A case study discusses a farming project where soil moisture sensors were used in conjunction with AI to optimize irrigation. The integration of AI in this scenario primarily serves to:
- Automate Harvesting
- Enhance Water Efficiency
- Improve Soil Fertility
- Increase Crop Yields
In the context of agriculture, the integration of AI with soil moisture sensors primarily serves to enhance water efficiency, ensuring that irrigation is applied precisely where and when it's needed, conserving water resources.
An agriculture firm is using drones to capture field images. To process this data immediately without sending it to a central cloud, the firm should use:
- Blockchain technology
- Cloud-based data processing
- Edge computing
- Hadoop cluster
In an agriculture scenario where immediate data processing is needed, edge computing is the suitable choice. Edge computing processes data locally (in the drones) without the need to send it to a central cloud, reducing latency and improving efficiency for real-time applications.
The integration of ________ technology marked a significant evolution in the capabilities of IoT devices.
- 5G
- Artificial Intelligence
- Augmented Reality
- Quantum Computing
The integration of "5G" technology marked a significant evolution in the capabilities of IoT devices. The high-speed, low-latency nature of 5G networks enables faster and more efficient communication between IoT devices, paving the way for new applications and functionalities.
A company is planning to deploy IoT devices across its infrastructure. Before doing so, it seeks certification from a recognized body to ensure the devices meet security standards. They are likely seeking certification from:
- Federal Communications Commission (FCC)
- IEEE Standards Association
- ISO/IEC 27001
- Wi-Fi Alliance
ISO/IEC 27001 is a widely recognized standard for information security management systems. Seeking certification from this body indicates a commitment to maintaining high-security standards for IoT devices.
The primary distinction between IoT security standards and regulatory compliance is:
- Focus on implementation details
- Legal requirements vs. best practices
- Network architecture vs. data integrity
- Technical specifications vs. encryption
IoT security standards primarily involve best practices and technical specifications for securing IoT devices and data, while regulatory compliance pertains to legal requirements set by authorities.
In the context of IoT projects, the study of successful implementations to understand best practices is referred to as ________.
- Best Practice Evaluation
- Case Analysis
- Lessons Learned
- Success Studies
In IoT projects, the study of successful implementations to understand best practices is often referred to as "Lessons Learned." This involves analyzing successful cases to identify strategies, approaches, and practices that can be applied to future projects.
Which of the following is a primary concern when it comes to IoT data security?
- Data compression techniques
- Data encryption and decryption
- Data transmission latency
- Unauthorized access and data breaches
A primary concern in IoT data security is unauthorized access and data breaches. IoT devices often handle sensitive information, making them targets for cyberattacks. Ensuring that data remains secure and protected from unauthorized access is a critical consideration.
The use of hardcoded credentials in IoT devices can lead to:
- Better user experience
- Enhanced security
- Improved device management
- Security vulnerabilities
Hardcoded credentials, such as default usernames and passwords, can lead to serious security vulnerabilities. Attackers can easily guess or find these credentials, potentially compromising the IoT device and the network it's connected to.
What does predictive analytics in IoT primarily focus on?
- Analyzing historical data to make informed future predictions
- Collecting and storing data from IoT devices
- Monitoring real-time data for immediate action
- Securing IoT devices from cyber threats
Predictive analytics in IoT primarily focuses on analyzing historical data from IoT devices to make informed future predictions. By examining past data, IoT systems can forecast future trends, troubleshoot issues, and optimize operations.
When developing an IoT application for data-intensive tasks, which programming language offers significant advantages due to its data handling capabilities?
- C
- Python
- R
- Swift
Python is favored for data-intensive IoT applications due to its extensive libraries for data processing and analysis. It's versatile and has a large community, making it suitable for a wide range of data-related tasks in IoT. While C, R, and Swift have their strengths, Python stands out in this context.