DAS-C01 · Question #185
A social media company is using business intelligence tools to analyze its data for forecasting. The company is using Apache Kafka to ingest the low-velocity data in near-real time. The company…
The correct answer is B. Replace Kafka with an Amazon Kinesis data stream. Use an Amazon Kinesis Data Firehose delivery. The key requirements are: fix Kafka scalability issues, ingest data to S3 with hourly updates, and support QuickSight with ML insights. Option B replaces Kafka entirely with Amazon Kinesis Data Streams, which is fully managed and elastic - directly solving the scalability…
Question
A social media company is using business intelligence tools to analyze its data for forecasting. The company is using Apache Kafka to ingest the low-velocity data in near-real time. The company wants to build dynamic dashboards with machine learning (ML) insights to forecast key business trends. The dashboards must provide hourly updates from data in Amazon S3. Various teams at the company want to view the dashboards by using Amazon QuickSight with ML insights. The solution also must correct the scalability problems that the company experiences when it uses its current architecture to ingest data. Which solution will MOST cost-effectively meet these requirements?
Options
- AReplace Kafka with Amazon Managed Streaming for Apache Kafka. Ingest the data by using AWS
- BReplace Kafka with an Amazon Kinesis data stream. Use an Amazon Kinesis Data Firehose delivery
- CConfigure the Kafka-Kinesis-Connector to publish the data to an Amazon Kinesis Data Firehose delivery
- DConfigure the Kafka-Kinesis-Connector to publish the data to an Amazon Kinesis Data Firehose delivery
How the community answered
(27 responses)- A11% (3)
- B63% (17)
- C22% (6)
- D4% (1)
Explanation
The key requirements are: fix Kafka scalability issues, ingest data to S3 with hourly updates, and support QuickSight with ML insights. Option B replaces Kafka entirely with Amazon Kinesis Data Streams, which is fully managed and elastic - directly solving the scalability problem. Kinesis Data Firehose then buffers and delivers the data to S3, supporting the hourly update cadence. QuickSight can natively connect to S3 (via Athena) and has built-in ML Insights for forecasting. Option A keeps Kafka (just managed via MSK), which doesn't fully resolve scalability or reduce operational overhead as cost-effectively. Options C and D retain Kafka and add a Kafka-Kinesis-Connector, preserving the existing ingestion architecture and its problems rather than replacing it.
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