DAS-C01 · Question #90
A company has an application that ingests streaming data. The company needs to analyze this stream over a 5-minute timeframe to evaluate the stream for anomalies with Random Cut Forest (RCF) and…
The correct answer is B. Ingest the data stream with Amazon Kinesis Data Streams. The correct architecture is: Amazon Kinesis Data Streams (ingestion) → Amazon Kinesis Data Analytics (windowed SQL + built-in RANDOM_CUT_FOREST function for anomaly detection) → results written to Amazon S3 for persistence. Kinesis Data Analytics supports tumbling and sliding…
Question
A company has an application that ingests streaming data. The company needs to analyze this stream over a 5-minute timeframe to evaluate the stream for anomalies with Random Cut Forest (RCF) and summarize the current count of status codes. The source and summarized data should be persisted for future use. Which approach would enable the desired outcome while keeping data persistence costs low?
Options
- AIngest the data stream with Amazon Kinesis Data Streams.
- BIngest the data stream with Amazon Kinesis Data Streams.
- CIngest the data stream with Amazon Kinesis Data Firehose with a delivery frequency of 1 minute
- DIngest the data stream with Amazon Kinesis Data Firehose with a delivery frequency of 5 minutes
How the community answered
(48 responses)- A15% (7)
- B75% (36)
- C4% (2)
- D6% (3)
Explanation
The correct architecture is: Amazon Kinesis Data Streams (ingestion) → Amazon Kinesis Data Analytics (windowed SQL + built-in RANDOM_CUT_FOREST function for anomaly detection) → results written to Amazon S3 for persistence. Kinesis Data Analytics supports tumbling and sliding window queries over 5-minute windows and has RCF as a built-in SQL extension, requiring no custom ML code. Kinesis Data Firehose (options C and D) is a delivery service only - it cannot run analytics, windowing, or RCF. Storing processed results and raw source data in S3 (instead of a database) keeps persistence costs low.
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