Amazon
MLS-C01 · Question #83
An aircraft engine manufacturing company is measuring 200 performance metrics in a time- series. Engineers want to detect critical manufacturing defects in near-real time during testing. All of the…
The correct answer is D. Use Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random. near real time, auto anomaly detection using RCF, uses Firehose later to store data for offline
Data Engineering
Question
An aircraft engine manufacturing company is measuring 200 performance metrics in a time- series. Engineers want to detect critical manufacturing defects in near-real time during testing. All of the data needs to be stored for offline analysis. What approach would be the MOST effective to perform near-real time defect detection?
Options
- AUse AWS IoT Analytics for ingestion, storage, and further analysis.
- BUse Amazon S3 for ingestion, storage, and further analysis.
- CUse Amazon S3 for ingestion, storage, and further analysis.
- DUse Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random
How the community answered
(32 responses)- A9% (3)
- B3% (1)
- C6% (2)
- D81% (26)
Explanation
near real time, auto anomaly detection using RCF, uses Firehose later to store data for offline
Topics
#Streaming Data#Real-time Analytics#AWS Kinesis#Data Ingestion
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