Databricks-DE-Assoc Working with Lakeflow Jobs Practice Question
Which TWO of the following statements are true regarding the behavior and capabilities of Databricks Jobs parameters and values?
⚠ Common exam trap
Candidates often think job-level parameters are immutable and cannot be altered at runtime, missing the flexibility of the Databricks API and CLI overrides.
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
✓
Task values can be used to pass small amounts of data, such as counts or status codes, from one task to a downstream task.
Databricks Jobs support parameterized runs, enabling dynamic workflows where values are passed at runtime or configured statically. Understanding how parameters propagate to tasks and how task values pass outputs between tasks is essential for building modular, reusable data engineering pipelines within the Lakeflow framework.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Task values can be used to pass small amounts of data, such as counts or status codes, from one task to a downstream task.
Why this is correct
The dbutils.jobs.taskValues API allows tasks to set key-value pairs that downstream tasks can retrieve. This facilitates dynamic branching and parameter passing based on runtime metrics generated during the execution of upstream workflow steps.
- ✓
Job parameters defined at the workflow level can be overridden when triggering a run via the Databricks CLI or REST API.
Why this is correct
When triggering job executions using automation tools like the CLI or REST API, you can supply custom parameters that override default values configured in the job definition. This provides flexibility for CI/CD pipelines and external orchestrators.
- ✗
Job parameters are automatically persisted in a Unity Catalog volume after the workflow completes successfully.
Why it's wrong here
Job parameters exist only in memory during the execution lifecycle of the workflow run. They are not automatically written or persisted to Unity Catalog volumes unless explicitly handled by task code writing output files.
- ✗
Notebook tasks cannot accept parameter values passed from the parent job orchestration configuration.
Why it's wrong here
Notebook tasks natively accept widget parameters configured in the job definition. Databricks automatically injects job parameters and task parameters into notebook widgets matching the parameter names defined in the workflow.
- ✗
Task values support passing large dataframes containing millions of rows between dependent tasks.
Why it's wrong here
Task values have strict size limitations and are strictly designed for small metadata payloads like file paths, metrics, or counts. Attempting to pass large dataframes will cause runtime exceptions due to payload restrictions.
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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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