Databricks Machine Learning Associate Question 33
Select 2You are tasked with building a machine learning pipeline in Databricks using Databricks Jobs. The pipeline consists of three tasks: (1) Data Preprocessing, (2) Model Training, and (3) Model Evaluation. The Data Preprocessing task must run first, followed by the Model Training task, and finally the Model Evaluation task. How can you configure the Databricks Job to ensure these dependencies are met?
- A
Configure the tasks in the Databricks Job to have dependencies, with Model Training dependent on Data Preprocessing and Model Evaluation dependent on Model Training.
- B
Use a single notebook for all tasks and execute them sequentially within the notebook to maintain the order.
- C
Create three separate Databricks Jobs for each task and schedule them one after the other with appropriate time intervals.
- D
Set up the Databricks Job with each task as a separate step, defining the execution order using task dependencies.
- E
Use Databricks Workflows to define the pipeline and manage task dependencies instead of Databricks Jobs.
Show answer and explanation
Correct answers: A, D
Explanation
To orchestrate multi-task ML workflows in Databricks, Databricks Jobs allows you to configure task dependencies to ensure tasks execute in the correct order. This approach is modular, maintainable, and leverages Databricks' built-in capabilities for managing workflows. Combining task dependencies with well-defined steps in a single Databricks Job ensures robust orchestration of ML pipelines.
- A. Correct.
Correct: Configuring task dependencies within Databricks Jobs ensures that the tasks are executed in the correct order and allows for more robust pipeline orchestration.
- B. Incorrect.
Incorrect: While using a single notebook can maintain execution order, it is not the best practice for orchestrating multi-task workflows in Databricks since it reduces modularity and reusability.
- C. Incorrect.
Incorrect: Creating separate Databricks Jobs and scheduling them with time intervals is inefficient and does not guarantee that the tasks will execute in the correct order, especially if a task takes longer than expected.
- D. Correct.
Correct: Defining execution order through task dependencies in Databricks Jobs is a best practice for orchestrating multi-task workflows.
- E. Incorrect.
Incorrect: Databricks Workflows is not a feature distinct from Databricks Jobs. Multi-task workflows are orchestrated within Databricks Jobs by defining task dependencies.