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Answer: All of the above.
When choosing between SQL Serverless and Spark clusters for query execution, it is important to consider their key differences. SQL Serverless is more suitable for lightweight queries (Option A), while Spark clusters are better for complex data processing tasks. Spark clusters are generally more cost-effective for large-scale projects (Option B), but SQL Serverless can be more affordable for smaller projects. Spark clusters provide better support for machine learning and AI workloads (Option C) compared to SQL Serverless. Therefore, all of these factors should be considered when selecting the appropriate compute solution, making Option D the correct answer.
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You are tasked with creating and executing queries using a compute solution that leverages SQL Serverless and Spark clusters in Azure Synapse Analytics. What are some of the key differences between SQL Serverless and Spark clusters that you should consider when choosing the appropriate compute solution for a specific query?
A
SQL Serverless is more suitable for lightweight queries, while Spark clusters are better for complex data processing tasks.
B
SQL Serverless is more cost-effective for small-scale projects, while Spark clusters are more expensive.
C
Spark clusters provide better support for machine learning and AI workloads compared to SQL Serverless.
D
All of the above.
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