Databricks-ML-Assoc ML Workflows Practice Question
An ML engineer is configuring a Databricks Job to automate nightly retraining of a model. The job must (1) run only after the upstream feature engineering job succeeds, and (2) notify the team via email if the training task fails. Which TWO configurations satisfy these requirements? (Choose two.)
⚠ Common exam trap
The trap here is relying on time-based scheduling or notebook chaining to enforce order, when only a declared task dependency guarantees the upstream job succeeded first.
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 the training task with a depends_on entry referencing the feature engineering task, and add an email notification for the job's failure event.
Databricks Jobs provide first-class task dependencies via depends_on, which guarantees a downstream task runs only after its upstream task succeeds, and job-level notifications that can email the team on failure. These native features satisfy both the ordering and alerting requirements without custom code or fragile timing assumptions.
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 schedule with a cron expression that triggers the training task five minutes after the feature engineering job is expected to finish, and enable email alerts on failure.
Why it's wrong here
Time-based scheduling cannot guarantee that the upstream job has actually succeeded; if feature engineering runs long or fails, training may start against stale or missing data. While email alerts would satisfy notification, the ordering requirement is not reliably met, so this configuration is not appropriate.
- ✓
Configure the training task with a depends_on entry referencing the feature engineering task, and add an email notification for the job's failure event.
Why this is correct
The depends_on field in a Databricks Job task definition enforces that the referenced upstream task completes successfully before the dependent task runs. Pairing that with a job-level email notification on failure satisfies both the ordering and alerting requirements, making this a correct configuration for the scenario.
- ✓
Add a task dependency so the training task depends on the upstream feature engineering task, and configure an email notification on the job for failed runs.
Why this is correct
Task dependencies in Databricks Jobs ensure the training task starts only after the upstream feature engineering task completes successfully, and job-level email notifications on failure alert the team. Together they meet both the ordering and alerting requirements without extra orchestration code, making this a valid configuration.
- ✗
Chain the notebooks by calling dbutils.notebook.run from the training notebook to invoke the feature engineering notebook, and rely on the notebook's exception handling to email the team.
Why it's wrong here
Calling dbutils.notebook.run from the training notebook inverts the intended order and couples orchestration into application code, which is harder to monitor and retry. It also lacks built-in job failure notifications, so the team would not receive the required email unless custom code is written, making it a poor fit.
- ✗
Set up an MLflow webhook that triggers the training notebook whenever a new model version is registered, and configure the webhook to send email on failure.
Why it's wrong here
MLflow webhooks react to registry events such as model version creation or transition, not to the completion of an upstream feature engineering job. They cannot enforce that features are ready before training begins, and they are not designed to send failure notifications for Databricks Job tasks, so this does not meet the requirements.
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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.