Exam AWS Certified Solutions Architect - Associate SAA-C03 topic 1 question 975 discussion

A (Athena) / C (Redshift Spectrum) / B (S3 Select): they can’t read client-side–encrypted objects, so they’d fail unless you first re-upload plaintext or switch to SSE—extra work/cost the question doesn’t allow.

https://aws.amazon.com/blogs/storage/how-to-optimize-querying-your-data-in-amazon-s3/

Seems like "sample moving averages" is a typo of "simple moving averages". A simple moving average is formed by computing the average price of a security over a specific number of periods, but let's just ignore it if you don't care one bit haha A - Athena is serverless, works perfectly with Parquet files, can handle encrypted data, and super cost-effective because you only pay for the amount of data you query. B - Amazon S3 Select is fast for simple filtering or lightweight queries, not for complex queries like calculating moving averages. C - Redshift with Redshift Spectrum is extremely powerful and competent to crunch heavy-duty data, so overkill for occasional SQL queries. D - EMR is scalable and fast but is usually for heavy data pipelines, not casual SQL analysis. Like C, this is overkill.

it is a simple SQL just in S3, believe it is a use case of Athena which supports client-side decryption

Key: CSE-KMS A,B,C not support decrypted by CSE-KMS

A. Athena would be overkill and more expensive for simple queries on specific files. C. Amazon Redshift with Redshift Spectrum would be significantly more complex and costly to set up and maintain for occasional queries. D. EMR Serverless with Apache SparkSQL would also be more complex and likely more expensive for this use case. S3 Select provides the right balance of functionality and cost-effectiveness for the described scenario, making it the most suitable choice

A is my choice.

Athena for sql