Databricks-ML-Assoc ML Workflows Practice Question
You are building a pipeline in Databricks and need to ensure that a training job only runs after the upstream data preparation job has successfully completed. Which Databricks feature should you use?
⚠ Common exam trap
Candidates often try to manage dependencies using custom notebook code or external scheduling tools. They fail to recognize that Databricks Workflows has built-in native support for task dependencies.
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 Databricks Workflows task dependencies.
Databricks Workflows allows for the creation of complex pipelines using task dependencies. By defining a dependency between the data preparation task and the training task, the orchestrator ensures the correct execution order. This dependency management is foundational for building reliable end-to-end ML pipelines, ensuring that models are always trained on processed, valid data and preventing failures caused by missing or stale input data during the automated training process.
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 the mlflow.end_run() command at the end of the data prep script.
Why it's wrong here
MLflow is for tracking experiments and managing model lifecycle, not for task orchestration or job flow control. Using MLflow for dependency management would be ineffective because it lacks the capability to trigger subsequent jobs based on the outcome of previous tasks in a distributed compute environment.
- ✗
Use Delta Live Tables (DLT) expectations to trigger the job.
Why it's wrong here
DLT expectations are used for data quality validation, not for triggering separate Databricks Jobs. While DLT can be part of a pipeline, it does not provide the orchestration logic to trigger an external job or model training script based on the successful conclusion of a data transformation task.
- ✓
Use Databricks Workflows task dependencies.
Why this is correct
Databricks Workflows enables users to build multi-task jobs where tasks are linked by success dependencies. This is the native and most efficient way to ensure that a training task waits for the data preparation task to complete, maintaining a strict and reliable execution order within the pipeline.
- ✗
Use a cron schedule with a 30-minute delay for the training job.
Why it's wrong here
Using time-based delays is unreliable and inefficient. If the data preparation job takes longer than expected, the training job will fail or run on stale data. Orchestrating based on completion status rather than clock time is the only reliable way to handle varying data processing times.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
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