Amazon MLS-C01 Exam - Topic 3 Question 128 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 128
Topic #: 3

[Modeling]

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?

AUse AWS IoT Analytics for ingestion, storage, and further analysis. Use Jupyter notebooks from withinAWS IoT Analytics to carry out analysis for anomalies.

BUse Amazon S3 for ingestion, storage, and further analysis. Use an Amazon EMR cluster to carry outApache Spark ML k-means clustering to determine anomalies.

CUse Amazon S3 for ingestion, storage, and further analysis. Use the Amazon SageMaker Random CutForest (RCF) algorithm to determine anomalies.

DUse Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest(RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for furtheranalysis.

Show Suggested Answer

Suggested Answer: D

by Tyisha at Apr 04, 2026, 11:47 PM

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1 month ago

I remember we discussed the importance of real-time data processing in our last class. I think Kinesis might be the best option for that.

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