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Answer: When they are working interactively with a small amount of data
## Explanation A single node cluster is most appropriate when working interactively with a small amount of data because: 1. **Cost Efficiency**: Single node clusters are less expensive as they don't require multiple worker nodes 2. **Interactive Development**: For exploratory data analysis, development, and testing with small datasets, a single node is sufficient 3. **Reduced Overhead**: No network communication overhead between nodes 4. **Simplified Debugging**: Easier to debug issues when working on a single node **Why other options are incorrect:** - **B**: Automated reports that need quick refresh would benefit from multi-node clusters for parallel processing - **C**: Working with SQL in Databricks SQL doesn't necessarily require a single node cluster; it depends on the data size and complexity - **D**: Concern about automatic scaling with larger data indicates a need for multi-node clusters with auto-scaling capabilities - **E**: Manual reports with large amounts of data would require multi-node clusters for distributed processing Single node clusters are ideal for development, testing, and small-scale interactive work where the data fits comfortably in memory and doesn't require distributed processing.
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