Databricks-GenAI-Assoc Design Applications Practice Question
A generative AI engineer is designing an agent on Databricks that uses a LangChain agent with tool-calling capabilities. The agent must call a Databricks SQL warehouse to run queries and a Vector Search index for retrieval. Which design consideration is most important for controlling agent behavior in production?
⚠ Common exam trap
The trap here is assuming that a more capable model or more iterations will make an agent reliable, when reliability comes from precise tool definitions and a constrained toolset.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Define clear tool descriptions and limit the agent's available tools to only those required for the task.
Agent behavior is driven by tool descriptions and the set of tools available. Clear descriptions help the model choose the right tool, and a minimal toolset reduces the chance of unintended calls. Iteration limits, logging, and model size affect performance or observability but do not directly control which tools the agent invokes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Define clear tool descriptions and limit the agent's available tools to only those required for the task.
Why this is correct
The agent selects tools based on their descriptions, so precise descriptions and a minimal toolset reduce incorrect tool selection and unintended actions. Limiting tools also narrows the blast radius if the agent misbehaves. This is a core design principle for production agents that must be predictable and governable.
- ✗
Use the largest available foundation model for the agent to maximize reasoning quality.
Why it's wrong here
Model size alone does not ensure correct tool use and can increase latency and cost. A large model with vague tool descriptions may still choose the wrong tool. Behavior control comes from clear tool specifications and constraints, not from model scale.
- ✗
Enable verbose logging of every intermediate step and store the logs in a Delta table for later review.
Why it's wrong here
Logging is valuable for observability, but it does not control agent behavior in real time. Storing verbose logs adds storage cost and may capture sensitive data. It is a monitoring practice, not a mechanism for constraining tool selection or preventing unintended actions.
- ✗
Increase the agent's maximum iterations to a very high number so it can always complete complex tasks.
Why it's wrong here
A high iteration limit allows the agent to loop indefinitely, increasing latency and cost without guaranteeing success. It also makes runaway behavior harder to detect. Production agents benefit from bounded iterations with graceful failure rather than an effectively unlimited loop.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-GenAI-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-GenAI-Assoc exam.