Artificial Intelligence - Exam
A) Automated Intelligence B) Advanced Intelligence C) Analogous Integration D) Artificial Intelligence
A) A test to determine the power consumption of a machine B) A test to evaluate a machine's physical strength C) A test of a machine's ability to exhibit intelligent behavior indistinguishable from a human D) A test to measure a machine's processing speed
A) Java B) Ruby C) Python D) C++
A) A subset of AI that enables machines to learn from data B) A method to improve network security C) A process of assembling hardware components D) A technique to manually program machines
A) Rapid Notification Node B) Recurrent Neural Network C) Robust Neuron Navigator D) Regular Numeric Notation
A) A measure of data complexity B) A hypothetical future point at which AI surpasses human intelligence and control C) A weather manipulation technique D) A type of machine learning algorithm
A) Analyzing audio signals B) Generating random pixel patterns C) Mimicking human vision and identifying objects in images or videos D) Testing computer hardware components
A) A program for graphic design B) A program for virtual reality gaming C) A program for music composition D) A program that simulates conversation with human users
A) Finding the shortest path in a graph B) Generating random numbers C) Detecting errors in data D) Optimizing computer memory usage
A) Nonlinear Linguistic Pattern B) Networked Logistic Performance C) Natural Language Processing D) Neural Learning Protocol
A) 1956 B) 1972 C) 1980 D) 1965
A) Learning B) Knowledge representation C) Quantum computing D) Reasoning
A) Intel B) Microsoft C) IBM D) OpenAI
A) Recurrent neural network B) Convolutional neural network C) Transformer architecture D) Perceptron
A) Autonomous vehicles B) Recommendation systems C) Advanced web search engines D) Virtual assistants
A) Neuroscience B) Astronomy C) Psychology D) Linguistics
A) Quantum entanglement B) Formal logic C) Artificial neural networks D) State space search
A) 2000s B) 1990s C) 2020s D) 2010s
A) Existential risks B) Lower energy consumption C) Decreased computational power D) Reduced software complexity
A) Early AI could not handle logical deductions. B) They were unable to process any form of incomplete information. C) They experience a 'combinatorial explosion' where they become exponentially slower as problems grow. D) These algorithms required human intervention for every step.
A) Humans use fast, intuitive judgments rather than step-by-step deduction. B) Humans solve problems by following pre-defined algorithms. C) Humans rely solely on logical deductions similar to early AI models. D) Humans use a combination of intuition and probabilistic reasoning exclusively.
A) No clear objective or preference. B) Multiple goals to achieve simultaneously. C) Randomly assigned tasks with no particular order. D) A specific goal.
A) Unsupervised learning B) Transfer learning C) Reinforcement learning D) Supervised learning
A) Regression requires more data than classification. B) Classification predicts categories while regression deduces numeric functions. C) Classification uses neural networks while regression does not. D) Classification is a type of unsupervised learning.
A) Speech synthesis B) Machine translation C) Information retrieval D) Word embedding
A) Recurrent neural networks (RNNs) B) Transformers C) Generative pre-trained transformers (GPT) D) Convolutional neural networks (CNNs)
A) Textual sentiment analysis. B) Object tracking. C) Speech recognition. D) Image classification.
A) Local search. B) Adversarial search. C) Particle swarm optimization. D) Gradient descent.
A) Swarm intelligence algorithms. B) Backpropagation algorithm. C) Mathematical optimization. D) Means-ends analysis.
A) Ant colony optimization. B) Gradient descent. C) Particle swarm optimization. D) Evolutionary computation.
A) Evolutionary computation. B) Particle swarm optimization. C) Deductive reasoning. D) Inductive reasoning.
A) It uses swarm intelligence algorithms. B) Inference is undecidable, making it intractable. C) It assigns degrees of truth between 0 and 1. D) It requires gradient descent for optimization.
A) Gradient descent. B) Evolutionary computation. C) Ant colony optimization. D) Particle swarm optimization.
A) Bayesian networks B) Markov decision processes C) Kalman filters D) Dynamic decision networks
A) Decision analysis B) Mechanism design C) Expectation–maximization algorithm D) Information value theory
A) Support vector machine B) Naive Bayes classifier C) Decision tree D) K-nearest neighbor algorithm
A) K-nearest neighbor algorithm B) Naive Bayes classifier C) Decision tree D) Support vector machine
A) Controllers B) Classifiers C) Neural networks D) Bayesian networks
A) Decision tree B) Support vector machine C) Naive Bayes classifier D) K-nearest neighbor algorithm
A) Dynamic decision networks B) Decision analysis C) Hidden Markov models D) Game theory
A) Google Assistant B) Cortana C) Alexa D) Siri
A) Neural networks B) Classifiers C) Controllers D) Bayesian networks
A) 3% B) 5% C) 10% D) 7%
A) rStar-Math B) Gemini Deep Think C) Qwen2-Math D) AlphaTensor
A) 5% B) 7% C) 10% D) 3%
A) Moore's law. B) Bell's law. C) Gibson's law. D) Huang's law.
