Databricks-ML-Pro Model Deployment Practice Question
A team has deployed a model to Databricks Model Serving and enabled inference tables. They notice that the inference table contains request and response payloads but no ground truth labels. They want to automatically join ground truth labels for monitoring. What should they do?
⚠ Common exam trap
The trap here is believing that inference tables automatically capture ground truth labels or that a simple configuration flag can enable it, when in reality ground truth must be joined from an external source.
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
✓
Configure a Databricks SQL query that joins the inference table with a ground truth Delta table and schedule it to refresh a monitoring metric.
Databricks Model Serving inference tables capture request and response payloads but do not include ground truth labels. To monitor model quality, you must join the inference table with a separate ground truth table. This is typically done by scheduling a query or pipeline that performs the join and computes metrics, which can then be used for model monitoring.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the MLflow Model Registry webhook to trigger a job that updates the inference table with ground truth labels.
Why it's wrong here
MLflow Model Registry webhooks trigger on model version transitions, not on inference events. They cannot update inference tables with ground truth labels. While a webhook could start a job, it would not have access to the necessary label data unless that data is already stored elsewhere. This approach does not address the core need to join labels with inference logs.
- ✗
Set the 'log_inputs' and 'log_outputs' flags to true in the endpoint configuration, which will automatically include ground truth labels.
Why it's wrong here
log_inputs and log_outputs control whether request and response payloads are logged to the inference table. They do not include ground truth labels. Ground truth is external to the inference request and must be joined from another source. Enabling these flags only ensures that the model inputs and outputs are captured, not the actual outcomes.
- ✗
Enable 'auto_capture_config' with 'ground_truth_column' set to the label column name in the inference table.
Why it's wrong here
The auto_capture_config in Model Serving controls payload logging, not ground truth ingestion. There is no ground_truth_column parameter in the auto capture configuration. Ground truth labels are not automatically captured from the inference payload because they are typically not available at inference time. This option misrepresents the capabilities of the auto capture feature.
- ✓
Configure a Databricks SQL query that joins the inference table with a ground truth Delta table and schedule it to refresh a monitoring metric.
Why this is correct
Inference tables store request and response data, but ground truth labels must be provided separately. The standard approach is to create a Delta table containing ground truth labels and join it with the inference table using a unique request ID. A scheduled Databricks SQL query or a Lakeflow pipeline can perform this join and compute monitoring metrics.
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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-Pro 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-Pro exam.