IOT IoT Data Analytics 2 — Questions and Answers
Question 1: What is 'edge analytics' in an IoT deployment?
- Analytics performed only in the cloud
- Processing and analyzing IoT data at or near the device rather than sending it all to the cloud (Correct answer)
- Analytics at the network's geographic boundary
- Analysis of network edge security threats
Correct answer: Processing and analyzing IoT data at or near the device rather than sending it all to the cloud
Edge analytics processes IoT data locally on edge devices or gateways, reducing latency and bandwidth by analyzing data before it reaches the cloud.
Question 2: Which concept describes the practice of processing only significant IoT data changes rather than transmitting every reading?
- Data replication
- Data deduplication
- Delta transmission or exception reporting (Correct answer)
- Data normalization
Correct answer: Delta transmission or exception reporting
Delta transmission or exception reporting only sends IoT data when values change beyond a threshold, reducing bandwidth and storage requirements.
Question 3: What is the purpose of a 'digital twin' in IoT data analytics?
- A backup copy of IoT device firmware
- A virtual model of a physical IoT device or system, updated with real-time sensor data for simulation and analysis (Correct answer)
- A duplicate device for redundancy
- A secondary cloud region for data backup
Correct answer: A virtual model of a physical IoT device or system, updated with real-time sensor data for simulation and analysis
A digital twin is a virtual representation of a physical IoT system continuously updated with real-time data, enabling simulation, monitoring, and predictive analysis.
Question 4: Which streaming analytics platform is commonly used to process high-volume, real-time IoT data streams?
- MySQL
- Apache Kafka with Kafka Streams or Apache Flink (Correct answer)
- SQLite
- MongoDB Atlas
Correct answer: Apache Kafka with Kafka Streams or Apache Flink
Apache Kafka with Kafka Streams or Apache Flink are popular platforms for ingesting and processing high-throughput real-time IoT data streams.
Question 5: What does 'data normalization' mean when preparing IoT sensor data for analytics?
- Deleting duplicate sensor readings
- Scaling data values to a standard range to remove bias due to different measurement units (Correct answer)
- Encrypting data for secure storage
- Converting data to JSON format
Correct answer: Scaling data values to a standard range to remove bias due to different measurement units
Data normalization scales IoT sensor readings to a common range so that variables with different units don't disproportionately influence analytics models.
Question 6: In IoT analytics, what does 'data labeling' refer to in the context of supervised machine learning?
- Adding metadata tags to IoT device configurations
- Annotating historical IoT sensor data with known outcomes to train predictive models (Correct answer)
- Labeling physical IoT devices with asset tags
- Naming data streams in a database
Correct answer: Annotating historical IoT sensor data with known outcomes to train predictive models
Data labeling annotates historical IoT sensor data with known outcomes (e.g., 'normal' or 'failure') so supervised learning models can learn to recognize those conditions.
IOT IoT Data Analytics 2
What is 'edge analytics' in an IoT deployment?