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

Data provenance tracks where IoT data originated, how it was transformed, and who accessed it, which is essential for compliance, debugging, and trust.

Question 2

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

Accepted Answer

Prescriptive analytics

Answer

Prescriptive analytics goes beyond prediction to recommend specific actions (e.g., schedule maintenance now) to optimize IoT system outcomes.

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

Sensor fusion combines readings from multiple IoT sensors (e.g., accelerometer + gyroscope) to derive more accurate measurements or richer context.

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

Statistical process control with control charts tracks IoT sensor readings against control limits to detect gradual drift or process degradation over time.

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

In Lambda architecture, the cold path (batch layer) processes large volumes of historical IoT data offline for deep analytics and training machine learning models.

IOT - Internet of Things Practice Test

IOT - Internet of Things Practice Test

IOT IoT Data Analytics 3