Exam AWS Certified Solutions Architect - Associate SAA-C03 topic 1 question 161 discussion

solution should remove operation overhead -> s3 -> lambda -> aurora

By placing the JSON documents in an S3 bucket, the documents will be stored in a highly durable and scalable object storage service. The use of AWS Lambda allows the company to run their Python code to process the documents as they arrive in the S3 bucket without having to worry about the underlying infrastructure. This also allows for horizontal scalability, as AWS Lambda will automatically scale the number of instances of the function based on the incoming rate of requests. The results can be stored in an Amazon Aurora DB cluster, which is a fully-managed, high-performance database service that is compatible with MySQL and PostgreSQL. This will provide the necessary durability and scalability for the results of the processing.

Using Lambda, S3 and Aurora is the most suitable and provides least operational overhead for this problem.

Ans D - because none of the other answers guarantee FIFO. What if a subsequent JSON operation is intended to update an existing database record (which is highly likely) - then the wrong change would be applied

b is the right answer it very obvious

Guys I have a question. We dont know how long the processing of JSON documents is going to take. What if that processing takes more than 15 min ? Lambda can run only for 15 correct ? Based on this the answer could be D Please correct my understanding.

"D" has a lot of moving parts and more operational overhead even if each part is a managed service in itself. Also, if something can be done with Lambda, don't use an EC2 instance in any form as it always increases operational overhead (compared to Lambda).

"D" is just like the most complex one, sometimes the admin make mistakes and don't realize. lambda is a service make for this

B is correc

Main requirement is: 'scalability and minimized operational overhead' = serverless = Amazon S3 bucket, AWS Lambda function, Amazon Aurora DB cluster

- Using Lambda functions triggered by S3 events allows the Python code to automatically scale up and down based on the number of incoming JSON documents. This provides high availability and maximizes scalability. - Storing the results in an Amazon Aurora DB cluster provides a managed, scalable, and highly available database. - This serverless approach minimizes operational overhead since Lambda and Aurora handle provisioning infrastructure, deploying code, monitoring, patching, etc.

The answer is B. Place the JSON documents in an Amazon S3 bucket. Create an AWS Lambda function that runs the Python code to process the documents as they arrive in the S3 bucket. Store the results in an Amazon Aurora DB cluster. This solution is highly available because Lambda functions are automatically scaled up or down based on the number of requests they receive. It is also scalable because you can easily add more Lambda functions to process more documents. Finally, it minimizes operational overhead because you do not need to manage any EC2 instances.

Using Lambda eliminates the need to manage and provision servers, ensuring scalability and minimizing operational overhead. S3 provides durable and highly available storage for the JSON documents. Lambda can be triggered automatically whenever new documents are added to the S3 bucket, allowing for real-time processing. Storing the results in an Aurora DB cluster ensures high availability and scalability for the processed data. This solution leverages serverless architecture, allowing for automatic scaling and high availability without the need for managing infrastructure, making it the most suitable choice. A. This option requires manual management and scaling of EC2 instances, resulting in higher operational overhead and complexity. C. This approach still involves manual management and scaling of EC2 instances, increasing operational complexity and overhead. D. This solution requires managing and scaling an ECS cluster, adding operational overhead and complexity. Utilizing SQS adds complexity to the system, requiring custom handling of message consumption and processing in the Python code.

Keywords here are : "maximizes scalability and minimizes operational overhead, hence option B is correct answer.

i vote for D as 'on-premises SQL database' is not mysql/postgre which can replace by aurora

B is the best option. https://aws.amazon.com/rds/aurora/

agree...B is the best option S3, Lambda , Aurora.