response to the employee based upon the documentation retrieved.

Frage #5
A Generative AI Engineer has created a RAG application which can help employees retrieve answers from an internal knowledge base, such as Confluence pages or Google Drive. The prototype application is now working with some positive feedback from internal company testers. Now the Generative Al Engineer wants to formally evaluate the system's performance and understand where to focus their efforts to further improve the system.
How should the Generative AI Engineer evaluate the system?

A. Use cosine similarity score to comprehensively evaluate the quality of the final generated answers.
B. Curate a dataset that can test the retrieval and generation components of the system separately. Use MLflow's built in evaluation metrics to perform the evaluation on the retrieval and generation components.
C. Benchmark multiple LLMs with the same data and pick the best LLM for the job.
D. Use an LLM-as-a-judge to evaluate the quality of the final answers generated.

Fragen und Antworten: