Exam AWS Certified Solutions Architect - Associate SAA-C02 All Questions

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Exam AWS Certified Solutions Architect - Associate SAA-C02 topic 1 question 523 discussion

Exam question from Amazon's AWS Certified Solutions Architect - Associate SAA-C02

Question #: 523
Topic #: 1

[All AWS Certified Solutions Architect - Associate SAA-C02 Questions]

A company has an application that scans millions of connected devices for security threats and pushes the scan logs to an Amazon S3 bucket. A total of 70 GB of data is generated each week, and the company needs to store 3 years of data for historical reporting. The company must process, aggregate, and enrich the data from Amazon S3 by performing complex analytical queries and joins in the least amount of time. The aggregated dataset is visualized on an Amazon QuickSight dashboard.
What should a solutions architect recommend to meet these requirements?

A. Create and run an ETL job in AWS Glue to process the data from Amazon S3 and load it into Amazon Redshift. Perform the aggregation queries on Amazon Redshift.
B. Use AWS Lambda functions based on S3 PutObject event triggers to copy the incremental changes to Amazon DynamoDB. Perform the aggregation queries on DynamoDB.
C. Use AWS Lambda functions based on S3 PutObject event triggers to copy the incremental changes to Amazon Aurora MySQL. Perform the aggregation queries on Aurora MySQL.
D. Use AWS Glue to catalog the data in Amazon S3. Perform the aggregation queries on the cataloged tables by using Amazon Athena. Query the data directly from Amazon S3.

Show Suggested Answer

Suggested Answer: A 🗳️

by KhushRoh at Dec. 15, 2021, 12:43 a.m.

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Comments

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MikeDuB

Highly Voted 3 years, 5 months ago

Anybody thinking D?

upvoted 12 times

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tin2022

Highly Voted 3 years, 5 months ago

Complicated analytical queries and joins means Redshift

upvoted 7 times

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jw1806

Most Recent 2 years, 8 months ago

Selected Answer: A

redshift for complex analytics. I do it everyday for a living.

upvoted 1 times

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Fyssy

2 years, 9 months ago

D First Use AWS Glue to catalog the data in Amazon S3

upvoted 1 times

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cloud_collector

2 years, 10 months ago

Selected Answer: A

Retrieving information from an Amazon Redshift data warehouse involves running complex queries on extremely large amounts of data, which can take a long time to process. https://docs.aws.amazon.com/redshift/latest/dg/c-query-tuning.html

upvoted 2 times

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bora4motion

2 years, 10 months ago

Selected Answer: A

I go with A. Read with Redshift from S3 and use Quicksight. These ones go hand in hand.

upvoted 2 times

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naveenagurjara

2 years, 11 months ago

Selected Answer: A

...complicated analytical queries and joins.. Redshift. Simple queries Athena

upvoted 3 times

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Karthikeyan_nick

3 years, 1 month ago

Option A: Redshift - Built for running complex queries that can involve multiple data sources Join Query – Redshift is faster than Athena due to the ability to easily handle traditional joins and relational workloads

upvoted 3 times

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Lakhsmi

3 years, 2 months ago

Selected Answer: D

I think that might be D. Moving 70 GB of data using glue will be time consuming and using redshift incurs a cost too. where as with Glue and Athena we can directly query the data in S3 itself. https://docs.aws.amazon.com/athena/latest/ug/data-sources-glue.html

upvoted 5 times

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FF11

3 years, 5 months ago

Selected Answer: A

A is correct.

upvoted 2 times

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BlassArun

3 years, 5 months ago

Ans is A

upvoted 2 times

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Alcpt

3 years, 5 months ago

Answer could be A: https://bryteflow.com/face-off-aws-athena-vs-redshift-spectrum/ Both Athena and Redshift Spectrum are serverless. Athena is dependent on the combined resources AWS provides to compute query results while resources at the disposal of Redshift Spectrum depend on your Redshift cluster size. With Redshift Spectrum you have the freedom to increase the Redshift cluster size to boost compute power (in case of complex queries involving large datasets or needing faster response) which can be expensive. With Athena you use whatever resources AWS allocates to your query and this can vary quite a bit in peak usage times.

upvoted 4 times

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