Databricks-GenAI-Assoc Application Development Practice Question
A developer needs to monitor the performance of an LLM application in production. They want to track the latency of their model endpoint. Where can they find this metric in the Databricks workspace?
⚠ Common exam trap
Candidates frequently look for external APM tools or cluster logs, failing to recognize that Model Serving endpoints feature a dedicated built-in Monitoring tab.
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
✓
In the 'Monitoring' tab of the Model Serving endpoint UI.
Databricks Model Serving provides built-in monitoring dashboards for every endpoint. These dashboards automatically track key performance indicators such as request latency, throughput, and error rates. Monitoring these metrics is vital for understanding the user experience and detecting potential performance bottlenecks. By providing this information natively, Databricks helps developers maintain high service quality without requiring external observability tools, streamlining the monitoring and optimization process for deployed models.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
In the Unity Catalog table lineage view.
Why it's wrong here
Table lineage tracks how data flows through tables and notebooks. It is a governance and audit tool, not a performance monitoring tool. It does not provide real-time metrics on inference latency, throughput, or error rates for deployed models, making it the wrong place to look for performance observability.
- ✓
In the 'Monitoring' tab of the Model Serving endpoint UI.
Why this is correct
The 'Monitoring' tab within the Model Serving endpoint interface is the dedicated location for viewing operational metrics. It displays auto-generated graphs for latency, request volume, and error rates, enabling developers to quickly assess the health and performance of their deployed models without needing additional configuration.
- ✗
In the Databricks SQL Warehouse query history.
Why it's wrong here
Query history shows performance data for SQL queries running against a warehouse. Model serving endpoints operate on distinct inference infrastructure, not on SQL warehouses. Therefore, the query history tool is not the correct place to find metrics regarding LLM inference latency or endpoint usage.
- ✗
In the workspace audit logs.
Why it's wrong here
Audit logs record security and administrative events, such as who accessed a notebook or changed a permission. They are designed for compliance and forensic analysis, not for tracking real-time application performance metrics like inference latency. Relying on audit logs for performance monitoring would be highly inefficient and ineffective.
About these practice questions
Courseiva writes every Databricks-GenAI-Assoc question from scratch — 330 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
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.