IOT IoT Data Analytics 3

Question 1

What is 'data provenance' important for in IoT analytics pipelines?

Accepted Answer

Tracking the origin, transformation history, and custody chain of IoT data for auditability

Answer

Increasing data processing speed

Answer

Reducing data storage costs

Answer

Improving sensor battery life

Question 2

Which type of IoT analytics recommends actions to optimize outcomes based on predictive model outputs?

Accepted Answer

Prescriptive analytics

Answer

Descriptive analytics

Answer

Diagnostic analytics

Answer

Predictive analytics

Question 3

What is 'sensor fusion' in IoT data analytics?

Accepted Answer

Merging data from multiple sensors to produce more accurate or comprehensive information than any single sensor provides

Answer

Combining power sources for IoT sensors

Answer

Fusing sensor hardware components together

Answer

Combining multiple wireless protocols

Question 4

Which statistical method is commonly used to detect gradual drift or degradation in IoT sensor performance over time?

Accepted Answer

Statistical process control (SPC) with control charts

Answer

K-nearest neighbors

Answer

Random forest classification

Answer

Convolutional neural networks

Question 5

What is the 'cold path' in a Lambda architecture used for IoT data processing?

Accepted Answer

Batch processing of historical IoT data for deep analysis and model training

Answer

Processing data from cold-weather IoT sensors

Answer

An emergency data pathway when primary systems fail

Answer

Processing data with low priority

Question 6

What challenge does 'concept drift' present in IoT machine learning models?

Accepted Answer

The statistical properties of IoT data changing over time, causing models trained on old data to become less accurate

Answer

IoT devices drifting out of their geographic zone

Answer

IP address conflicts in IoT networks

Answer

Clock synchronization issues between devices