Answer: Send activity data to an Amazon Kinesis Data Firehose delivery stream. Configure the stream to deliver the data to an Amazon Redshift cluster.

## Explanation **Correct Answer: B** - Send activity data to an Amazon Kinesis Data Firehose delivery stream. Configure the stream to deliver the data to an Amazon Redshift cluster. **Why this is correct:** 1. **Petabyte-scale data handling**: Amazon Redshift is designed for petabyte-scale data warehousing and analytics, making it suitable for the growing data store requirement. 2. **SQL-based analytics**: Amazon Redshift provides SQL-based querying capabilities, which directly addresses the requirement for "on-demand analytics of existing data and new data with SQL." 3. **Least operational overhead**: Kinesis Data Firehose is a fully managed service that automatically scales to handle data throughput and delivers data to destinations like Amazon Redshift, S3, or Elasticsearch with minimal operational overhead. 4. **High availability**: Both Kinesis Data Firehose and Amazon Redshift are managed services that provide built-in high availability and durability. 5. **Real-time data ingestion**: Kinesis Data Firehose can ingest streaming data in real-time and batch it for delivery to Redshift, supporting both existing and new data analytics. **Analysis of other options:** **A. Send activity data to an Amazon Kinesis data stream. Configure the stream to deliver the data to an Amazon S3 bucket.** - While Kinesis Data Streams can handle streaming data, storing data in S3 alone doesn't provide SQL-based analytics capabilities without additional services like Amazon Athena or Redshift Spectrum. - Requires more operational overhead to set up analytics on S3 data. **C. Place activity data in an Amazon S3 bucket. Configure Amazon S3 to run an AWS Lambda function on the data as the data arrives in the S3 bucket.** - S3 is good for storage but doesn't provide SQL analytics natively. - Lambda functions would need to process and transform data, adding complexity and operational overhead. - Doesn't directly support SQL-based analytics on petabytes of data. **D. Create an ingestion service on Amazon EC2 instances that are spread across multiple Availability Zones. Configure the service to forward data to an Amazon RDS Multi-AZ database.** - RDS is not designed for petabyte-scale analytics; it's optimized for transactional workloads. - Managing EC2 instances for ingestion adds significant operational overhead. - RDS doesn't scale well to petabytes and is expensive for analytics workloads. **Key AWS Services Understanding:** - **Amazon Kinesis Data Firehose**: Fully managed service for loading streaming data into data stores and analytics tools. - **Amazon Redshift**: Petabyte-scale data warehouse service with SQL-based analytics. - **Amazon S3**: Object storage service, good for data lakes but requires additional services for SQL analytics. - **Amazon RDS**: Relational database service for transactional workloads, not analytics at petabyte scale.