Exam AWS Certified Machine Learning - Specialty topic 1 question 55 discussion

Ans C is reasonable

Agree with C. Quicksight cannot handle 100TB each day.

Amazon QuickSight, particularly when using its SPICE (Super-fast, Parallel, In-memory Calculation Engine) feature, has specific data capacity limits. For the Enterprise Edition, SPICE can handle up to 1 billion rows or 1 TB per dataset1. This means that while QuickSight is highly capable, handling 100 TB of data per day would exceed its current capacity limits.

The limit of QuickSight for 1TB is soft limit which can be increased to unlimited number of TBs.

Ans C Because Quicksight Can't handle 100 TB even in Entiripise Quotas for SPICE are as follows: 2,047 Unicode characters for each field 127 Unicode characters for each column name 2,000 columns for each file 1,000 files for each manifest For Standard edition, 25 million (25,000,000) rows or 25 GB for each dataset For Enterprise edition, 1 billion (1,000,000,000) rows or 1 TB for each dataset https://docs.aws.amazon.com/quicksight/latest/user/data-source-limits.html

QuickSight can handle large volumes of data for analytics and visualizations. Some key points: QuickSight scales seamlessly from hundreds of megabytes to many terabytes of data without needing to manage infrastructure. It uses an in-memory engine called SPICE to enable high performance analytics on large datasets. so the choice is B

B. Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team. This solution leverages QuickSight's managed service capabilities for both data processing and visualization, which should minimize the coding effort required to provide the Business team with the necessary insights. However, it's important to note that QuickSight's ability to calculate the precision-recall data depends on its support for the necessary statistical functions or the availability of such calculations in the dataset. If QuickSight cannot perform these calculations directly, option C might be necessary, despite the increased effort.

The question does not ask for processing of 1Tb data. it asks for visuals/predications of that data. So B

C. Considering the large volume of data (100 TB daily), Option C seems to be the most appropriate solution

B it's not correct because of 100tb data size. C is the answer: https://docs.aws.amazon.com/quicksight/latest/user/data-source-limits.html

ANs c is correct

A. NO - we want a dashboard for business B. NO - 100TB is very large, it will not fit in memory (1TB max for SPICE dataset) or return within the 2min limit if delegated to a DB (https://docs.aws.amazon.com/quicksight/latest/user/data-source-limits.html) C. YES - best combination; EMR can distribute the computation of precision-recall for each slice of data D. NO - ES cannot help to generate precision-recall

although C is tempting but goes with B due to less effort

using quick sight for creation of the precision recall with 100 TB every day cann't be done since the max size for quick sight to deal with is : For Standard edition, 25 million (25,000,000) rows or 25 GB for each dataset For Enterprise edition, 1 billion (1,000,000,000) rows or 1 TB for each dataset acc to AWS documentation : https://docs.aws.amazon.com/quicksight/latest/user/data-source-limits.html but we can do it with EMR and latterly use quick sight to visualize the results

Looking at the QuickSight documentation: it has a limit of 1 TB per dataset. So it's necessary a previous layer. Letter C is the correct one.

It's 100TB daily, need EMR to reduce, option C is correct.

Quicksight can handle maximum 1TB data set only. We have 100TB data set so we need EMR. https://docs.aws.amazon.com/quicksight/latest/user/data-source-limits.html