40405211
Beschreibung
Quiz von Sumukh Hegde, aktualisiert vor 8 Monate
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Erstellt von Sumukh Hegde
vor 8 Monate
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Frage 1
Frage
Which of the following best describes the No Free Lunch Theorem in AI?
Antworten
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AI algorithms perform better when trained with more data
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No single algorithm is best for all problems
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AI algorithms cannot surpass human intelligence
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Training AI models requires computational optimization
Frage 2
Frage
What is catastrophic forgetting in neural networks?
Antworten
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Overfitting to the training data
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Deletion of weights during training
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Loss of previously learned information when learning new tasks
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Memory overflow during backpropagation
Frage 3
Frage
In Deep Q-Networks (DQN), what is the main purpose of the target network?
Antworten
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To reduce training time
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To explore the environment
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To stabilize learning by fixing the Q-value targets
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To store policy gradients
Frage 4
Frage
Which activation function is most prone to the dying neuron problem?
Antworten
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Sigmoid
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Tanh
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ReLU
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Swish
Frage 5
Frage
What does the attention mechanism do in Transformer models?
Antworten
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A. Reduces model parameters
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B. Assigns weights to input tokens based on relevance
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C. Prevents overfitting
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D. Compresses the input sequence
Frage 6
Frage
Which technique is commonly used to deal with the vanishing gradient problem in RNNs?
Antworten
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Dropout
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Gradient clipping
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Batch normalization
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Layer normalization
Frage 7
Frage
In game-playing AI, the Minimax algorithm assumes that:
Antworten
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Both players make optimal decisions
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Only one player uses a rational strategy
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Players take random actions
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The game is stochastic in nature
Frage 8
Frage
Which of the following models does not use the Markov property explicitly?
Antworten
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Hidden Markov Model (HMM)
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Q-learning
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Recurrent Neural Network (RNN)
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Markov Decision Process (MDP)
Frage 9
Frage
In transfer learning, which layer of a pre-trained network is most commonly replaced or fine-tuned for a new task?
Antworten
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Initial convolutional layers
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All hidden layers
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Output layer
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Pooling layer
Frage 10
Frage
Which of the following is not a valid assumption in the Naive Bayes classifier?
Antworten
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Features are independent given the class
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The model uses Bayes' theorem
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Features are conditionally dependent on each other
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The prior probabilities are used
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