Question: Which challenge is Cloud IoT designed to address?
- Organizations find it difficult to stay ahead when they continuously have to accommodate new data sources and more data without sacrificing efficiency.
- Accepting that most devices can theoretically be connected to a network, building and managing such networks in a global, secure way—and then getting data out of them for analysis—is complex and difficult for organizations.
- Multiple data marts are inefficient: they are complex and costly, and they make data difficult to use.
- Organizations that want to take advantage of machine learning need to centralize their data with a managed data store that can consolidate structured and semi-structured data.
Explanation
Cloud IoT addresses the difficulty of securely connecting, managing, and extracting data from distributed connected devices. IoT environments create operational complexity because devices can be global, numerous, and continuously generating telemetry. Google Cloud IoT architectures use services such as Pub/Sub, Dataflow, and BigQuery to ingest, process, and analyze device data. This helps organizations move from connected device data to usable insight without building every ingestion and analytics component from scratch.
Why the other options are incorrect
New data sources describes general data growth, not the specific device connectivity challenge addressed by Cloud IoT.
Multiple data marts describes data warehouse fragmentation, not connected device management.
Centralize data describes analytics and machine learning preparation, not the main challenge of managing IoT device networks.
Source for verification
https://docs.cloud.google.com/architecture/connected-devices
https://cloud.google.com/blog/topics/developers-practitioners/what-cloud-iot-core
The answer(s) to the question is highlighted in the BOLD text above. You can also find more questions and answers related to the exams on the "Google Cloud Platform Business Professional" page.