Q68 — AWS SAA-C03 Ch.13

Question 68 of 100 | ← Chapter 13

Q968. A social media company is expanding its Amazon DynamoDB backed infrastructure to accommodate increasing user activity.The company manages three data types:user profiles,posts,and comments.The company wants to ensure high performance for write-heavy queries.The new solution must ensure high availability.The new solution needs to provide low latency between the database layer and the application layer.The new solution must be able to handle dynamic traffic patterns for global users.The company must also track changes to items in the DynamoDB tables.Which solution will meet these requirements with the LEAST operational overhead?

• A. Create a single DynamoDB table to store all the data types. Use global secondary indexes(GSls) for queries. Use DynamoDB auto scaling to ensure adaptive capacity. Use Amazon Kinesis Data Streams to provide low latency and to track changes.
• B. Use a separate DynamoDB table for each data type. Use on-demand capacity mode for all tables. Use DynamoDB Accelerator (DAX) to provide low latency. Use Amazon Kinesis Data Streams to track changes.
• C. Use DynamoDB global tables to distribute data across AWS Regions and to provide low latency. Use a separate DynamoDB table for each data type. Use Amazon DynamoDB Streams to track changes. ✓
• D. Create a single DynamoDB table to store all data types. Use provisioned capacity mode to provide low latency. Use composite sort keys for queries. Use Amazon Data Firehose to track changes.

Correct Answer: C. Use DynamoDB global tables to distribute data across AWS Regions and to provide low latency. Use a separate DynamoDB table for each data type. Use Amazon DynamoDB Streams to track changes.

Explanation

The correct answer is C. Use DynamoDB global tables to distribute data across AWS Regions and to provide low latency. Use a separate DynamoDB table for each data type. Use Amazon DynamoDB Streams to track changes.Here's why:A. Create a single DynamoDB table to store all the data types. Use global secondary indexes (GSIs) for queries. Use DynamoDB auto scaling to ensure adaptive capacity. Use Amazon Kinesis Data Streams to provide low latency and to track changes.- Using a single table with GSIs may not provide the best performance for write-heavy workloads, as GSIs can add overhead.- Kinesis Data Streams is not necessary for low latency, as DynamoDB Accelerator (DAX) or DynamoDB global tables can provide this.B. Use a separate DynamoDB table for each data type. Use on-demand capacity mode for all tables. Use DynamoDB Accelerator (DAX) to provide low latency. Use Amazon Kinesis Data Streams to track changes.- On-demand capacity mode may not be the most cost-effective solution for a write-heavy workload.- Kinesis Data Streams is not necessary for tracking changes, as DynamoDB Streams can provide this functionality.C. Use DynamoDB global tables to distribute data across AWS Regions and to provide low latency. Use a separate DynamoDB table for each data type. Use Amazon DynamoDB Streams to track changes.- DynamoDB global tables are designed to provide high availability and low latency for globally distributed applications.- Using separate tables for each data type aligns with the requirement to ensure high performance for write- heavy queries.- DynamoDB Streams is the recommended solution for tracking changes to items in the DynamoDB tables.D. Create a single DynamoDB table to store all data types. Use provisioned capacity mode to provide low latency. Use composite sort keys for queries. Use Amazon Data Firehose to track changes.- Using a single table may not provide the best performance for write-heavy workloads.- Amazon Kinesis Data Firehose is not the recommended solution for tracking changes, as DynamoDB Streams is the intended service for this purpose.Overall, the solution in option C, using DynamoDB global tables, separate tables for each data type, and DynamoDB Streams, provides the best balance of performance, availability, low latency, and operational simplicity to meet the given requirements.