AZ-900 Describe cloud concepts Practice Question
A manufacturing company wants to process IoT sensor data locally at their factory floor to reduce latency before sending summarized data to the cloud. Which cloud computing concept does this represent?
⚠ Common exam trap
Candidates often confuse hybrid cloud with edge computing, thinking that any on-premises component automatically implies hybrid cloud, but edge computing specifically focuses on local data processing for latency reduction, not just a mix of cloud and on-premises resources.
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
✓
Edge computing
Edge computing is the correct concept because it involves processing data near the source (the factory floor) rather than sending it to a centralized cloud data center. This reduces latency by performing initial analysis locally, with only summarized results transmitted to the cloud. The scenario explicitly describes local processing to minimize delay, which is the core purpose of edge computing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Hybrid cloud
Why it's wrong here
Hybrid cloud is incorrect because it combines on-premises infrastructure (a private cloud) with public cloud services to allow workloads to run in the most appropriate environment, often for regulatory, cost, or performance reasons. Although hybrid cloud can include edge locations and might be used in a factory scenario, the question specifically highlights processing at the data source to reduce latency. Edge computing is a distinct architectural pattern that pushes compute directly to the location of data generation, whereas hybrid cloud is broader and focuses on the mix of private and public environments rather than proximity to the source.
- ✓
Edge computing
Why this is correct
Edge computing is the correct answer because it refers to performing data processing near the source of data generation, such as on a factory floor, rather than in a centralized cloud data center. This local processing dramatically reduces latency by eliminating the round-trip travel time to the cloud, which is critical for real-time industrial automation and IoT scenarios. In this context, edge computing enables immediate analysis and action on sensor data while still allowing select results to be sent to the cloud for long-term storage or aggregation.
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Public cloud
Why it's wrong here
Public cloud is incorrect because it describes a deployment model where infrastructure is owned and operated by a third-party cloud provider and shared among multiple tenants over the internet. While public cloud services can be used in conjunction with edge computing (e.g., for managing edge devices or storing data), the public cloud itself is centralized and remote, not local to the factory floor. Therefore, it does not inherently address the need to reduce latency by processing data at the source location.
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Disaster recovery
Why it's wrong here
Disaster recovery is incorrect because it focuses on maintaining or restoring business-critical systems and data after a disruptive event, such as a natural disaster, cyberattack, or hardware failure. Its primary goal is business continuity through backups, failover, and replication, not reducing the time it takes to process data at the edge. While edge computing can contribute to resilience by allowing continued local operation during a cloud outage, disaster recovery does not explain the local, low-latency processing described in the scenario.
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