SAA-C03 Question #526: Real Exam Question with Answer & Explanation

Question

A company needs a solution to ingest streaming sensor data from 100,000 devices, transform the data in near real time, and load the data into Amazon S3 for analysis. The solution must be fully managed, scalable, and maintain sub-second ingestion latency. Which solution will meet this requirement?

Options

• AUse Amazon Kinesis Data Streams to ingest the data. Use Amazon Managed Service for Apache
• BUse Amazon Simple Queue Service (Amazon SQS) standard queues to collect the sensor data.
• CDeploy a fleet of Amazon EC2 instances that run Apache Kafka to ingest the data. Run Apache
• DImplement Amazon EventBridge to capture all sensor data. Use AWS Batch to run containerized

Explanation

The most scalable and managed solution for streaming ingestion, real-time transformation, and delivery to Amazon S3 is Amazon Kinesis Data Streams, Amazon Managed Service for Apache Flink, and Amazon Kinesis Data Firehose. "Amazon Kinesis Data Streams enables real-time processing of streaming data at massive scale. With Apache Flink on Kinesis Data Analytics, you can process data streams in near real-time, then use Amazon Kinesis Data Firehose to reliably deliver that data to S3." Why A is correct: Fully managed: All services involved are serverless and managed. Real-time ingestion: Kinesis Data Streams supports sub-second latency and can handle high- throughput workloads like 100,000+ devices. Near real-time processing: Apache Flink is designed for continuous stream processing with complex event handling. Efficient delivery: Kinesis Firehose delivers processed data directly to S3 with retry and backup Why other options are incorrect: Option B: SQS is not optimized for real-time streaming at high volume. Option C: EC2 + Kafka + EMR adds high operational overhead and cost. Option D: EventBridge is event-driven, not designed for high-throughput streaming; AWS Batch is unsuitable for near real-time processing.

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