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 type of learning involves using labeled data? A) Reinforcement learning B) Unsupervised learning C) Semi-supervised learning D) Supervised learning 2 / 45 2) Which of the following is a common performance metric for classification models? A) Recall B) Precision C) Accuracy D) F1 Score 3 / 45 3) What type of neural network is often used for natural language processing tasks? A) CNNs B) GANs C) Autoencoders D) RNNs and Transformers 4 / 45 4) What is a common application of transfer learning? A) Computer vision B) Text generation C) Data augmentation D) Regression tasks 5 / 45 5) What is a neural network? A) A simple model B) A set of functions C) A type of regression D) A series of algorithms 6 / 45 6) What is the purpose of using an ensemble model in deep learning? A) Improve accuracy B) Simplify architecture C) Normalize data D) Reduce training time 7 / 45 7) What is an essential characteristic of a feedforward neural network? A) Recursive connections B) Multiple outputs C) Parallel processing D) No cycles 8 / 45 8) What is “cross-validation” in deep learning? A) Evaluate model performance B) Simplify architecture C) Adjust weights D) Increase complexity 9 / 45 9) What is the role of the hidden layers in a neural network? A) Adjust weights B) Transform input C) Produce output D) Normalize data 10 / 45 10) What does “data leakage” refer to in model evaluation? A) Overfitting B) Adjust weights C) Information from test set used D) Simplify architecture 11 / 45 11) What does “normalization” do in the context of deep learning? A) Reduces dimensions B) Scales input data C) Adjusts weights D) Increases epochs 12 / 45 12) What does a pooling layer do in a CNN? A) Downsamples the input B) Applies filters C) Adds more neurons D) Increases dimensions 13 / 45 13) What is “transfer learning” useful for? A) Adjust weights B) Improve performance with limited data C) Increase complexity D) Simplify architecture 14 / 45 14) What does the term “weight” refer to in neural networks? A) Fixed values B) Constant outputs C) Random values D) Parameters adjusted during training 15 / 45 15) What type of data is best suited for recurrent neural networks (RNNs)? A) Sequential data B) Tabular data C) Structured data D) Image data 16 / 45 16) What does the term “early stopping” refer to? A) Increases learning rate B) Prevents overfitting C) Reduces training data D) Optimizes features 17 / 45 17) What is a common technique for initializing weights in deep networks? A) Zero initialization B) Xavier initialization C) Random initialization D) Uniform initialization 18 / 45 18) 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 19 / 45 19) Which activation function is most used in DL? A) Tanh B) ReLU C) Softmax D) Sigmoid 20 / 45 20) What is the primary goal of unsupervised learning? A) Predict outcomes B) Classify data C) Discover patterns D) Improve accuracy 21 / 45 21) Which optimization algorithm is known for its simplicity? A) Adam B) Stochastic Gradient Descent C) RMSprop D) Momentum 22 / 45 22) What is the importance of “feature scaling” in machine learning? A) Simplify architecture B) Improve training and convergence C) Adjust weights D) Increase complexity 23 / 45 23) What is the purpose of the output layer in a neural network? A) Adjust weights B) Increase complexity C) Normalize outputs D) Produce final predictions 24 / 45 24) What is an epoch in deep learning? A) Weight update step B) A single iteration C) Backpropagation step D) One complete pass 25 / 45 25) What is the main advantage of using deeper networks in deep learning? A) Capture complex patterns B) Simplify architecture C) Normalize outputs D) Reduce training time 26 / 45 26) What does “L1 regularization” help prevent? A) Prevent overfitting B) Simplify architecture C) Increase complexity D) Normalize outputs 27 / 45 27) What is the primary benefit of using a convolutional layer in a CNN? A) Simplify architecture B) Adjust weights C) Capture spatial hierarchies D) Increase complexity 28 / 45 28) What does “backpropagation” do in a neural network? A) Computes gradients B) Normalizes outputs C) Updates weights D) Adjusts learning rates 29 / 45 29) What is a common use of “autoencoders”? A) Data generation B) Feature extraction C) Dimensionality reduction D) Image classification 30 / 45 30) Which layer is primarily responsible for feature extraction in CNNs? A) Convolutional layer B) Fully connected layer C) Dropout layer D) Pooling layer 31 / 45 31) What does “transfer learning” allow a model to do? A) Simplify architecture B) Increase complexity C) Leverage pre-trained knowledge D) Train from scratch 32 / 45 32) What is the function of the learning rate in DL optimization? A) Controls step size B) Reduces bias C) Adjusts weights D) Regulates loss 33 / 45 33) Which layer is used to reduce the spatial dimensions of an image in a CNN? A) Pooling layers B) Convolutional layers C) Activation layers D) Fully connected layers 34 / 45 34) What is the primary goal of reinforcement learning? A) Maximize cumulative rewards B) Minimize errors C) Normalize outputs D) Simplify architecture 35 / 45 35) What is the role of the “input layer” in a neural network? A) Normalize outputs B) Increase complexity C) Adjust weights D) Receive input data 36 / 45 36) What does “vanishing gradients” refer to? A) Gradients become very small B) Reduced data C) Adjusted weights D) Increased training time 37 / 45 37) What is the role of the learning rate in training deep learning models? A) Control weight updates B) Adjust batch size C) Normalize data D) Simplify architecture 38 / 45 38) What does “gradient descent” refer to in training a model? A) Regularization technique B) Optimization algorithm C) Learning rate adjustment D) Normalization method 39 / 45 39) Which type of architecture is known for parallel processing in NLP? A) RNNs B) Transformers C) GANs D) CNNs 40 / 45 40) What is the role of the softmax function? A) Applies dropout B) Normalizes data C) Increases accuracy D) Converts scores to probabilities 41 / 45 41) Which deep learning technique is used for anomaly detection? A) RNNs B) GANs C) CNNs D) Autoencoders 42 / 45 42) What is a common loss function for binary classification tasks? A) Mean squared error B) Categorical cross-entropy C) Hinge loss D) Binary cross-entropy 43 / 45 43) Which layer in a neural network is typically responsible for feature extraction? A) Pooling layers B) Convolutional layers C) Output layers D) Fully connected layers 44 / 45 44) What does “sequence-to-sequence” learning typically involve? A) Reducing dimensions B) Mapping input to output sequences C) Adjusting learning rates D) Normalizing data 45 / 45 45) What is the primary advantage of using an LSTM network? A) Normalize outputs B) Capture long-term dependencies C) Increase complexity D) Simplify architecture 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 13Share on WhatsApp6Share on LinkedIn4Share on YouTube9Share on Facebook Comments 0
