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Exam AWS Certified Solutions Architect - Associate SAA-C03 All Questions

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

Exam question from Amazon's AWS Certified Solutions Architect - Associate SAA-C03
Question #: 975
Topic #: 1
[All AWS Certified Solutions Architect - Associate SAA-C03 Questions]

A weather forecasting company collects temperature readings from various sensors on a continuous basis. An existing data ingestion process collects the readings and aggregates the readings into larger Apache Parquet files. Then the process encrypts the files by using client-side encryption with KMS managed keys (CSE-KMS). Finally, the process writes the files to an Amazon S3 bucket with separate prefixes for each calendar day.

The company wants to run occasional SQL queries on the data to take sample moving averages for a specific calendar day.

Which solution will meet these requirements MOST cost-effectively?

  • A. Configure Amazon Athena to read the encrypted files. Run SQL queries on the data directly in Amazon S3.
  • B. Use Amazon S3 Select to run SQL queries on the data directly in Amazon S3.
  • C. Configure Amazon Redshift to read the encrypted files. Use Redshift Spectrum and Redshift query editor v2 to run SQL queries on the data directly in Amazon S3.
  • D. Configure Amazon EMR Serverless to read the encrypted files. Use Apache SparkSQL to run SQL queries on the data directly in Amazon S3.
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Suggested Answer: A 🗳️
by swati1508 at Aug. 9, 2024, 11:11 p.m.
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FlyingHawk
1 week, 3 days ago
Selected Answer: A
https://aws.amazon.com/blogs/storage/how-to-optimize-querying-your-data-in-amazon-s3/
upvoted 1 times
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LeonSauveterre
4 weeks, 1 day ago
Selected Answer: A
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.
upvoted 2 times
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EllenLiu
1 month, 1 week ago
Selected Answer: A
it is a simple SQL just in S3, believe it is a use case of Athena which supports client-side decryption
upvoted 1 times
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Cpso
2 months, 1 week ago
Selected Answer: D
Key: CSE-KMS A,B,C not support decrypted by CSE-KMS
upvoted 1 times
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Ronanh
3 months, 4 weeks ago
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
upvoted 2 times
elmyth
3 months, 1 week ago
S3 Select is deprecated, right answer is A, clue word is "occasional", otherwise it would be Redshift.
upvoted 3 times
FlyingHawk
1 week, 3 days ago
Thank for sharing this, I just checked, After careful consideration, we have made the decision to close new customer access to Amazon S3 Select and Amazon S3 Glacier Select, effective July 25, 2024. Amazon S3 Select and Amazon S3 Glacier Select existing customers can continue to use the service as usual.
upvoted 1 times
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dhewa
5 months, 2 weeks ago
Selected Answer: A
A is my choice.
upvoted 2 times
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[Removed]
5 months, 2 weeks ago
Selected Answer: A
A sounds right
upvoted 2 times
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swati1508
5 months, 4 weeks ago
Selected Answer: A
Athena for sql
upvoted 3 times
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