WAEC Computer Science 2020
Questions & Answers

Students might mistakenly choose options like managing network traffic or increasing storage capacity, associating bio-inspired algorithms with general system administration or hardware tasks. These algorithms, including genetic algorithms and particle swarm optimization, tackle complex optimization problems by drawing from natural phenomena like evolution and animal swarms, iteratively improving solutions through mechanisms that mimic selection, crossover, and mutation. Common mistake: confusing algorithmic problem-solving techniques with physical hardware enhancements.

Students might mistakenly pick traditional silicon chips, as they represent the dominant paradigm in current computing architecture. Molecular computing instead leverages individual molecules or molecular assemblies to perform computations, allowing for massive parallelism and ultra-high density storage and computation at the nanoscale that goes beyond traditional silicon-based systems. Common mistake: assuming advanced computing systems must still rely on standard silicon hardware.

Students might mistakenly select centralized user data storage, confusing user-managed systems with traditional identity management platforms. Self-sovereign identity (SSI) empowers users to own, control, and manage their digital identities without relying on central authorities, utilizing decentralized technologies like blockchain to enhance privacy and security during authentication. Common mistake: thinking digital identity solutions require a central administrative server.

Students might mistakenly choose structured or imperative paradigms, which are common traditional programming styles. The event-driven programming paradigm focuses instead on responding to user actions, sensor inputs, or messages as events that trigger code execution, making it widely used in GUIs, web applications, and real-time systems for responsive behavior. Common mistake: confusing procedural execution flow with reactive, event-triggered logic.

Students might mistakenly select enhancing hardware performance or increasing data size, assuming quantum channels act merely as high-speed data pipes. Quantum entanglement channels facilitate quantum teleportation and secure key distribution in quantum networks, allowing correlated particles to enable effectively instantaneous information transfer without classical signaling, though limited by the no-communication theorem. Common mistake: conflating quantum entanglement communication with traditional high-bandwidth data transfers.

Students might mistakenly choose options implying reliance on electrical current only, missing the foundational magnetic property of these systems. Spintronics exploits the spin of electrons along with their charge for information processing and storage, enabling devices like MRAM to achieve lower power consumption and higher speed compared to standard charge-based electronics. Common mistake: ignoring the electron spin property and treating spintronic devices as ordinary electrical circuits.

Students might mistakenly select centralized data processing, assuming smart home environments require heavy cloud coordination. Ambient intelligence (AmI) instead integrates sensors, AI, and IoT for context-aware, unobtrusive interactions in smart homes, allowing devices to anticipate and respond to user needs seamlessly without explicit commands. Common mistake: assuming smart home automation requires direct user commands rather than context-aware anticipation.

Students might mistakenly pick Java, Python, or C++, which are popular general-purpose object-oriented or procedural programming languages. Prolog is specifically a logic programming language that supports constraint programming by defining problems through constraints and variables, allowing declarative solutions via backtracking and unification for search and optimization. Common mistake: confusing general-purpose programming languages with declarative logic and constraint languages.

Students might mistakenly select increasing network bandwidth, assuming holographic systems are designed for data transmission. Holographic data storage records information across the volume of a medium using interference patterns of laser light, enabling high-density, parallel access for terabyte-scale archival storage. Common mistake: confusing data storage capacity with data transmission bandwidth.

Students might mistakenly pick the access layer or core network, which handle general connectivity rather than immersive user environments. The sensing and perception layer in 6G architectures integrates AI-driven sensing tools like radar and LiDAR to enable immersive XR experiences, environmental awareness, and digital twins for enhanced reality applications. Common mistake: attributing user-facing immersive features to core network routing layers.

Students might mistakenly select optimizing database queries, associating reinforcement learning with backend data management. A reinforcement learning agent in robotics learns policies by interacting with environments and receiving rewards or penalties for actions, enabling adaptive behaviors like navigation or manipulation without explicit programming. Common mistake: confusing robotic behavioral learning with database optimization routines.

Students might mistakenly choose a rigid structure, overlooking the flexible nature required for wearable technology. Shape-memory alloys such as Nitinol deform under stress or heat and revert to predefined shapes, enabling adaptive, flexible components like self-adjusting braces or sensors. Common mistake: assuming advanced metal alloys in wearables maintain a fixed, inflexible form.

Students might mistakenly choose enhancing data storage or optimizing internet speed, confusing consensus mechanisms with hardware performance. Quorum systems in distributed computing require agreement from a majority, or quorum, of replicas for operations like reads and writes, ensuring consistency and fault tolerance in consensus protocols like Paxos or Raft. Common mistake: confusing distributed node consensus with data storage capacity.

Students might mistakenly select reduced sensory immersion, misinterpreting the addition of olfactory elements. Olfactory interfaces release scents synchronized with virtual reality content, adding the sense of smell to visual, auditory, and tactile stimuli, thereby deepening immersion in simulations such as training or gaming. Common mistake: underestimating the impact of multi-sensory integration, specifically smell, on virtual reality immersion.

Students might mistakenly select increasing data storage, assuming plasmonic systems are memory devices. Plasmonic circuits confine light to the nanoscale via surface plasmons, which are electron oscillations in metals, enabling ultra-fast, compact photonic interconnects for data transfer in chips. Common mistake: confusing photonic and plasmonic data transfer channels with data storage media.

Students might mistakenly choose traditional cyber threats like a Trojan or brute force attack, which target electronic computers rather than biological molecules. Bio-hacking involves manipulating or corrupting DNA strands used for data storage, exploiting biological vulnerabilities like enzymatic degradation or synthetic biology tools to access or alter encoded information. Common mistake: applying traditional software-based security terms to biological data storage systems.

Students might mistakenly select designing user interfaces, associating sentiment analysis with front-end web design. Sentiment analysis engines use natural language processing and machine learning to classify text from posts and comments as positive, negative, or neutral, helping platforms moderate content, gauge public opinion, or personalize feeds. Common mistake: confusing text emotion classification with graphical user interface design.

Students might mistakenly choose centralized control, assuming coordination requires a master controller. Swarm intelligence emerges from simple interactions among decentralized, self-organized agents such as ants and birds, solving complex problems like optimization without any central coordination. Common mistake: assuming complex collective behavior requires a centralized command structure.

Students might mistakenly pick enhancing graphic rendering or optimizing network traffic, confusing code protection with graphics processing. Cryptographic obfuscation transforms code and data into complex, unreadable forms using techniques like renaming and control flow flattening to deter reverse engineering while maintaining executable functionality. Common mistake: assuming code obfuscation alters the functional behavior of the program.

Students might mistakenly choose standard productivity applications like Microsoft Word, Notepad, or Paint, which rely on conventional keyboards and mice. Leap Motion is a device that tracks hand and finger movements in 3D space, enabling gesture-based control for applications like VR, CAD, or gaming without physical contact. Common mistake: confusing gesture-tracking hardware with standard office productivity software.