Most Scalable Automated Survey Sentiment Analysis on AWS?

SAA-C03 By islever Editorial Last updated

Design scalable and loosely coupled architectures. Determine high-performing data ingestion and transformation solutions.

Short answer

A Use API Gateway and SQS with a Lambda function that calls Amazon Comprehend and stores results in DynamoDB with a 365-day TTL.

Question

A company tracks customer satisfaction by using surveys that the company hosts on its website. The surveys sometimes reach thousands of customers every hour. Survey results are currently sent in email messages to the company so company employees can manually review results and assess customer sentiment. The company wants to automate the customer survey process. Survey results must be available for the previous 12 months. Which solution will meet these requirements in the MOST scalable way?
  1. Send the survey results data to an Amazon API Gateway endpoint that is connected to an Amazon Simple Queue Service (Amazon SQS) queue. Create an AWS Lambda function to poll the SQS queue, call Amazon Comprehend for sentiment analysis, and save the results to an Amazon DynamoDB table. Set the TTL for all records to 365 days in the future. Correct Answer
  2. Send the survey results data to an API that is running on an Amazon EC2 instance. Configure the API to store the survey results as a new record in an Amazon DynamoDB table, call Amazon Comprehend for sentiment analysis, and save the results in a second DynamoDB table. Set the TTL for all records to 365 days in the future.
  3. Write the survey results data to an Amazon S3 bucket. Use S3 Event Notifications to invoke an AWS Lambda function to read the data and call Amazon Rekognition for sentiment analysis. Store the sentiment analysis results in a second S3 bucket. Use S3 lifecycle policies on each bucket to expire objects after 365 days.
  4. Send the survey results data to an Amazon API Gateway endpoint that is connected to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the SQS queue to invoke an AWS Lambda function that calls Amazon Lex for sentiment analysis and saves the results to an Amazon DynamoDB table. Set the TTL for all records to 365 days in the future.

Key Takeaways

The question tests matching Amazon Comprehend for text sentiment and a loosely coupled serverless design that absorbs thousands of surveys per hour. The trap is choosing EC2, Rekognition, or Lex, which are not the right scalability or NLP fit.

This SAA-C03 question tests a scalable, serverless pipeline for high-volume survey sentiment analysis with 12-month retention. The page establishes that option A is correct because it combines API Gateway, SQS, Lambda, Amazon Comprehend, and DynamoDB TTL, while the other options rely on EC2 or the wrong AI services.

Common mistake: Many learners pick option D because it looks serverless, but Amazon Lex is a chatbot service, not a sentiment analysis engine, and the SQS-to-Lambda wording hides an invalid invocation pattern. Option A remains the only design that uses the correct NLP service and fully managed scaling.

Expert Analysis

Why the Answer Is Correct

Option A is correct because it uses Amazon Comprehend, which is AWS's natural language processing service for sentiment analysis on text, not images or chatbot conversations. API Gateway and Amazon SQS decouple survey ingestion from processing, so traffic spikes to thousands of surveys per hour do not overwhelm a single server. AWS Lambda then polls SQS, scales automatically with queue depth, calls Comprehend, and writes results to a DynamoDB table. DynamoDB TTL set to 365 days in the future satisfies the requirement to keep survey results available for the previous 12 months while automatically removing older records.

Why the Other Options Are Wrong

Option B places the API on an Amazon EC2 instance, which requires capacity planning and does not scale as seamlessly as API Gateway with SQS and Lambda. Option C uses Amazon Rekognition for sentiment analysis, but Rekognition analyzes images and video, not customer survey text, so the analysis step cannot work as described. Option D names Amazon Lex for sentiment analysis, but Lex is a conversational chatbot service, and the option also describes SQS directly invoking Lambda rather than Lambda polling the queue through an event source mapping. Option A is the only choice that pairs the correct Comprehend NLP service with a fully managed, elastic ingestion and storage design.

Community Comment Notes

Changwha summarized option A as the most scalable and cost-effective design and specifically called out Comprehend for sentiment analysis plus DynamoDB's TTL feature for 12-month retention. EllenLiu linked directly to the Amazon Comprehend documentation, reinforcing that Comprehend is the intended service for text sentiment. trinh_le keyed on the phrase about reaching thousands of customers every hour and noted that SQS is used to automate handling the survey with Amazon Comprehend. siheom simply voted A, and the full community vote distribution is unanimous for option A.

Official Reference

These links open the official vendor documentation in a new tab.

Exam Strategy

Eliminate options by service fit first: sentiment analysis on text points straight to Amazon Comprehend, not Rekognition or Lex. Then check scalability: managed serverless ingestion with API Gateway, SQS, and Lambda beats an EC2-hosted API for thousands of events per hour. Retention via DynamoDB TTL is the clean 12-month answer.

Frequently Asked Questions

Why is Amazon Lex wrong for survey sentiment analysis in option D?

Amazon Lex builds conversational voice and chat bots; it does not detect sentiment. Amazon Comprehend is the NLP service designed to analyze text sentiment, so option D fails on service fit.

Why does DynamoDB TTL satisfy the 12-month retention requirement?

DynamoDB TTL automatically deletes items after the specified timestamp, so setting it 365 days ahead keeps each survey result available for the previous 12 months without custom cleanup code.

More SAA-C03 FAQ →

Community Votes

A
100%

100% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 4 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 4

  1. Changwha 👍 2
    A. This solution is the most scalable, cost-effective, and tailored to the requirements. It uses Amazon Comprehend for sentiment analysis, leverages serverless architecture, and retains data for 12 months using DynamoDB's TTL feature.
  2. trinh_le 👍 1 Answered A
    Following keywords reach thousands of customers every hour: use SQS Automate handle survey: aws comprehen
  3. EllenLiu 👍 1 Answered A
    https://docs.aws.amazon.com/comprehend/latest/dg/what-is.html
  4. siheom 👍 2 Answered A
    VOTE A

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