Cloud Digital Leader Why cloud technology is transforming business Practice Question
A manufacturing company wants to use sensor data from equipment to predict failures before they happen, reducing downtime. How does cloud technology enable this transformation?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
By offering IoT Core and AI Platform to collect data and build predictive models
Option D is correct because cloud providers offer managed IoT services such as AWS IoT Core or Azure IoT Hub to ingest sensor telemetry at scale, paired with AI/ML platforms (e.g., Amazon SageMaker, Azure Machine Learning) to train predictive maintenance models that forecast failures before they occur. This combination directly enables the transformation from reactive to predictive maintenance, reducing unplanned downtime. Option A is wrong because storing all data locally with edge computing limits centralized analytics and model training, and edge computing is typically used for low-latency filtering, not bulk storage. Option B is insufficient because monitoring alerts are reactive threshold notifications, not predictive modeling. Option C is wrong because batch analysis in a data warehouse does not provide the real-time streaming ingestion and ML model deployment needed for failure prediction.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
By using edge computing to store all data locally
Why it's wrong here
Storing all data locally at the edge keeps it off cloud analytics services, so no predictive model can train on the full sensor history. It is tempting because edge computing reduces latency and bandwidth for immediate control decisions, which would be right when the requirement is local real-time processing rather than centralised failure prediction.
- ✗
By setting up monitoring alerts for equipment failure
Why it's wrong here
Monitoring alerts fire after a threshold breach, reporting failure rather than forecasting it, so they cannot predict impending breakdowns. It is tempting because alerting is a familiar operational practice for equipment health, which would be correct when the goal is notifying staff of current conditions instead of anticipating failures from sensor patterns.
- ✗
By migrating all data to a data warehouse for batch analysis
Why it's wrong here
Batch warehouse analysis runs on historical data at intervals, so it cannot deliver the near-real-time scoring needed to warn of impending equipment failure. It is tempting because data warehouses support reporting and trend analysis, which would be correct when the goal is retrospective insight rather than predictive, latency-sensitive maintenance.
- ✓
By offering IoT Core and AI Platform to collect data and build predictive models
Why this is correct
IoT Core ingests telemetry from equipment sensors, while AI Platform trains predictive models on that data to forecast failures before downtime occurs. This directly satisfies the stem's requirement for predictive maintenance, since the cloud supplies both managed ingestion at scale and the machine-learning tooling needed to turn raw sensor readings into actionable failure predictions.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
Related to this question
Learn chapter
Structured vs Unstructured Data Analytics
Key term
Data warehouse
A data warehouse is a central repository that stores large amounts of structured data from multiple sources, optimized for querying and analysis rather than day-to-day transactions.
Key term
Batch
Batch is a cloud computing service that runs large numbers of computing jobs as a group, or batch, without needing to manage individual servers.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This GCDL practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the GCDL exam.