Wer die Databricks Certified Generative AI Engineer Associate besteht, belegt seine Kompetenz mit einem anerkannten Herstellerzertifikat. Fast2test unterstützt Sie dabei mit 94 Übungsfragen zur Databricks-Generative-AI-Engineer-Associate Prüfung, die inhaltlich eng am offiziellen Prüfungsrahmen erstellt wurden.

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

Frage #1

A Generative Al Engineer is working with a retail company that wants to enhance its customer experience by automatically handling common customer inquiries. They are working on an LLM-powered Al solution that should improve response times while maintaining a personalized interaction. They want to define the appropriate input and LLM task to do this.
Which input/output pair will do this?

A. Input: Customer service chat logs; Output: Find the answers to similar questions and respond with a summary
B. Input: Customer service chat logs; Output Group the chat logs by users, followed by summarizing each user ' s interactions, then respond
C. Input: Customer reviews: Output Classify review sentiment
D. Input: Customer reviews; Output Group the reviews by users and aggregate per-user average rating, then respond

Frage #2

A generative AI engineer is deploying an AI agent authored with MLflow's ChatAgent interface for a retail company ' s customer support system on Databricks. The agent must handle thousands of inquiries daily, and the engineer needs to track its performance and quality in real-time to ensure it meets service-level agreements. Which metrics are automatically captured by default and made available for monitoring when the agent is deployed using the Mosaic AI Agent Framework?

A. Quality metrics like correctness and guideline adherence
B. Both operational and quality metrics
C. No metrics are automatically captured
D. Operational metrics like request volume, latency, and errors

Frage #3

A Generative Al Engineer is creating an LLM system that will retrieve news articles from the year 1918 and related to a user ' s query and summarize them. The engineer has noticed that the summaries are generated well but often also include an explanation of how the summary was generated, which is undesirable.
Which change could the Generative Al Engineer perform to mitigate this issue?

A. Split the LLM output by newline characters to truncate away the summarization explanation.
B. Provide few shot examples of desired output format to the system and/or user prompt.
C. Tune the chunk size of news articles or experiment with different embedding models.
D. Revisit their document ingestion logic, ensuring that the news articles are being ingested properly.

Frage #4

A Generative Al Engineer would like an LLM to generate formatted JSON from emails. This will require parsing and extracting the following information: order ID, date, and sender email. Here's a sample email:

They will need to write a prompt that will extract the relevant information in JSON format with the highest level of output accuracy.
Which prompt will do that?

A. You will receive customer emails and need to extract date, sender email, and order ID. You should return the date, sender email, and order ID information in JSON format.
B. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in a human-readable format.
C. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON format.
Here's an example: {"date": "April 16, 2024", "sender_email": "[email protected]", "order_id":
"RE987D"}
D. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON format.

Frage #5

After changing the response generating LLM in a RAG pipeline from GPT-4 to a model with a shorter context length that the company self-hosts, the Generative AI Engineer is getting the following error:

What TWO solutions should the Generative AI Engineer implement without changing the response generating model? (Choose two.)

A. Reduce the maximum output tokens of the new model
B. Use a smaller embedding model to generate
C. Reduce the number of records retrieved from the vector database
D. Decrease the chunk size of embedded documents
E. Retrain the response generating model using ALiBi

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