Databricks-ML-Assoc Databricks Machine Learning Practice Question
A machine learning engineer is orchestrating a multi-step training workflow with Databricks Jobs. The workflow must retrain a model, evaluate it, and only register it if a quality threshold is met, otherwise stop without registering. Which approach best implements this conditional logic within the job?
⚠ Common exam trap
The trap here is assuming retries or timeouts can enforce a quality gate, when only condition tasks provide branching control flow.
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
✓
Use task dependencies with a condition task that branches based on the evaluation result.
Condition tasks in Databricks Jobs evaluate an expression and branch execution, enabling a workflow to proceed to registration only when the evaluation metric meets the threshold. This places the quality gate inside the job, so a failing model follows a terminating branch instead of triggering registration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Rely on the job's automatic retry policy to skip registration when evaluation fails.
Why it's wrong here
Retry policies re-run failed tasks; they do not implement branching logic or prevent a downstream registration task from executing. A failed evaluation task would simply retry or mark the job failed, and registration would still be attempted if dependencies allowed. Retries address transient failures, not quality gating, so this does not satisfy the conditional registration requirement.
- ✗
Set a job timeout so that registration never runs if evaluation takes too long.
Why it's wrong here
A timeout bounds total execution time and fails the run when exceeded, but it does not evaluate model quality or branch based on a threshold. Registration could still execute if evaluation finishes quickly with poor metrics. Timeouts handle runaway duration, not conditional logic, so they cannot enforce the quality gate described.
- ✓
Use task dependencies with a condition task that branches based on the evaluation result.
Why this is correct
Databricks Jobs support condition tasks that evaluate a boolean expression and route execution down one of two branches. By placing evaluation before the condition, the job can proceed to the registration task only when the threshold is met and otherwise take a terminating branch. This encodes the conditional gating directly in the workflow without external orchestration.
- ✗
Configure the cluster's autoscaling to pause the job when metrics drop below the threshold.
Why it's wrong here
Autoscaling adjusts worker count based on workload, not model quality. It has no awareness of evaluation metrics and cannot stop a workflow or prevent registration. Using infrastructure scaling to implement a quality gate conflates resource management with control flow, so it cannot deliver the required conditional behavior in this workflow.
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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.