Databricks-ML-Assoc ML Workflows Practice Question
A data science team is using Databricks Jobs to orchestrate a machine learning pipeline. The pipeline includes a task that trains a model and a subsequent task that evaluates the model. The evaluation task must access the model version produced by the training task. Which mechanism should the team use to pass the model version between tasks?
⚠ Common exam trap
The trap here is assuming that querying the latest model version is equivalent to the version produced by the training task, which can be incorrect if other runs occur.
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 to pass the model version as a string between tasks.
Task values are the native Databricks Jobs feature for passing small data between tasks. The training task can set a task value with the model version, and the evaluation task retrieves it using dbutils.jobs.taskValues.get(). This avoids ambiguity and external storage, ensuring the correct version is used.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Write the model version to a Delta table and have the evaluation task read it.
Why it's wrong here
While possible, using a Delta table to pass a single value introduces unnecessary overhead and potential race conditions. It also requires managing a table just for coordination. Task values are designed specifically for this purpose and are simpler and more reliable for small data.
- ✗
Store the model version in a file in DBFS and have the evaluation task read it.
Why it's wrong here
Using a file in DBFS is a manual approach that lacks atomicity and can fail if multiple runs write to the same path. It also requires cleanup and does not integrate with the Jobs orchestration. Task values are the native, secure, and reliable method for passing data between tasks.
- ✗
Use MLflow's tracking API to query the latest model version in the evaluation task.
Why it's wrong here
Querying the latest model version might seem convenient, but it can be ambiguous if multiple runs occur concurrently or if the training task does not produce the absolute latest version. It also couples the evaluation task to the timing of the training task, which can lead to incorrect version selection. Task values provide explicit passing.
- ✓
Use task values to pass the model version as a string between tasks.
Why this is correct
Databricks Jobs support task values, which allow tasks to pass small amounts of data (like a model version number) to downstream tasks. The training task can set a task value with the model version, and the evaluation task can retrieve it using dbutils.jobs.taskValues.get(). This is the recommended way to pass dynamic values between tasks in a workflow.
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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.