Databricks-ML-Assoc ML Workflows Practice Question
An ML engineer is using Databricks Jobs to orchestrate a machine learning pipeline that includes data ingestion, feature engineering, model training, and batch scoring. The engineer wants to ensure that the pipeline is reproducible, handles failures gracefully, and allows for easy debugging of individual tasks. Which TWO features of Databricks Jobs should the engineer leverage to meet these requirements? (Choose two.)
⚠ Common exam trap
The trap here is focusing on code versioning or cluster configuration as the primary means of orchestration and failure recovery, when the core Job features for those needs are dependencies and repair runs.
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
✓
Repair runs to re-execute only failed or skipped tasks without rerunning successful ones.
Task dependencies define execution order and conditional branching, ensuring that downstream tasks like training run only after upstream tasks succeed and enabling failure handling. Repair runs allow re-executing only failed or skipped tasks, which is critical for debugging and recovering from failures without rerunning the entire pipeline. Together, these Databricks Jobs features provide orchestration, graceful failure handling, and efficient debugging.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Notebook parameters to pass dynamic values between tasks.
Why it's wrong here
Notebook parameters allow you to pass values into a notebook task, which can be useful for configuration. However, they do not provide dependency management, failure handling, or reproducibility of the pipeline as a whole. Parameters alone cannot orchestrate task order or enable repair runs, so they do not meet the stated requirements.
- ✗
Git integration to version-control notebooks and reference specific commits.
Why it's wrong here
Git integration in Databricks Repos allows you to version-control notebooks and reference specific commits, which supports reproducibility of code. However, it does not handle pipeline orchestration, task dependencies, or failure recovery. The engineer still needs Job-level features like dependencies and repair runs to meet the orchestration and debugging requirements.
- ✓
Repair runs to re-execute only failed or skipped tasks without rerunning successful ones.
Why this is correct
Repair runs allow you to rerun only the tasks that failed or were skipped, preserving the results of successful tasks. This is essential for graceful failure handling and efficient debugging, as the engineer can fix an issue and repair the run without redoing the entire pipeline. It directly addresses the requirement to handle failures and debug individual tasks.
- ✓
Task dependencies to define the order of execution and enable conditional branching.
Why this is correct
Task dependencies in Databricks Jobs allow you to specify that a task runs only after its upstream tasks succeed, and you can add conditions to branch based on task outcomes. This provides graceful failure handling and clear orchestration. By defining dependencies, the engineer ensures that training runs only after feature engineering completes, and failures halt downstream tasks, making debugging easier.
- ✗
Job clusters that are created for each run and terminated upon completion.
Why it's wrong here
Job clusters are ephemeral and provide isolation, but they do not by themselves ensure reproducibility or graceful failure handling. They also do not facilitate debugging individual tasks beyond providing logs. While useful for cost and isolation, they are not a specific feature for reproducibility or failure handling in the pipeline orchestration sense.
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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.