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Question 63 - SAP-C02 discussion

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A company runs an loT platform on AWS loT sensors in various locations send data to the company's Node js API servers on Amazon EC2 instances running behind an Application Load Balancer The data is stored in an Amazon RDS MySQL DB instance that uses a 4 TB General Purpose SSD volume

The number of sensors the company has deployed in the field has increased over time and is expected to grow significantly The API servers are consistently overloaded and RDS metrics show high write latency

Which of the following steps together will resolve the issues permanently and enable growth as new sensors are provisioned, while keeping this platform cost-efficient? {Select TWO.)

A.
Resize the MySQL General Purpose SSD storage to 6 TB to improve the volume's IOPS
Answers
A.
Resize the MySQL General Purpose SSD storage to 6 TB to improve the volume's IOPS
B.
Re-architect the database tier to use Amazon Aurora instead of an RDS MySQL DB instance and add read replicas
Answers
B.
Re-architect the database tier to use Amazon Aurora instead of an RDS MySQL DB instance and add read replicas
C.
Leverage Amazon Kinesis Data Streams and AWS Lambda to ingest and process the raw data
Answers
C.
Leverage Amazon Kinesis Data Streams and AWS Lambda to ingest and process the raw data
D.
Use AWS X-Ray to analyze and debug application issues and add more API servers to match the load
Answers
D.
Use AWS X-Ray to analyze and debug application issues and add more API servers to match the load
E.
Re-architect the database tier to use Amazon DynamoDB instead of an RDS MySQL DB instance
Answers
E.
Re-architect the database tier to use Amazon DynamoDB instead of an RDS MySQL DB instance
Suggested answer: C, E

Explanation:

Option C is correct because leveraging Amazon Kinesis Data Streams and AWS Lambda to ingest and process the raw data resolves the issues permanently and enable growth as new sensors are provisioned. Amazon Kinesis Data Streams is a serverless streaming data service that simplifies the capture, processing, and storage of data streams at any scale. Kinesis Data Streams can handle any amount of streaming data and process data from hundreds of thousands of sources with very low latency. AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. Lambda can be triggered by Kinesis Data Streams events and process the data records in real time. Lambda can also scale automatically based on the incoming data volume.By using Kinesis Data Streams and Lambda, the company can reduce the load on the API servers and improve the performance and scalability of the data ingestion and processing layer3

Option E is correct because re-architecting the database tier to use Amazon DynamoDB instead of an RDS MySQL DB instance resolves the issues permanently and enable growth as new sensors are provisioned. Amazon DynamoDB is a fully managed key-value and document database that delivers single-digit millisecond performance at any scale. DynamoDB supports auto scaling, which automatically adjusts read and write capacity based on actual traffic patterns. DynamoDB also supports on-demand capacity mode, which instantly accommodates up to double the previous peak traffic on a table. By using DynamoDB instead of RDS MySQL DB instance, the company can eliminate high write latency and improve scalability and performance of the database tier.

asked 16/09/2024
Caridade Martins
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