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Databricks Certified Generative AI Engineer Associateの試験情報

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

Databricks-Generative-AI-Engineer-Associate試験の出題範囲

Databricks Databricks-Generative-AI-Engineer-Associate 試験シラバストピック:

Databricks Certified Generative AI Engineer Associateに関する受験前Q&A

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

問題 #1

A Generative Al Engineer is building a system which will answer questions on latest stock news articles.
Which will NOT help with ensuring the outputs are relevant to financial news?

A. Incorporate manual reviews to correct any problematic outputs prior to sending to the users
B. Implement a comprehensive guardrail framework that includes policies for content filters tailored to the finance sector.
C. Increase the compute to improve processing speed of questions to allow greater relevancy analysis C Implement a profanity filter to screen out offensive language

問題 #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.

問題 #3

A Generative AI Engineer is evaluating a customer-support agent in Databricks. The team needs to score each response on a domain-specific policy: the answer must cite an approved refund rule and must not mention unsupported escalation paths. Built-in evaluation metrics do not capture this logic. The team wants the metric to run during agent evaluation in Databricks and return a repeatable, structured score for each trace.
Which approach should the engineer use?

A. Add the policy text to the system prompt and rely on the model's self-reported compliance as the evaluation result.
B. Use only latency and token-count metrics because custom policy checks are not supported in evaluation workflows.
C. Create a custom MLflow scorer that inspects agent outputs against the policy and pass it into the evaluation run.
D. Log the traces to MLflow v3.x and review failures in the UI without defining a scorer.

問題 #4

A Generative Al Engineer is tasked with developing a RAG application that will help a small internal group of experts at their company answer specific questions, augmented by an internal knowledge base. They want the best possible quality in the answers, and neither latency nor throughput is a huge concern given that the user group is small and they're willing to wait for the best answer. The topics are sensitive in nature and the data is highly confidential and so, due to regulatory requirements, none of the information is allowed to be transmitted to third parties.
Which model meets all the Generative Al Engineer's needs in this situation?

A. OpenAI GPT-4
B. Dolly 1.5B
C. BGE-large
D. Llama2-70B

問題 #5

A Generative AI Engineer at an automotive company would like to build a question-answering chatbot to help customers answer specific questions about their vehicles. They have:
* A catalog with hundreds of thousands of cars manufactured since the 1960s
* Historical searches with user queries and successful matches
* Descriptions of their own cars in multiple languages
They have already selected an open-source LLM and created a test set of user queries. They need to discard techniques that will not help them build the chatbot. Which do they discard?

A. Setting chunk size to match the model ' s context window to maximize coverage
B. Fine-tuning an embedding model on automotive terminology
C. Implementing metadata filtering based on car models and years
D. Adding few-shot examples for response generation

解説：