Exam AWS Certified Solutions Architect - Associate SAA-C03 topic 1 question 25 discussion

A - refactoring can be a solution, BUT requires a LOT of effort - not the answer B - DynamoDB is NoSQL and Aurora is SQL, so it requires a DB migration... again a LOT of effort, so no the answer C and D are similar in structure, but... C uses SNS, which would notify the 2nd Lambda function... provoking the same bottleneck... not the solution D uses SQS, so the 2nd lambda function can go to the queue when responsive to keep with the DB load process. Usually the app decoupling helps with the performance improvement by distributing load. In this case, the bottleneck is solved by uses queues... so D is the answer.

Keywords: - Company has to increase the Lambda quotas significantly to handle the high volumes of data that the company needs to load into the database. - Improve scalability and minimize the configuration effort. A: Incorrect - Lambda is Serverless and automatically scale - EC2 instance we have to create load balancer, auto scaling group,.. a lot of things. using native Java Database Connectivity (JDBC) drivers don't improve the performance. B: Incorrect - a lot of things to changes and DynamoDB Accelerator use for cache(read) not for write. C: Incorrect - SNS is use for send notification (e-mail, SMS). D: Correct - with SQS we can scale application well by queuing the data.

It could be C or D, but Ans D wins because –SNS is Push Mechanism - ‘Other Lambda’ function is forced to take message when it might not be ready (or refuse it) –SQS is Pull Mechanism – ‘Other Lambda’ function can take next message when its ready to do so SQS is simple and allows better de-coupling.

uses SQS

If the throughput is so high that lambda concurrency needs to go beyond 1000, we need to set up a queue to throttle the request.

D. Set up two Lambda functions, one for receiving information and another for loading data into the database. Integrate them using an Amazon SQS queue. This approach allows for better scalability, maintains the serverless paradigm, and minimizes manual configuration effort. It leverages Amazon SQS as a reliable message queue between Lambda functions. Options A and B introduce complexities and changes in architecture, while Option C introduces an additional service that may not be as suitable for decoupling processes in this scenario.

SQS will help Lambda scale even more. A EC2 + Tomcat will be slower than Lambda for this usecase B is wrong because the problem is with Lambda scaling not the DB C SNS is not the best option for this usecase when SQS is an option

Answer-D

D : other ones just don’t make sense

Sorry, but the question does not make sense by itself. What are you asking for more scalability from an already scalable Lambda function? If you're concerned about the concurrency limits of Lambda function, decoupling just doesn't make sense, since it'll keep even more lambda instances running in a given time period(including 2 phases of execution for each request, let alone the cold start issues). If you're concerned about bottleneck database induced, that'll even be more ridiculous since you're supposed to resolve the scalability issue of the database(e.g. Aurora) instead of decoupling the Lambda function to improve the throughput of this entire data flow.

Lambda and SQS are serverless. No involvement will be required in execution.

I think B would be better solution. How splitting one function into 2 increase scalability when company already increased service quota? Effectively they will have same compute time. Changing Aurora to DAX will shorten the time for data loads by ~100x requiring way less time for data loading, and it's most time consuming thing this lambda does. DAX has better scaling than aurora and is better fit with lambda

Lambda Functions: A review Run your code in response to events You can build chatbots using Lambda functions to process user input, execute business logic, and generate responses. Scales automatically They can be triggered in response to API events Lambda functions can process files as they are uploaded to S3 buckets. This is often used for tasks like image resizing, data extraction, or file validation.

AWS Cost Explorer is a tool that enables you to view and analyze your costs and usage. You can explore your usage and costs using the main graph, the Cost Explorer cost and usage reports, or the Cost Explorer RI reports. You can view data for up to the last 12 months, forecast how much you're likely to spend for the next 12 months, and get recommendations for what Reserved Instances to purchase. Ans: B is correct https://docs.aws.amazon.com/cost-management/latest/userguide/ce-what-is.html

Do you all have to take the whole practice exam on here, in order to pass AWS SAA C03

Increase Lambda quotas = Set up two Lambda functions. Improve scalability = Amazon Simple Queue Service.

Option D is the right answer for this.