MLS-C01 · Question #118
A data scientist is developing a pipeline to ingest streaming web traffic data. The data scientist needs to implement a process to identify unusual web traffic patterns as part of the pipeline. The…
The correct answer is D. Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an. The algorithm starts developing the machine learning model using current records in the stream when you start the application. The algorithm does not use older records in the stream for machine learning, nor does it use statistics from previous executions of the application…
Question
A data scientist is developing a pipeline to ingest streaming web traffic data. The data scientist needs to implement a process to identify unusual web traffic patterns as part of the pipeline. The patterns will be used downstream for alerting and incident response. The data scientist has access to unlabeled historic data to use, if needed. The solution needs to do the following:
- Calculate an anomaly score for each web traffic entry.
- Adapt unusual event identification to changing web patterns over time.
Which approach should the data scientist implement to meet these requirements?
Options
- AUse historic web traffic data to train an anomaly detection model using the Amazon SageMaker
- BUse historic web traffic data to train an anomaly detection model using the Amazon SageMaker
- CCollect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an
- DCollect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an
How the community answered
(33 responses)- A9% (3)
- B6% (2)
- C15% (5)
- D70% (23)
Explanation
The algorithm starts developing the machine learning model using current records in the stream when you start the application. The algorithm does not use older records in the stream for machine learning, nor does it use statistics from previous executions of the application. https://docs.aws.amazon.com/kinesisanalytics/latest/sqlref/sqlrf-random-cut-forest.html
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