Q51 — AWS SAA-C03 Ch.14
Question 51 of 100 | ← Chapter 14
Q1051. 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?
- A. 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. ✓
- B. 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.
- C. 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.
- D. 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.
Correct Answer: A. 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.
Explanation
To automate the customer survey process and ensure that survey results are available for the previous 12 months in a scalable way, the best solution is:A. 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.Explanation:\1. Scalability: - Using API Gateway and SQS allows the system to handle a high volume of incoming survey results efficiently. SQS can buffer the survey results, ensuring that the system can scale up or down based on demand.\2. AWS Lambda: - AWS Lambda can process messages from the SQS queue, allowing for serverless execution that automatically scales with the volume of survey results. This minimizes the need for managing server infrastructure.\3. Sentiment Analysis: - Amazon Comprehend is tailored for natural language processing and sentiment analysis, making it an appropriate choice for analyzing customer feedback.\4. Data Storage: - Storing the results in DynamoDB provides a scalable NoSQL database solution that can handle high read/write throughput. Setting a Time to Live (TTL) of 365 days ensures that data is automatically expired after this period, managing storage costs effectively.Evaluation of Other Options:B. Send the survey results data to an API that is running on an Amazon EC2 instance: - While this could work, it introduces more management overhead (managing EC2 instances) and does not leverage serverless architecture, which is more scalable.C. Write the survey results data to an Amazon S3 bucket: - Although S3 is a good storage option, using S3 Event Notifications with Lambda to read the data and call Amazon Rekognition (which is primarily for image analysis) is not suitable for sentiment analysis. Additionally, managing sentiment analysis with S3 does not align well with the requirement for structured data analysis.D. Send the survey results data to an Amazon API Gateway endpoint connected to Amazon SQS: - This option correctly utilizes SQS, but it mistakenly suggests using Amazon Lex for sentiment analysis. Lex is designed for conversational interfaces and not for sentiment analysis, making it less appropriate than Amazon Comprehend.Conclusion:Option A provides the most scalable and efficient solution for automating the customer survey process, ensuring sentiment analysis, and managing data retention requirements effectively.