WAEC Computer Science 2021
Questions & Answers

A student might mistakenly choose to manage network traffic or increase storage capacity if they confuse specialized AI hardware with standard routers or hard drives. Mimicking neural structures for efficient processing is correct because neuromorphic chips emulate biological brains, enabling energy-efficient parallel processing for tasks like pattern recognition outside traditional von Neumann limits. Common mistake: Equating brain-inspired hardware architectures with general-purpose memory or networking equipment.

A student might mistakenly pick traditional mouse or conventional speakers, thinking of standard user-input computer peripherals. Neural signal sensors are the correct choice since components like EEG electrodes or invasive implants form the absolute core of a BCI system by capturing brain activity to translate thoughts into commands. Optical cables and traditional speakers are standard output or transmission media, not brain-reading interfaces. Common mistake: Confusing standard computer input hardware with direct neural measurement devices.

A student might mistakenly choose to centralize data storage, confusing decentralized frameworks with conventional cloud servers. Enabling self-governing entities via smart contracts is correct because a DAO leverages blockchain smart contracts to automate governance and fund allocation transparently without needing any central authority. Common mistake: Assuming decentralized systems are used to concentrate administrative control rather than automate distributed governance.

A student might mistakenly select Object-Oriented or Imperative programming, which are common paradigms that often manage mutable states. Functional programming is correct because it emphasizes immutable data structures, effectively preventing unintended modifications and side effects to enhance security in concurrent systems. Common mistake: Believing that standard object-oriented state-changing models inherently provide better data security than immutability.

A student might mistakenly select increasing data size or enhancing hardware performance, misinterpreting cryptographic protocols for physical performance boosters. Verifying information without revealing it is correct because zero-knowledge proofs allow one party to prove they know a value without disclosing the actual underlying data, ensuring privacy in protocols like zk-SNARKs. Common mistake: Confusing data concealment techniques with data compression or hardware optimization.

A student might mistakenly choose 'Uses electrical signals only', confusing optical systems with traditional electronics. Relies on light-based data transmission is the correct option because photonic computing utilizes photons for data processing and transmission, yielding vastly higher speeds and lower energy usage than electrons. Common mistake: Assuming advanced computing chips must rely strictly on electron flow.

A student might mistakenly choose centralized data storage, assuming global models require gathering all raw data into one server. Training models on local devices without sharing data is correct because federated learning keeps sensitive information localized on end-user devices while safely aggregating only model updates centrally. Common mistake: Thinking that collaborative machine learning training always demands compromising user data privacy via central pooling.

A student might mistakenly select SQL or Python, which are widely recognized general programming and relational query tools. Cypher is correct because it is a declarative query language built specifically for graph databases like Neo4j to handle connected data structures. Common mistake: Assuming relational database languages like SQL work natively for complex graph traversal.

A student might mistakenly choose to enhance graphic design, mixing up industrial simulations with video game rendering engines. To create a virtual replica for monitoring and simulation is correct because a digital twin models a physical asset to drive real-time predictive maintenance and optimization. Common mistake: Confusing digital engineering twins with artistic 3D computer graphics.

A student might mistakenly select the Core network or Application layer, overlooking where edge wireless connection occurs. Radio access network is correct because the RAN specifically manages the wireless communication link between user equipment and the broader cellular core. Common mistake: Confusing the mobile device-facing air interface with backend core network switching components.

A student might mistakenly choose to optimize database queries, mixing up generative machine learning models with backend data architectures. To generate new data using competing models is correct because GANs deploy a generator to synthesize data and a discriminator to evaluate it, producing hyper-realistic outputs like deepfakes. Common mistake: Confusing generative AI synthesis models with database optimization tools.

A student might mistakenly select rigid structure or high power usage, assuming cutting-edge screens retain traditional structural limitations. Bendable and adaptable screens are correct since flexible displays leverage organic LEDs or e-ink on pliable substrates to build foldable devices and wearables. Common mistake: Believing modern display advancements focus solely on resolution rather than physical form factor adaptability.

A student might mistakenly choose to centralize data storage, confusing database scaling methods with legacy single-server setups. To partition data across multiple servers is correct because sharding horizontally splits large database tables across multiple shards to boost NoSQL and overall system scalability. Common mistake: Confusing data partitioning strategies with data consolidation.

A student might mistakenly choose reduced data security or simplified hardware maintenance, anticipating that immersive tech introduces architectural flaws. 3D interactive video calls are correct because holographic communication projects three-dimensional spatial images to drastically improve remote collaboration in telemedicine and virtual meetings. Common mistake: Assuming advanced visual display technologies inherently compromise system security or network integrity.

A student might mistakenly select to increase data storage, confusing security key protocols with high-capacity memory drives. To secure communication using quantum mechanics is correct because QKD utilizes physical phenomena like quantum entanglement to distribute encryption keys while instantly exposing any attempted eavesdropping. Common mistake: Confusing cryptographic key generation mechanisms with physical data storage capacity.

A student might mistakenly pick man-in-the-middle attacks because it is a prevalent general network threat. Side-channel attack is correct because it targets the physical implementation vulnerabilities of quantum cryptographic devices—such as measuring hardware power consumption or timing leaks—rather than breaking the core mathematical or quantum protocols directly. Common mistake: Assuming quantum security protocols are immune to physical hardware leakage attacks.

A student might incorrectly choose options like A, C, or D if they mistakenly associate data generation with UI design, network speeds, or hardware configuration rather than dataset preparation. By producing realistic datasets to augment limited real data, synthetic data generators enable training in scenarios with scarce or sensitive information while preserving privacy. Common mistake: Confusing machine learning data preparation tasks with physical hardware or user interface design.

Options like A, C, or D could be mistakenly selected if a student assumes that network recovery requires human intervention, is restricted exclusively to wired setups, or lacks backup pathways. Operating without human intervention, self-healing networks use AI and automation to identify issues like link failures and reroute traffic or repair configurations. Common mistake: Overlooking the role of automated artificial intelligence in handling network configurations and link failures.

A student might incorrectly select options A, C, or D by confusing cryptographic operations with graphic rendering, storage expansion, or traffic optimization. Enabling secure cloud computing, homomorphic encryption allows arithmetic operations on ciphertexts, yielding encrypted results that match operations on plaintexts. Common mistake: Assuming encryption tools are strictly for storage space or network speeds rather than computational security.

Options like A, C, or D might be selected if a student mistakes standard software applications like word processors, text editors, or drawing tools for intelligent virtual agents. Developed by Amazon, Alexa responds to voice commands for tasks like playing music, controlling smart homes, and answering queries using natural language processing. Common mistake: Confusing general-purpose desktop applications with voice-activated AI assistants.