Databricks-ML-Assoc ML Workflows Practice Question
A machine learning team is using Databricks Jobs to orchestrate a multi-step ML pipeline: data ingestion, feature engineering, model training, and batch inference. They need to ensure that if the model training step fails, the batch inference step does not run, and that the entire pipeline can be retried from the failed step. Which Databricks Jobs feature should they use to achieve this?
⚠ Common exam trap
The trap here is assuming that a single notebook or continuous job can provide the same level of dependency management and repair capabilities as Databricks Jobs with task dependencies.
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 each task with a dependency on the previous task and set the job to repair runs on failure.
Databricks Jobs allows defining tasks with dependencies, so the inference task will only run if the training task succeeds. The repair run feature enables retrying a failed run from the failed task, reusing successful task results. This combination provides both conditional execution and efficient recovery.
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 a single notebook with all steps and rely on the notebook's built-in error handling to skip inference on failure.
Why it's wrong here
A single notebook with manual error handling does not provide the orchestration, retry, or dependency management of Databricks Jobs. It also lacks the ability to repair runs from a specific step. This approach is less robust and harder to maintain for multi-step pipelines.
- ✗
Set up a continuous job that always runs all tasks, and use conditional logic in the inference task to check if training succeeded.
Why it's wrong here
A continuous job runs on a schedule or trigger, not on task completion. Conditional logic inside the inference task cannot prevent the task from starting if training failed, because the task would still execute. This does not provide the required dependency control.
- ✓
Configure each task with a dependency on the previous task and set the job to repair runs on failure.
Why this is correct
Databricks Jobs supports task dependencies, ensuring that a task runs only after its dependencies succeed. The repair run feature allows retrying a failed run from the point of failure, skipping already successful tasks. This meets the requirement of conditional execution and efficient retry.
- ✗
Use Databricks Jobs with a cluster that has autoscaling enabled and set the inference task to run only if the training task logs a success metric.
Why it's wrong here
Autoscaling is unrelated to task dependencies. Logging a success metric does not automatically control task execution; Databricks Jobs does not natively trigger tasks based on metric values. This approach would require custom logic and does not guarantee that inference is skipped on failure.
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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.