DAS-C01 · Question #60
DAS-C01 Question #60: Real Exam Question with Answer & Explanation
The correct answer is B: Send the data to Amazon Managed Streaming for Kafka and configure an Amazon Kinesis. {"question_number": 3, "correct_answer": "B", "explanation": "Two critical constraints drive this answer: the 30-second SLA and evolving JSON schemas. Amazon Kinesis Data Firehose (options A and C) has a minimum buffering interval of 60 seconds before delivering data to S3, which
Question
A media content company has a streaming playback application. The company wants to collect and analyze the data to provide near-real-time feedback on playback issues. The company needs to consume this data and return results within 30 seconds according to the service-level agreement (SLA). The company needs the consumer to identify playback issues, such as quality during a specified timeframe. The data will be emitted as JSON and may change schemas over time. Which solution will allow the company to collect data for processing while meeting these requirements?
Options
- ASend the data to Amazon Kinesis Data Firehose with delivery to Amazon S3.
- BSend the data to Amazon Managed Streaming for Kafka and configure an Amazon Kinesis
- CSend the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure Amazon
- DSend the data to Amazon Kinesis Data Streams and configure an Amazon Kinesis Analytics for
Explanation
{"question_number": 3, "correct_answer": "B", "explanation": "Two critical constraints drive this answer: the 30-second SLA and evolving JSON schemas. Amazon Kinesis Data Firehose (options A and C) has a minimum buffering interval of 60 seconds before delivering data to S3, which immediately violates the 30-second SLA - eliminating A and C. Between options B and D, both Kinesis Data Streams and Amazon MSK (Managed Streaming for Apache Kafka) can deliver data in real-time within 30 seconds. However, the schema evolution requirement is the differentiator: Amazon MSK integrates natively with AWS Glue Schema Registry, which is specifically designed to manage and enforce evolving schemas for streaming data (including JSON). This allows consumers to handle schema changes gracefully without breaking the pipeline. Option D (Kinesis Data Streams + Kinesis Data Analytics) lacks native schema registry support, making it harder to handle JSON schema evolution over time. Option B's MSK-based architecture provides low-latency ingestion, schema governance, and the ability to wire the consumer output to SNS or another alerting mechanism within the 30-second SLA.", "generated_by": "claude-sonnet", "llm_judge_score": 4}
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