Databricks-ML-Assoc ML Workflows Practice Question
A data scientist needs to run the same feature-engineering notebook against three different parameter sets in a Databricks Job. She wants each parameter set to execute independently and in parallel, with separate logs. Which Job feature should she use?
⚠ Common exam trap
The trap here is treating cluster autoscaling or a single parameterized task as a way to run multiple parameter sets, when iteration requires a for-each task over an array.
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
✓
A for-each task that iterates over a parameter array and runs the notebook once per element.
The for-each task is designed for iterating a nested task over an input array, producing one task run per element with its own logs and status. That gives the independent, parallel executions the scientist needs while keeping everything in one workflow. Passing a JSON string, creating separate jobs, or relying on autoscaling all fail to produce per-parameter task runs with isolated logs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A job cluster with autoscaling enabled and a single task.
Why it's wrong here
Autoscaling adjusts worker count for a cluster but does not create multiple task runs or vary parameters. A single task still executes the notebook once with one parameter set, so the three variations are never launched independently. Cluster sizing is orthogonal to parameter iteration.
- ✗
A single notebook task with the three parameter sets passed as a JSON string argument.
Why it's wrong here
Passing one JSON string runs the notebook only once; the notebook would have to loop internally, producing one task run and interleaved logs rather than three independent parallel executions. It does not give per-parameter isolation in the job UI and cannot parallelize across cluster resources automatically. This does not meet the stated requirement.
- ✗
Three separate jobs created from the same notebook, each with its own parameter.
Why it's wrong here
Creating three jobs would produce three independent runs, but they are not organized as one workflow, and scheduling or monitoring them together requires extra effort. It also duplicates configuration and does not provide the for-each iteration model with a shared parameter array. This is more maintenance than the scenario calls for.
- ✓
A for-each task that iterates over a parameter array and runs the notebook once per element.
Why this is correct
A for-each task takes an input array and runs its nested task once per element, optionally in parallel. Each iteration is a separate task run with its own logs and status, which matches the requirement for independent parallel executions with distinct parameter values. The nested notebook receives the current element as a parameter.
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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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