Databricks-ML-Pro Model Deployment Practice Question
A data scientist has deployed a model to Databricks Model Serving and wants to monitor its performance over time. They need to track prediction drift and data quality issues. Which Databricks feature should they use to automatically capture inference logs and compute metrics?
⚠ Common exam trap
The trap here is assuming that MLflow tracking or manual queries can replace inference tables for production monitoring, but they lack automatic capture and integration with monitoring tools.
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
✓
Enable inference tables on the serving endpoint.
Inference tables are a Databricks feature that automatically logs request and response payloads from a Model Serving endpoint into a Delta table. This data can then be used with Lakehouse Monitoring to track prediction drift, data quality, and model performance. Other options do not provide automatic, scalable logging of production inference data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set up a Databricks job to periodically query the endpoint.
Why it's wrong here
Periodically querying the endpoint would generate synthetic traffic and does not capture actual inference requests. It also adds unnecessary load and cost. This approach does not provide automatic logging of real predictions and cannot monitor drift or data quality effectively.
- ✗
Use the model's signature to validate incoming data.
Why it's wrong here
The model signature validates the schema of input data at inference time, but it does not log the data or compute drift metrics. It only ensures that the input conforms to the expected format. Monitoring drift and data quality requires capturing and analyzing historical inference data, which the signature alone cannot provide.
- ✗
Configure MLflow tracking to log model predictions.
Why it's wrong here
MLflow tracking is used during model training and experimentation to log parameters, metrics, and artifacts. It does not automatically capture inference requests from a deployed serving endpoint. While you can manually log predictions, it lacks the automatic, scalable logging needed for production monitoring of drift and data quality.
- ✓
Enable inference tables on the serving endpoint.
Why this is correct
Inference tables automatically capture request and response data from a Model Serving endpoint and store them in a Delta table. This enables monitoring of prediction drift, data quality, and model performance by analyzing the logged data. It is the native Databricks feature designed for this purpose, integrating with Lakehouse Monitoring.
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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
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