You work for a manufacturer of wifi-connected radios.
Your company wants to use data captured when these radios are in use by their customers (such as how the hardware is performing, the applications that are running on the radio, and the content that's being streamed) to serve their customers better.
You and your team of machine learning specialists have been asked to use the data captured when users play their radios to build a model that detects anomalies with the hardware performance. What AWS service and function within that service will allow you to identify anomalies in the data stream?
Click on the arrows to vote for the correct answer
A. B. C. D. E. F.Answer: B.
Option A is incorrect.
The Kinesis Data Analytics Hotspot function is used to get information about dense regions in your data, not to identify outlier data, or anomalies, in your streaming data.
Option B is correct.
The Kinesis Data Analytics Random_Cut_Forest function is used to identify outlier data, or anomalies, in your streaming data.
Option C is incorrect.
Kinesis Data Firehose does not have functions like Hotspots or Random_Cut_Forest.
Option D is incorrect.
Kinesis Data Streams does not have functions like Hotspots or Random_Cut_Forest.
Option E is incorrect.
Kinesis Data Streams does not have functions like Hotspots or Random_Cut_Forest.
Option F is incorrect.
Kinesis Data Firehose does not have functions like Hotspots or Random_Cut_Forest.
Reference:
Please see the Amazon Kinesis Data Analytics for SQL Applications Developer Guide titled Examples: Machine Learning, the Amazon Kinesis Data Analytics for SQL Applications Developer Guide titled Example: Detecting Data Anomalies on a Stream (RANDOM_CUT_FOREST Function), and the Amazon Kinesis Data Analytics for SQL Applications Developer Guide titled Example: Detecting Hotspots on a Stream (HOTSPOTS Function)
The correct answer for this question is B. Kinesis Data Analytics and its Random Cut Forest function.
Explanation:
The problem statement mentions that the company wants to use data captured from wifi-connected radios to build a model that detects anomalies with the hardware performance. This indicates that the data is being generated in real-time and needs to be analyzed in real-time as well.
To process streaming data, AWS offers multiple services like Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics. Among these services, Kinesis Data Analytics is designed to perform real-time analytics on streaming data. Kinesis Data Analytics allows you to analyze and process data using standard SQL queries and offers various in-built functions to perform machine learning operations on the streaming data.
The Hotspots function, which is mentioned in options A and E, is not designed to identify anomalies in the data stream. Instead, it helps to identify frequently occurring patterns in the data.
The Random Cut Forest function, which is mentioned in options B and F, is a machine learning algorithm that is used for anomaly detection. The algorithm uses an ensemble of decision trees to detect anomalies in the data stream. The algorithm creates a forest of decision trees, and the score of each data point is calculated as the average depth of the tree required to isolate it. The data points with higher scores are considered anomalies.
Therefore, the correct answer is B. Kinesis Data Analytics and its Random Cut Forest function. This combination of services and function allows for real-time analytics and detection of anomalies in streaming data, which aligns with the problem statement requirements.