A) DeepMind B) Microsoft C) IBM D) Google
A) $1.5 trillion B) $4.0 trillion C) $3.5 trillion D) $2.7 trillion
A) Gemini B) ChatGPT C) Prolog D) Claude
A) Amazon B) Microsoft C) Apple D) Google
A) 2026 B) 2025 C) 2030 D) 2028
A) Deep Blue B) Watson C) MuZero D) AlphaStar
A) May 2025 B) February 2023 C) December 2017 D) July 2024
A) 84% B) 75% C) 53% D) 90%
A) Microsoft B) Google DeepMind C) Alibaba Group D) OpenAI
A) Chief Technology Officer (CTO) B) Chief Information Officer (CIO) C) Chief Data Officer (CDO) D) Chief Automation Officer (CAO)
A) $25 million B) $10 million C) $50 million D) $100 million
A) 15% B) 47% C) 30% D) 9%
A) 60% B) 9% C) 47% D) 25%
A) Exactly 61% B) 80% C) 50% D) 75%
A) Singapore B) Taiwan C) United States D) Japan
A) Fukushima B) Palisades Nuclear reactor C) Susquehanna D) Three Mile Island
A) Markov decision processes B) Game theory C) Mechanism design D) Dynamic Bayesian networks
A) Pluribus B) SIMA C) MuZero D) AlphaStar
A) 8% B) 10% C) 12% D) 5%
A) Predictive fairness B) Representational fairness C) Distributive fairness D) Procedural fairness
A) Maximizing user engagement B) Reducing misinformation spread C) Promoting accurate information D) Enhancing content diversity
A) 2021 B) 2019 C) 2023 D) 2024
A) Confirmation bias B) Information overload C) Filter bubbles D) Echo chambers
A) Various topological approaches B) Probabilistic models C) Monte Carlo tree search D) Natural language processing
A) Tesla, SpaceX, Uber, Lyft B) Alphabet Inc., Amazon, Apple Inc., Meta Platforms, Microsoft C) Nike, Adidas, Puma, Reebok D) Coca-Cola, PepsiCo, Red Bull, Monster
A) MuZero B) Deep Blue C) AlphaStar D) Watson
A) 75% B) 90% C) 53% D) 84%
A) Faces B) Edges C) Digits D) Whole objects
A) Blockchain technology B) Cloud storage C) Data encryption D) Differential privacy
A) 90% B) 50% C) 70% D) 30%
A) 15 times B) 20 times C) 10 times D) 5 times
A) 2013 B) 2016 C) 2015 D) 2014
A) Gradient descent B) Backpropagation C) Stochastic gradient descent D) Forward propagation
A) Real-time strategy games. B) Chess and Go. C) Jeopardy! quiz shows. D) Imperfect-information games like poker.
A) Stephen Hawking B) Wendell Wallach C) Eliezer Yudkowsky D) Stuart J. Russell
A) rStar-Math B) AlphaTensor C) Qwen-7B D) Gemini Deep Think
A) 5% B) 22% C) 50% D) 75%
A) Gordon Moore. B) John McCarthy. C) Jensen Huang. D) Alan Turing.
A) 50% B) 20% C) 5% D) 10%
A) Dynamic Bayesian networks B) Decision theory C) Kalman filters D) Expectation–maximization algorithm
A) Lethal autonomous weapon B) Cybersecurity tool C) Drones used for surveillance D) Conventional firearm
A) They require constant internet connectivity. B) Built-in security measures can be trained away until ineffective. C) Their architecture and parameters are kept secret. D) They cannot be used for commercial purposes.
A) Keras. B) PyTorch. C) TensorFlow. D) Scikit-learn.
A) Tim Cook B) Bill Gates C) Elon Musk D) Geoffrey Hinton
A) DALL-E B) GPT-3 C) ChatGPT D) AlphaGo
A) Stephen Hawking B) Eliezer Yudkowsky C) Stuart J. Russell D) Wendell Wallach
A) Faux images B) AI clones C) Synthetic media D) Deepfakes
A) Watson B) Deep Blue C) MuZero D) AlphaGo
A) Generate text based on semantic relationships between words. B) Predict future stock market trends. C) Translate languages in real-time. D) Analyze and interpret images.
A) 10% B) 50% C) 25% D) About 4%
A) Both directions B) Only one direction C) Backwards D) Randomly
A) Personhood credentials B) AI ethical guidelines C) Digital signatures D) Blockchain verification
A) Amazon B) Talen Energy C) Microsoft D) Constellation Energy
A) Moral robotics B) Ethical computing C) Artificial intelligence ethics D) Computational morality |
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