Databricks-ML-Assoc ML Workflows Practice Question
An ML engineer is setting up a Databricks Job to retrain a production model nightly. The job must run only when upstream data validation succeeds, notify the team on failure, and avoid retraining when the input data has not changed. Which TWO capabilities of Databricks Jobs directly support these requirements? (Choose two.)
⚠ Common exam trap
The trap here is treating performance features such as autoscaling or Photon as workflow controls, when conditional execution and alerting are handled by condition tasks and notifications.
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
✓
Job-level and task-level notifications that alert on failure or duration thresholds
Condition tasks provide the branching needed to run training only when validation passes and data has changed, while task and job notifications deliver the failure alerts. Together they implement the conditional retraining and notification requirements, whereas autoscaling, repair runs, and Photon address performance or recovery rather than control flow and alerting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cluster autoscaling to add workers when the training dataset grows
Why it's wrong here
Autoscaling adjusts worker count based on load to control cost and throughput during execution. It has no awareness of upstream validation results or data change detection, so it cannot gate the retraining step or suppress a run when the input data is identical to the previous night's data.
- ✓
Job-level and task-level notifications that alert on failure or duration thresholds
Why this is correct
Databricks Jobs support email, webhook, and other notifications configured per task or for the whole job, triggered on failure, success, or duration limits. Configuring failure notifications satisfies the requirement to inform the team when the nightly retraining pipeline breaks, complementing the branching logic that controls whether training runs at all.
- ✗
Job compute configured with photon acceleration for the training tasks
Why it's wrong here
Photon accelerates SQL and DataFrame operations on supported compute, improving query performance for data preparation steps. It does not evaluate validation outcomes, detect unchanged input data, or send alerts, so it contributes speed but not the conditional execution and notification behavior the scenario requires.
- ✓
Task dependencies with condition tasks to branch based on upstream task outcomes
Why this is correct
Condition tasks evaluate a boolean expression over upstream task values and route execution down one of two branches, so the job can skip retraining when validation fails or when the data hash is unchanged. This directly implements the gating and skip logic the engineer needs without running the training task unnecessarily.
- ✗
Repair runs to rerun only failed tasks after a partial failure
Why it's wrong here
Repair runs let an operator re-execute only the tasks that failed or were skipped, which saves time after an incident. This is a recovery convenience rather than a control-flow mechanism, so it does not decide whether training should proceed based on validation results or unchanged input data.
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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
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