Databricks-DE-Assoc Working with Lakeflow Jobs Practice Question
A data engineer wants to pass a file path from Task A to Task B in a Lakeflow Job. Task A is a notebook that computes the path, and Task B is a notebook that reads from that path. Which mechanism should the engineer use to share the value between tasks?
⚠ Common exam trap
The trap here is assuming job parameters can be written by a task, when they are actually read-only inputs set at submission time.
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
✓
Use task values: Task A sets a task value with dbutils.jobs.taskValues.set, and Task B retrieves it with dbutils.jobs.taskValues.get.
Task values are the intended feature for passing small, dynamically computed values between tasks in a Lakeflow Job. Task A sets a value using dbutils.jobs.taskValues.set, and Task B retrieves it with dbutils.jobs.taskValues.get, referencing the task that set it. Job parameters are read-only, global variables are not shared across tasks, and using a Delta table is unnecessarily complex for this use case.
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 job parameters: Task A writes the path to a job parameter, and Task B reads it.
Why it's wrong here
Job parameters are set at job submission time and are read-only during the run; they cannot be modified by a task. Task A cannot write to a job parameter for Task B to read. Job parameters are for passing static or externally supplied values into the job, not for inter-task communication. Therefore, this approach does not work for dynamically sharing a value computed in Task A.
- ✗
Use the job's run ID: Task A writes the path to a Delta table keyed by run ID, and Task B queries it.
Why it's wrong here
While using a Delta table to store intermediate values is possible, it is overly complex and introduces external state management. It requires Task A to write to a table and Task B to read from it, with coordination on run ID. Task values are specifically built for this purpose and are simpler and more efficient for passing small values like a file path between tasks in the same job run.
- ✓
Use task values: Task A sets a task value with dbutils.jobs.taskValues.set, and Task B retrieves it with dbutils.jobs.taskValues.get.
Why this is correct
Task values are designed for passing small values between tasks in a Lakeflow Job. Task A can set a task value using dbutils.jobs.taskValues.set, and Task B can retrieve it using dbutils.jobs.taskValues.get, specifying the originating task and key. This is the recommended way to share dynamic values like file paths within a job run.
- ✗
Use a global variable in the notebook: Task A sets a global variable, and Task B reads it.
Why it's wrong here
Notebooks in separate tasks run in separate execution contexts, often on different clusters, so global variables are not shared between them. A variable set in Task A's notebook is not visible to Task B's notebook. Global variables are scoped to a single notebook session and do not persist across tasks, making this an invalid method for inter-task communication.
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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-DE-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-DE-Assoc exam.