IoT Integration with Cloud Platforms Questions and Answers for Viva

Answer-1: By analyzing sensor data trends to predict failures and schedule proactive maintenance.

Answer-2: Streaming services ingest and process continuous IoT data in real-time for instant insights.

Answer-3: They provide tools for provisioning, updating, monitoring, and decommissioning devices.

Answer-4: Costs include data transfer, storage, processing, and device connectivity fees.

Answer-5: Through common data formats, APIs, and middleware that unify device communications.

Answer-6: Managing traffic, environmental monitoring, and public safety using real-time IoT data analytics.

Answer-7: Cloud storage solutions ensure redundant backups and quick recovery of IoT data.

Answer-8: IoT cloud services specialize in handling device connectivity, telemetry, and IoT-specific data processing.

Answer-9: By implementing TLS/SSL encryption, mutual authentication, and secure APIs.

Answer-10: Dashboards visualize IoT data trends, alerts, and key metrics for easy monitoring.

Answer-11: By providing real-time tracking, inventory monitoring, and predictive analytics.

Answer-12: Reliable networks ensure continuous data flow and reduce downtime in IoT applications.

Answer-13: Through protocols and services that sync data processed at the edge with cloud storage.

Answer-14: Orchestration automates and manages workflows, scaling, and integration between IoT devices and cloud services.

Answer-15: By enforcing policies on data access, retention, and compliance across devices and cloud systems.

Answer-16: Increased edge computing, AI integration, improved security, and broader device interoperability.

Answer-17: It is the process of connecting IoT devices and sensors to cloud services for data storage, processing, and analytics.

Answer-18: Cloud provides scalable infrastructure, data storage, real-time analytics, and remote device management essential for IoT.

Answer-19: AWS IoT Core, Microsoft Azure IoT Hub, Google Cloud IoT Core, IBM Watson IoT.

Answer-20: An IoT gateway acts as a bridge between IoT devices and the cloud, handling data processing, protocol translation, and security.

Answer-21: Cloud platforms offer services like stream processing and event handling that analyze IoT data as it arrives.

Answer-22: MQTT, HTTP/HTTPS, CoAP, AMQP.

Answer-23: MQTT is a lightweight messaging protocol designed for low-bandwidth, high-latency, or unreliable networks, ideal for IoT.

Answer-24: Azure IoT Hub provides secure bi-directional communication, device provisioning, firmware updates, and monitoring.

Answer-25: Challenges include data privacy, device authentication, secure communication, and protection against attacks.

Answer-26: They use scalable object storage, time-series databases, and data lakes to store massive IoT data efficiently.

Answer-27: Edge computing processes data near the IoT devices to reduce latency and bandwidth before sending to the cloud.

Answer-28: Cloud platforms provide tools for big data analytics, machine learning, and visualization on IoT data.

Answer-29: APIs enable communication between IoT devices, cloud services, and applications for data exchange and control.

Answer-30: Through automated device provisioning services that register devices securely and configure settings.

Answer-31: Device shadow is a virtual representation of a device?s state stored in the cloud to enable offline and asynchronous interaction.

Answer-32: IoT systems must handle growing numbers of devices and data without performance degradation, which cloud platforms provide.

Answer-33: Normalizing IoT data formats makes it easier to process, analyze, and integrate with other systems.

Answer-34: By using encryption, secure communication protocols, and validation mechanisms during data transfer and storage.

Answer-35: Serverless allows running code triggered by IoT events without managing servers, enabling scalable and cost-efficient processing.

Answer-36: It offers secure device registration, telemetry ingestion, and integration with Google Cloud analytics tools.

Answer-37: Latency can affect real-time applications; edge computing and optimized cloud services help reduce delays.

Answer-38: A digital twin is a cloud-based virtual model of a physical device or system for monitoring and simulation.

Answer-39: Cloud platforms provide over-the-air (OTA) update mechanisms for remote and secure firmware deployment.

Answer-40: Handling heterogeneous devices, ensuring security, managing data volume, and network reliability.

Answer-41: It controls device and user authentication, authorization, and access to cloud resources.

Answer-42: Through protocol translation services and gateways that allow devices with different protocols to communicate.

Answer-43: An architecture where IoT data triggers automatic workflows or functions in the cloud.

Answer-44: Data is collected, processed, stored, analyzed, and archived within cloud platforms throughout its lifecycle.

Answer-45: By providing tools for data encryption, auditing, and compliance certifications.

Answer-46: It ensures continuous operation and data availability even if parts of the system fail.

Answer-47: They offer machine learning, AI models, and dashboarding tools tailored for IoT use cases.

Answer-48: AWS Greengrass extends cloud capabilities to local devices for edge processing and secure device communication.

Answer-49: Through encryption, anonymization, and strict access controls to protect user and device data.

Answer-50: SDKs provide libraries and tools to build applications that interact with cloud IoT services.