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Answer: When they are working interactively with a small amount of data
## Explanation A single-node cluster is appropriate when working interactively with a small amount of data because: 1. **Cost Efficiency**: Single-node clusters are less expensive than multi-node clusters since they don't require worker nodes. 2. **Simplified Setup**: They have minimal configuration overhead and are ideal for development, testing, or small-scale interactive work. 3. **Interactive Development**: For exploratory data analysis, prototyping, or working with small datasets, a single-node cluster provides sufficient compute power without the complexity of distributed computing. **Why other options are incorrect**: - **B**: Automated reports that need to be refreshed quickly typically benefit from multi-node clusters for parallel processing. - **C**: Working with SQL in Databricks SQL doesn't necessarily require a single-node cluster; SQL warehouses can be configured with multiple nodes for better performance. - **D**: Concerns about automatic scaling with larger data would suggest using multi-node clusters with autoscaling capabilities. - **E**: Manually running reports with large amounts of data requires multi-node clusters for distributed processing to handle the data volume efficiently.
Author: Keng Suppaseth
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Which of the following describes a scenario in which a data engineer will want to use a single-node cluster?
A
When they are working interactively with a small amount of data
B
When they are running automated reports to be refreshed as quickly as possible
C
When they are working with SQL within Databricks SQL
D
When they are concerned about the ability to automatically scale with larger data
E
When they are manually running reports with a large amount of data