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Agent Development & Tool Design — Databricks GenAI Associate Practice Question

A representative Databricks Certified Generative AI Engineer Associate (Databricks GenAI Associate) exam question on Agent Development & Tool Design. Work through it below, then read why each option is right or wrong.

This question is part of the free Databricks Certified Generative AI Engineer Associate practice test.

Short answer

The correct answer is A. Define tool schemas with clear descriptions and input parameters, then use structured output formatting in the LLM call to enforce JSON schema validation on the agent's response..

Structured output formatting with schema validation is the recommended pattern in the Mosaic AI Agent Framework for ensuring consistent, valid outputs. Defining clear tool schemas enables the agent to reason about which tool to use, and enforcing JSON schema on the LLM response guarantees valid output without post-processing fragility. Manual parsing after execution introduces errors and latency. String concatenation in prompts is unreliable and bypasses schema guarantees. Multiple agents with conditional routing adds unnecessary complexity when a single agent with well-defined tools and structured outputs handles the pattern efficiently.

The Question

You are designing an agent using the Mosaic AI Agent Framework that must process customer service requests by querying a database, retrieving documents, and generating responses. The agent needs to decide whether to call a database lookup tool or a document retrieval tool based on the customer's question. You also need to ensure that the agent's final response is always a valid JSON object containing fields for 'action_taken', 'result', and 'confidence_score'. Which approach best implements this requirement?

ADefine tool schemas with clear descriptions and input parameters, then use structured output formatting in the LLM call to enforce JSON schema validation on the agent's response.Correct
BUse string concatenation to build the JSON response within the agent's system prompt, relying on the LLM to follow the template.
CInstruct the agent to always return plain text and manually parse and convert the output to JSON after the agent completes execution.
DCreate separate agents for each tool type and use conditional logic to route requests, then combine results using string formatting.

Why A is correct

Structured output formatting with schema validation is the recommended pattern in the Mosaic AI Agent Framework for ensuring consistent, valid outputs. Defining clear tool schemas enables the agent to reason about which tool to use, and enforcing JSON schema on the LLM response guarantees valid output without post-processing fragility. Manual parsing after execution introduces errors and latency. String concatenation in prompts is unreliable and bypasses schema guarantees. Multiple agents with conditional routing adds unnecessary complexity when a single agent with well-defined tools and structured outputs handles the pattern efficiently.

Why the other options are wrong

Option B: Use string concatenation to build the JSON response within the agent's system prompt, relying on the LLM to follow the template.

Option B does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (A) is the only one that fully meets every constraint stated in the question.

Option C: Instruct the agent to always return plain text and manually parse and convert the output to JSON after the agent completes execution.

Option C does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (A) is the only one that fully meets every constraint stated in the question.

Option D: Create separate agents for each tool type and use conditional logic to route requests, then combine results using string formatting.

Option D does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (A) is the only one that fully meets every constraint stated in the question.

Key idea: Agent Development & Tool Design

Structured output formatting with schema validation is the recommended pattern in the Mosaic AI Agent Framework for ensuring consistent, valid outputs. Defining clear tool schemas enables the agent to reason about which tool to use, and enforcing JSON schema on the LLM response guarantees valid output without post-processing fragility. Manual parsing after execution introduces errors and latency. String concatenation in prompts is unreliable and bypasses schema guarantees. Multiple agents with conditional routing adds unnecessary complexity when a single agent with well-defined tools and structured outputs handles the pattern efficiently. On the Databricks GenAI Associate exam, questions in the "Application Development" domain test whether you can map a scenario's constraints to the right choice. Read the requirement carefully, eliminate options that violate any single constraint, and pick the one that satisfies all of them with the least operational overhead.

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