AWS Certified Solutions Architect – Professional — Question 318

A utility company wants to collect usage data every 5 minutes from its smart meters to facilitate time-of-use metering. When a meter sends data to AWS, the data is sent to Amazon API Gateway, processed by an AWS Lambda function and stored in an Amazon DynamoDB table. During the pilot phase, the Lambda functions took from 3 to 5 seconds to complete.
As more smart meters are deployed, the Engineers notice the Lambda functions are taking from 1 to 2 minutes to complete. The functions are also increasing in duration as new types of metrics are collected from the devices. There are many ProvisionedThroughputExceededException errors while performing PUT operations on DynamoDB, and there are also many TooManyRequestsException errors from Lambda.
Which combination of changes will resolve these issues? (Choose two.)

Answer options

• A. Increase the write capacity units to the DynamoDB table.
• B. Increase the memory available to the Lambda functions.
• C. Increase the payload size from the smart meters to send more data.
• D. Stream the data into an Amazon Kinesis data stream from API Gateway and process the data in batches.
• E. Collect data in an Amazon SQS FIFO queue, which triggers a Lambda function to process each message.

Correct answer: A, B

Explanation

Increasing the write capacity units directly resolves the ProvisionedThroughputExceededException errors by allowing the DynamoDB table to handle the increased volume of concurrent writes. Additionally, increasing the memory allocated to the Lambda functions proportionally increases CPU power, which reduces execution duration and helps prevent TooManyRequestsException errors caused by long-running functions exhausting concurrency limits. While streaming or queueing data are valid architectural patterns, scaling the database throughput and Lambda compute capacity directly addresses the specific performance bottlenecks identified in the current architecture.