admin Oct, Tue, 2024 EXAM DEEP LEARNING 123456789101112131415161718192021222324252627282930313233343536373839404142434445 Exam Instructions: Total Number of Questions: 45 You will be required to answer a total of 45 multiple-choice questions. Time Limit: 15 minutes for the entire exam Once the time is up, the exam will automatically submit. Passing Criteria: A minimum score of 50% is required to pass the exam Multiple Attempts: You are allowed to take the exam multiple times. Only your highest score will be considered for certification. Additional Instructions: The exam timer cannot be paused once it begins. Good luck, and feel free to retake the exam to improve your score! DEEP LEARNING Test your DEEP LEARNING skills with a challenging exam designed to evaluate your knowledge in programming, data structures, and algorithms The certificate will be generated based on the information you provide in the form, so please ensure that all details are entered correctly. NameEmailPhone NumberUniversityCollegeDegreeDepartmentPass Out YearPass Out Year2014201520162017201820192020202120222023202420252026202720282029 1 / 45 1) What does the term “backpropagation” refer to? A) Increase complexity B) Simplify architecture C) Compute gradients D) Adjust weights 2 / 45 2) Which activation function is commonly used in the output layer for binary classification? A) Tanh B) Sigmoid C) ReLU D) Softmax 3 / 45 3) What is the role of the output layer in multi-class classification? A) Normalizes inputs B) Reduces dimensions C) Adjusts weights D) Provides class probabilities 4 / 45 4) What is one primary use of deep learning in finance? A) Risk assessment B) Customer segmentation C) Market prediction D) Fraud detection 5 / 45 5) What is the function of a loss function? A) Adjusts biases B) Increases loss C) Optimizes weights D) Quantifies prediction accuracy 6 / 45 6) What is the purpose of using the softmax function? A) Converts logits to probabilities B) Normalizes data C) Adjusts weights D) Reduces dimensions 7 / 45 7) What is the function of a pooling layer in CNNs? A) Normalize outputs B) Increase complexity C) Adjust weights D) Reduce spatial dimensions 8 / 45 8) What is the purpose of a confusion matrix? A) Evaluates classification performance B) Adjusts biases C) Increases accuracy D) Optimizes weights 9 / 45 9) What is a common method for sequence-to-sequence tasks in NLP? A) Encoder-decoder architectures B) CNNs C) RNNs D) GANs 10 / 45 10) Which neural network type is commonly used for language processing tasks? A) GANs B) RNNs C) Autoencoders D) CNNs 11 / 45 11) What does the term “gradient descent” refer to? A) Adjust weights B) Increase complexity C) Simplify architecture D) Minimize the loss function 12 / 45 12) Which technique is commonly used to reduce dimensionality in datasets? A) Adjust weights B) Simplify architecture C) Increase complexity D) Principal Component Analysis 13 / 45 13) What is the purpose of using a validation dataset? A) Test final accuracy B) Train the model C) Evaluate performance D) Increase complexity 14 / 45 14) What type of learning is used when the model receives no labels during training? A) Unsupervised learning B) Supervised learning C) Reinforcement learning D) Transfer learning 15 / 45 15) In a convolutional layer, what is the purpose of the kernel? A) Detect features B) Normalize data C) Reduce dimensions D) Adjust weights 16 / 45 16) What is the role of the hidden layer in a neural network? A) Simplify architecture B) Adjust weights C) Transform input data D) Increase complexity 17 / 45 17) What is a common strategy for tuning hyperparameters in deep learning? A) Increase training data B) Simplify architecture C) Adjust learning rates D) Grid search and random search 18 / 45 18) What does the term “batch normalization” refer to? A) Normalize layer inputs B) Increase complexity C) Simplify architecture D) Adjust weights 19 / 45 19) Which of the following methods can be used for hyperparameter tuning? A) Increase complexity B) Grid search C) Adjust weights D) Simplify architecture 20 / 45 20) What is the main advantage of using Convolutional Neural Networks (CNNs)? A) Use simpler architectures B) Learn spatial hierarchies C) Train faster D) Require manual feature selection 21 / 45 21) Which loss function is often used for multi-class classification tasks? A) Binary cross-entropy B) Categorical cross-entropy C) Hinge loss