Databricks-ML-Pro ML Ops Practice Question
A team wants a scheduled Databricks job to automatically retrain a demand-forecasting model whenever the upstream feature table receives new data, and to register the resulting model version only if validation metrics improve. Which Databricks capability should they use to orchestrate this?
⚠ Common exam trap
The trap here is reaching for MLflow webhooks or Delta Live Tables for event-driven retraining, when the table-update trigger on a Databricks job is the native mechanism.
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
✓
A Databricks job with a table-update trigger that runs the training notebook and a conditional registration task.
Databricks jobs support table-update triggers, letting a run start when the feature table changes. A multi-task job can then train the model and use a conditional task to register the version only when validation metrics beat the current production baseline, all within native orchestration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A cron expression on the training notebook that runs every minute to poll for new feature data.
Why it's wrong here
Minute-level polling is wasteful and still not event-driven; it burns cluster time and can miss or double-process updates. It also does not include the conditional registration logic by itself. The scenario calls for reacting to table changes, which a fixed cron poll approximates poorly and at unnecessary cost.
- ✗
A Delta Live Tables pipeline that materializes the feature table and automatically promotes models with the highest accuracy.
Why it's wrong here
Delta Live Tables orchestrates data transformations and expectations, not model training and registry promotion. It can build the feature table but has no built-in concept of validating metrics and conditionally registering a model version. Relying on it for promotion would require unsupported custom behavior outside its intended scope.
- ✗
An MLflow webhook that fires when a new experiment run is created and calls the registration API.
Why it's wrong here
MLflow webhooks react to registry or experiment events, not to new data landing in a feature table. They cannot detect the upstream table update that should initiate retraining. Using a webhook here would either miss the trigger entirely or cause registration without a fresh training run, so it does not satisfy the scenario.
- ✓
A Databricks job with a table-update trigger that runs the training notebook and a conditional registration task.
Why this is correct
Databricks jobs support triggers based on upstream table updates, which is exactly the event described. Chaining a training task with a task that conditionally registers the model based on metrics implements the gated promotion. This uses native orchestration, so no external scheduler or custom polling code is required.
About these practice questions
This Databricks-ML-Pro question is part of Courseiva's 300-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam 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-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.