A developer is building an agent that uses a conversational API to retrieve data. They need to handle errors gracefully when the API returns an error or the data is not found. They also want to use a framework that supports retries and fallbacks. Which approach is best?

Ignore errors and assume the API always works

Write custom error handling in the agent code

Use a simple try-except block without retries

Use LangChain's built-in error handling and retry mechanisms

A team is building a document Q&A system that must index thousands of PDFs and answer questions based on their content. They also need to track the performance of different indexing strategies. Which framework and tracking tool combination is most appropriate?

Use Haystack for indexing and store metrics in a CSV file

Use a simple Python script with a vector database and no tracking

Use LlamaIndex for indexing and MLflow for tracking experiments

Use LangChain for indexing and a custom logging script

What is the primary purpose of integrating a multi-agent system with Genie Spaces?

To replace the need for a vector database

To train language models on the data stored in Genie Spaces

To allow agents to retrieve data from curated datasets using natural language queries

To provide a graphical user interface for agents

A business analyst wants to query sales data using natural language, but the data is stored in a curated Databricks Lakehouse. The team wants to enable this through a conversational interface without writing SQL. Which integration should they use?

Use a third-party BI tool that connects directly to the Lakehouse

Use a custom Python script that runs SQL queries

Use Genie Spaces to allow natural language queries against curated datasets

DatabricksCertified Generative AI Engineer Associate

Domain 3Objective 5

Tooling and Frameworks GENERATIVE-AI-ENGINEER-ASSOCIATE Practice Questions (Page 5)

Part of the Section 3: Application Development domain, which makes up ~18% of our current practice bank. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~6–11 in this domain), expect 1–2 from this objective — we provide 24 practice questions to prepare you well beyond it. (estimate)

24questions here

5free pages

6concepts

Questions 21–24

21expert · hard
A developer is building an agent that uses a conversational API to retrieve data. They need to handle errors gracefully when the API returns an error or the data is not found. They also want to use a framework that supports retries and fallbacks. Which approach is best?
Select an answer first
22application · medium
A team is building a document Q&A system that must index thousands of PDFs and answer questions based on their content. They also need to track the performance of different indexing strategies. Which framework and tracking tool combination is most appropriate?
Select an answer first
23foundation · easy
What is the primary purpose of integrating a multi-agent system with Genie Spaces?
Select an answer first
24application · medium
A business analyst wants to query sales data using natural language, but the data is stored in a curated Databricks Lakehouse. The team wants to enable this through a conversational interface without writing SQL. Which integration should they use?
Select an answer first

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