Question 21: Which language type is used for hybrid apps?
A. A language specific to each supported platform
B. A language focused on client-side technologies that can be accessed from any device
C. A language wrapped in a platform-specific shell
D. None of the above
Correct Answer: C. A language wrapped in a platform-specific shell
Explanation: Web apps are relatively easy to build and deploy. They use widespread languages, such as HTML5, and can run cross-platform on any device with an HTML5-enabled browser. Native apps run smoothly on their host platforms, but they require developers to know the mobile app programming language specific to each platform and to create an app for each.
Hybrid apps use the best of both native apps and web-based apps. They offer the cross-platform ability of web apps, which use one set of code for all devices. By wrapping the code in a platform-specific shell, developers benefit from native apps’ access to more platform-specific features. Developers need to consider these pros and cons when choosing the type of app that is best for them.
Question 22: What are the advantages of mobile application development platforms?
A. They don’t require developers to learn a new coding language
B. They simplify the development process with point-and-click and drag-and-drop operations
C. They require fewer resources to create mobile apps
D. All of the above
Correct Answer: D. All of the above
Explanation: When the developers and resources are not available for mobile app development, organizations can consider rapid mobile application development platforms (MADPs). In some cases, MADPs don’t require the user to know any code. They can develop apps for multiple devices and OSes, support cross-platform development and use intuitive drag-and-drop interactions to ease the mobile app development process. This can limit how much a developer can customize the app, however.
Question 23: Which open source framework creates native apps through cross-platform development?
Correct Answer: A. NativeScript
Question 24: IoT edge computing is processing raw data at the boundaries of the network, as far away from the points of data creation as possible.
Correct Answer: B. False
Explanation: IoT edge computing is processing data where it is created or as close as possible, which, in this case, is at the edge of a network. In the case of IoT deployments, the data can be processed on the devices themselves or on edge gateways. IT pros may also come across the term fog computing and still debate whether it is the same thing as edge computing. Some pros distinguish fog computing as taking place in processing areas such as gateways, not at the machines creating the data nor in the cloud. Tied to this definition of fog computing, IT pros would define edge computing more narrowly as data processing that takes place directly on the machines creating the data.
Question 25: Which of the following is a challenge of IoT edge computing?
A. Increased security risks
B. Inconsistent industry standards and regulations
C. Lack of support for new devices
D. All of the above
Correct Answer: D. All of the above
Explanation: IoT deployments typically spread beyond traditional IT infrastructure, which creates more entry points for a cyberattack. The lack of IoT standards not only makes management of IoT devices on the edge more difficult for administrators, it also make them vulnerable. When IoT developers design devices with different communication protocols or even operating systems, administrators will have trouble applying updates across the board. In addition to the spread of IoT devices, the number of IoT devices is also growing, making it even more vital that administrators have a way to keep track of all devices. Without knowing what devices are in use and how much data is collected and transmitted, edge computing can cause security risks and create latency issues. Although security can open more opportunities for malicious acts, there are also aspects of edge computing that make it more secure than sending and keeping data in a centralized cloud.
Question 26: Which issue couldn’t IoT edge computing help solve?
A. Bandwidth issues
B. Complex connectivity issues
C. Legacy-system bridge issues
D. Data sovereignty compliance issues
Correct Answer: B. Complex connectivity issues
Explanation: IoT edge computing resolves many problems by keeping and processing the data from IoT devices at the edge where it is created. The expansion of the edge with the proliferation of IoT devices has created more complex networks and connectivity issues that organizations will likely continue to scale. Cutting out the distance of data transfers to the cloud or data center for processing lowers latency and cuts out the power and bandwidth needed to transmit. In turn, lower bandwidth cuts the costs in finances, opportunity costs and storage of frequent small IoT application updates. Edge data processing also addresses any data sovereignty compliance issues by preventing data from being stolen during transit, including transfers between different countries. Organizations that combine legacy systems with IoT deployments must contend with the non-IP or ethernet connections of IoT devices. IoT edge computing fixes this issue by translating between old and new.
Question 27: One application of IoT edge computing is using sensors, real-time data analytics and data operations to run a self-driving car.
Correct Answer: A. True
Explanation: Self-driving cars require real-time data analytics at the edge because even the milliseconds it takes to transmit data to the cloud is too much latency when lives are at stake. Cars with IoT edge computing combined with AI will make immediate decisions where the data is created. It is also impossible to simultaneously transmit data from millions of cars back to a data center to track vehicles and process decisions with immediacy. Edge computing also applies in use cases with IoT sensors such as identifying and analyzing production errors more quickly in manufacturing plants, conserving resources by monitoring water consumption and reducing latency for real-time applications such as online multiplayer games.
Question 28: What connects IoT devices to the cloud in order to aggregate data, translate between protocols and process data before sending it on?
A. IoT sensors
B. IoT standards
C. IoT gateways
D. IoT processors
Correct Answer: C. IoT gateways
Explanation: IoT gateways, which can be interchangeable with the term edge gateways, manage and connect IoT devices to the cloud. Despite their name, they serve a greater role than simply allowing data back and forth. IoT devices use different protocols or have different energy requirements that don’t all support each other. Gateways ensure that all IoT devices can connect, translate data to a standard protocol and maintain security. Gateways also help bridge operations and IT perspectives on IoT deployments. Operations professionals require the data gets transferred from its creation to where they can use it. Gateways assist the IT angle because they ensure security and support functionality. IoT gateways must be capable of withstanding the processing demands of IoT data.
Question 29: How will the edge change organizations’ relationship with the cloud?
A. The edge will send more data directly to the cloud
B. The edge will reduce the amount of data sent to the cloud, potentially saving organizations money
C. Organizations will use the cloud the same way and just add edge computing
D. None of the above
Correct Answer: B. The edge will reduce the amount of data sent to the cloud, potentially saving organizations money
Explanation: Bringing data processing to the edge will reduce the data sent to centralized data processing in the cloud. The increase in IoT data pushes organizations to figure out how to use that data more economically in real-time and longer-term analysis. Organizations will spend less on cloud data storage when the data is processed at the edge without needing to connect to the cloud. Organizations that keep their data in a centralized cloud are also vulnerable to greater risk, such as data breaches. Edge computing encourages organizations to stop creating honeypots of sensitive data in the cloud.
Question 30: How much enterprise data does Gartner predict will be created and processed at the edge by 2025?
Correct Answer: C. 75%
Explanation: In 2018, only 10% of an organization’s data was at the edge, according to Gartner senior research director Santhosh Rao, in the Gartner report “What Edge Computing Means for Infrastructure and Operations Leaders.” By 2025, Gartner estimated the number will grow to 75% of all enterprise data. The increase in data created at the edge will make transmitting the IoT data deluge to a cloud inefficient.