Identifying Anomalies in Data Stream: AWS Service and Function
Question
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?
Answers
Explanations
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)
To detect anomalies in the data stream of the wifi-connected radios, the most appropriate AWS service to use is Kinesis Data Analytics. This service allows you to process and analyze real-time streaming data using SQL queries or Apache Flink applications.
The next step is to choose the right function within Kinesis Data Analytics that can identify anomalies in the data stream. There are two options given in the answer choices: Hotspots and Random Cut Forest.
Hotspots is a built-in function within Kinesis Data Analytics that detects outliers in a dataset. It uses statistical analysis to identify the top contributors to a specific metric, allowing you to investigate and diagnose issues. However, it may not be the best option for detecting anomalies in hardware performance data.
On the other hand, Random Cut Forest is a machine learning algorithm that can detect anomalies in data by creating a forest of decision trees. It works by assigning anomaly scores to data points based on how isolated they are from the rest of the data. This function is better suited for detecting anomalies in hardware performance data as it can identify subtle changes in the data that may indicate a problem with the hardware.
Therefore, the correct answer to the question is B. Kinesis Data Analytics and its Random Cut Forest function. This combination allows you to analyze real-time streaming data and identify anomalies in hardware performance data.