D) Mean squared error 22 / 45 22) What is the purpose of using a validation set? A) Tune hyperparameters B) Adjust weights C) Increase training speed D) Normalize data 23 / 45 23) What is a common application of deep learning in healthcare? A) Patient monitoring B) Drug discovery C) Medical image analysis D) Treatment planning 24 / 45 24) What is the purpose of the batch normalization layer? A) Adjust weights B) Stabilize training C) Simplify architecture D) Increase complexity 25 / 45 25) What is the function of a convolutional layer in a CNN? A) Reduces dimensions B) Produces predictions C) Extracts features D) Normalizes outputs 26 / 45 26) What does “sequence-to-sequence” learning typically involve? A) Reducing dimensions B) Mapping input to output sequences C) Adjusting learning rates D) Normalizing data 27 / 45 27) Which loss function is commonly used for binary classification tasks? A) Hinge loss B) Mean Squared Error C) Binary Cross-Entropy loss D) Categorical Cross-Entropy 28 / 45 28) What is the main purpose of a test set? A) Validates data B) Trains the model C) Evaluates model performance D) Reduces bias 29 / 45 29) What is the main advantage of using dropout layers in training? A) Normalize inputs B) Increase complexity C) Reduce overfitting D) Adjust weights 30 / 45 30) What is a common technique to enhance model robustness? A) Increase epochs B) Reduce data C) Simplify architecture D) Data augmentation and regularization 31 / 45 31) What does “data augmentation” achieve in deep learning? A) Reduces training time B) Adjusts learning rate C) Increases dataset size D) Normalizes inputs 32 / 45 32) What does “gradient descent” aim to achieve? A) Minimize loss function B) Simplify architecture C) Maximize accuracy D) Increase complexity 33 / 45 33) What is a common activation function used in hidden layers? A) Tanh B) Sigmoid C) ReLU D) Softmax 34 / 45 34) What is the function of a batch normalization layer? A) Reduces dimensions B) Adjusts weights C) Increases complexity D) Normalizes inputs 35 / 45 35) In deep learning, what is the significance of using dropout layers? A) Simplify architecture B) Increase training time C) Adjust weights D) Prevent overfitting 36 / 45 36) Which of the following is a benefit of using a convolutional neural network (CNN)? A) Increase model complexity B) Adjust weights C) Learn spatial hierarchies D) Simplify architecture 37 / 45 37) What is the key characteristic of recurrent neural networks (RNNs)? A) Increase complexity B) Reduce dimensions C) Simplify architecture D) Handle sequential data 38 / 45 38) Which of the following is a common technique to reduce overfitting? A) Early stopping B) Normalize outputs C) Increase learning rate D) Reduce data 39 / 45 39) What is an epoch in deep learning? A) Weight update step B) A single iteration C) Backpropagation step D) One complete pass 40 / 45 40) What is the primary function of the hidden layers in a neural network? A) Normalize inputs B) Learn representations C) Reduce dimensions D) Output predictions 41 / 45 41) What is the primary function of a loss function? A) Quantifies prediction error B) Increases complexity C) Reduces dimensions D) Adjusts weights 42 / 45 42) What is a common application of GANs (Generative Adversarial Networks)? A) Classifying data B) Time-series forecasting C) Regression tasks D) Generate realistic images 43 / 45 43) What is a common application of deep learning in healthcare? A) Medical image analysis B) Health monitoring C) Drug discovery D) Patient record management 44 / 45 44) What does dropout do in a neural network? A) Increases complexity B) Adjusts weights C) Reduces training time D) Prevents overfitting 45 / 45 45) What is a convolution in deep learning? A) Mathematical operation B) Weight adjustment C) Non-linear function D) Activation function Your score is LinkedIn Facebook Twitter VKontakte 0% ExamWEB DEVELOPMENT EXAM...Read MoreREACT JS PROGRAMMING EXAM...Read MorePYTHON FULL STACK...Read MorePYTHON EXAMS ...Read MoreMYSQL...Read MoreMACHINE LEARNING...Read MoreJAVASCRIPT PROGRAMMING EXAM...Read MoreJAVA SPRING BOOT...Read MoreJAVA PROGRAMMING EXAM...Read MoreJAVA FULL STACK...Read MoreHTML PROGRAMMING EXAM...Read MoreDEEP LEARNING...Read MoreDATA SCIENCE...Read MoreCSS PROGRAMMING EXAM...Read MoreANGULAR JS PROGRAMMING EXAM...Read More 14Share on WhatsApp10Share on LinkedIn5Share on YouTube9Share on Facebook Comments 0