Databricks-GenAI-Assoc Application Development Practice Question
A team is deploying a LLM-based application using Databricks Model Serving. They want to implement robust observability and monitoring for their endpoint. Which TWO features should they utilize to track performance and quality metrics? (Select TWO)
⚠ Common exam trap
Candidates tend to confuse generic cluster monitoring tools with LLM-specific telemetry features, missing that Model Inference Tables and the Serving Monitoring Tab are specifically built for tracking endpoint inputs, outputs, and quality metrics.
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
✓
Model Inference Tables
To effectively monitor a LLM application, teams must integrate both system-level performance metrics and application-level quality telemetry. Inference tables automatically log input and output data for analysis, while the native 'Monitoring' tab in the Model Serving UI provides latency and throughput metrics. Combined, these features provide a comprehensive view of how the model is performing, identifying bottlenecks in latency or degradation in response quality over time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Model Inference Tables
Why this is correct
Inference tables provide a structured way to capture all requests and responses sent to a serving endpoint. This data is written directly into Unity Catalog, allowing developers to perform SQL analysis on input prompts and model outputs to evaluate quality, detect drift, and perform offline debugging of model performance.
- ✓
Model Serving Monitoring Tab
Why this is correct
The native Monitoring tab in the Model Serving UI provides real-time visibility into operational metrics like request latency, request volume, and error rates. These metrics are essential for maintaining operational health, allowing engineers to react quickly to spikes in traffic or failures that impact the user experience.
- ✗
Unity Catalog Lineage
Why it's wrong here
Unity Catalog Lineage is designed to track data dependencies between tables, views, and notebooks. While it is crucial for data governance and understanding data provenance, it does not provide the real-time operational or quality monitoring capabilities required to track performance metrics for active LLM inference endpoints.
- ✗
Databricks Delta Sharing
Why it's wrong here
Delta Sharing is an open protocol for secure data sharing across organizations and platforms. It is optimized for sharing datasets rather than monitoring live model performance or tracking inference artifacts, and it lacks the telemetry integration required to support the specific needs of an LLM application lifecycle.
- ✗
Workspace-level Audit Logs
Why it's wrong here
Audit logs record administrative actions and security events across the workspace, such as user logins or permission changes. While useful for security and compliance monitoring, they do not contain the granular request-response payloads or latency metrics necessary to monitor and optimize the performance of specific model endpoints.
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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.