Databricks-ML-Assoc Model Deployment Practice Question
Why should you use an inference table in Databricks Model Serving?
⚠ Common exam trap
Candidates think inference tables are for performance tuning or caching. They fail to realize the primary purpose is observability: tracking inputs and outputs for monitoring and drift detection.
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
✓
To provide visibility into production inputs and predictions
Inference tables allow you to capture every request and response processed by a model endpoint. This is vital for monitoring model performance over time, debugging issues by examining specific input-output pairs, and detecting data drift. Without this captured data, you essentially have no visibility into how the model is performing in the real world, which makes it impossible to maintain and improve the system effectively.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
To increase the speed of model inference
Why it's wrong here
Inference tables actually add a small amount of latency due to the asynchronous logging of requests to Delta storage. They are designed for observability and auditability, not for speeding up the model's inference performance. They do not optimize the model code or the underlying compute infrastructure for faster execution.
- ✗
To automatically retrain the model on new data
Why it's wrong here
While inference tables provide the data necessary for retraining, they do not trigger the retraining process itself. Retraining is a separate workflow typically orchestrated by jobs that consume the logged data. The table is a storage mechanism, not an automated training service that executes model updates autonomously.
- ✓
To provide visibility into production inputs and predictions
Why this is correct
Inference tables provide a permanent, queryable record of every inference call, including the input features and the resulting prediction. This is the foundation for monitoring, drift detection, and debugging. By logging this data to Delta tables, you gain full insight into how the model is being used in production.
- ✗
To secure the endpoint against malicious attacks
Why it's wrong here
Inference tables are for logging, not for security. They do not act as a firewall, intrusion detection system, or authentication layer. Security for the endpoint is handled by Unity Catalog access policies and network configurations. While the logs can be used for security forensics, they do not provide active protection.
About these practice questions
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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-ML-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-ML-Assoc exam.