最も有効な Databricks-Generative-AI-Engineer-Associate 試験参考書

Databricks-Generative-AI-Engineer-Associate試験本番の環境を再現したテストエンジンで、Databricks Certified Generative AI Engineer Associateの94問を実戦さながらに演習できます。ShikenPASSなら、時間配分の感覚まで本番前に身につけられます。

Databricks Databricks-Generative-AI-Engineer-Associate 試験概要:

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Databricks-Generative-AI-Engineer-Associate試験に関するQ&Aまとめ

Databricks Certified Generative AI Engineer Associate 認定 Databricks-Generative-AI-Engineer-Associate 試験問題：

問題 #1

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.

問題 #2

A Generative AI Engineer has deployed a customer-support agent in production that retrieves product documentation and generates responses. SMEs have been reviewing agent responses and providing feedback through a web interface that captures ratings of 1-5 stars and written comments. The engineer needs to systematically collect this feedback and use it to create an evaluation dataset that can be used to compare future agent versions against the current baseline performance.
Which approach should the engineer use to accomplish this task?

A. Use Unity Catalog to create a view that filters only 5-star-rated interactions, then register this view as the evaluation dataset to benchmark all future agent versions.
B. Log the SME ratings and comments directly to a Delta table with the corresponding user queries and agent responses, then use MLflow to create an evaluation dataset from this table and register it for future agent evaluations.
C. Use the customer review app to collect SME feedback, then directly deploy the highest-rated responses as the new agent baseline without storing them as a formal evaluation dataset.
D. Export only the written SME comments to a text file and manually score them using a custom script, then use the script's output as the evaluation dataset for future agent comparisons.

問題 #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. CodeLlama-34B
B. Llama2-70b
C. BGE-large
D. MPT-7b

問題 #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. Return the extracted information in a human-readable format.
B. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON 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. You should return the date, sender email, and order ID information in JSON format.

問題 #5

A Generative AI Engineer is developing an agent system using a popular agent-authoring library. The agent comprises multiple parallel and sequential chains. The engineer encounters challenges as the agent fails at one of the steps, making it difficult to debug the root cause. They need to find an appropriate approach to research this issue and discover the cause of failure. Which approach do they choose?

A. Enable MLflow tracing to gain visibility into each agent ' s behavior and execution step.
B. Run MLflow.evaluate to determine root cause of failed step.
C. Deconstruct the agent into independent steps to simplify debugging.
D. Implement structured logging within the agent ' s code to capture detailed execution information.

解説：