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Answer: When they are working interactively with a small amount of data
**Explanation:** A single-node cluster is ideal for development, testing, or interactive workloads with small datasets where scaling is not required. Here's why: 1. **Cost-effectiveness**: Single-node clusters are cheaper since they don't require worker nodes 2. **Development and testing**: Perfect for interactive development, debugging, and testing with small data samples 3. **Small data processing**: When working with small amounts of data, a single node is sufficient and avoids unnecessary cluster overhead 4. **Interactive work**: For exploratory data analysis or ad-hoc queries where immediate results are needed **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 within Databricks SQL doesn't necessarily require a single-node cluster; it depends on the data volume and performance requirements - **D**: Concern about automatic scaling with larger data suggests a need for multi-node clusters that can scale - **E**: Manually running reports with large amounts of data requires multi-node clusters for distributed processing Single-node clusters are best suited for lightweight, interactive workloads where the data fits comfortably in memory and processing requirements are minimal.
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