Databricks Certified Data Engineer - Associate
In the context of designing workflows on the Databricks Lakehouse Platform, a data engineering team is evaluating the use of all-purpose clusters versus job clusters, along with the management of Databricks Runtime versions. Considering the need for cost efficiency, scalability, and control over the execution environment, which of the following statements best describes the distinction between all-purpose clusters and job clusters, and how Databricks Runtime versioning is handled? (Choose one correct option)
Simulated
A
All-purpose clusters are solely for ad-hoc, interactive analysis with no capability for production jobs, and Databricks Runtime versions are automatically selected by the platform without user intervention.
B
All-purpose clusters support collaborative, interactive data processing and analysis across multiple users and notebooks, whereas job clusters are ephemeral, created for specific job executions, and terminate post-completion. Users have the flexibility to manually select the Databricks Runtime version for both cluster types to ensure compatibility and meet specific workload requirements.
C
Job clusters are designed for both development and production environments, offering more flexibility than all-purpose clusters, which are restricted to interactive analysis. Databricks Runtime versions are automatically updated to the latest version, with no option for manual selection.
D
There is no functional difference between all-purpose clusters and job clusters; however, job clusters provide an option to manually select the Databricks Runtime version, while all-purpose clusters are limited to automatic version updates.
Explanation:
The correct answer highlights the primary use cases for all-purpose and job clusters within the Databricks Lakehouse Platform. All-purpose clusters are optimized for interactive, collaborative work, supporting multiple users and notebooks, making them ideal for development and exploratory analysis. Job clusters, on the other hand, are designed for running specific jobs or tasks efficiently, with the cluster terminating once the job is completed to optimize resource usage and costs. Importantly, both cluster types allow users to manually select the Databricks Runtime version, offering control over the execution environment to ensure compatibility with specific libraries or features required for various workloads. This flexibility is crucial for meeting diverse operational requirements and optimizing performance across different stages of data processing and analysis.