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 is the role of “feature extraction” in deep learning? A) Simplify architecture B) Adjust weights C) Identify important attributes D) Increase complexity 2 / 45 2) What is the main purpose of using a learning rate scheduler? A) Normalize outputs B) Increase complexity C) Adjust learning rate D) Simplify architecture 3 / 45 3) What is the main role of the “output layer” in a neural network? A) Simplify architecture B) Adjust weights C) Increase complexity D) Produce final predictions 4 / 45 4) What is the purpose of an optimizer in deep learning? A) Adjusts weights B) Improves accuracy C) Adds regularization D) Controls overfitting 5 / 45 5) What is the purpose of a validation dataset? A) Optimize architecture B) Adjust learning rate C) Tune hyperparameters D) Increase training data 6 / 45 6) What does the term “gradient clipping” refer to? A) Increase complexity B) Adjust learning rates C) Simplify architecture D) Limit maximum gradients 7 / 45 7) What is the role of the bias term in a neural network? A) Normalize outputs B) Increase complexity C) Shift activation function D) Adjust learning rates 8 / 45 8) Which technique is often used for visualizing deep learning model performance? A) Increase training speed B) Confusion matrices C) Reduce dimensions D) Normalize data 9 / 45 9) Which framework is widely used for building deep learning models? A) TensorFlow and PyTorch B) Keras C) MXNet D) Scikit-learn 10 / 45 10) What is the function of a pooling layer in CNNs? A) Normalize outputs B) Increase complexity C) Adjust weights D) Reduce spatial dimensions 11 / 45 11) What does LSTM stand for in deep learning? A) Long Short-Term Memory B) Learning Sequence Task Method C) Local Sequential Task Model D) Least Square Tensor Model 12 / 45 12) What is the effect of using a high learning rate? A) Slow convergence B) Simplify architecture C) Overshoot minima D) Increase accuracy 13 / 45 13) What is the function of a batch normalization layer? A) Reduces dimensions B) Adjusts weights C) Increases complexity D) Normalizes inputs 14 / 45 14) In which scenario would you use an autoencoder? A) Optimize features B) Adjust weights C) Dimensionality reduction D) Increase complexity 15 / 45 15) What does “transfer learning” enable in deep learning? A) Reuse pre-trained model B) Adjust weights C) Full re-training D) Increase model complexity 16 / 45 16) What does the term “backpropagation” refer to? A) Increase complexity B) Simplify architecture C) Compute gradients D) Adjust weights 17 / 45 17) What is a common use case for reinforcement learning? A) Feature extraction B) Robotics and gaming C) Data normalization D) Image classification 18 / 45 18) 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 19 / 45 19) Which activation function is often used in the output layer for multi-class classification? A) Tanh B) ReLU C) Sigmoid D) Softmax 20 / 45 20) What does “multi-task learning” refer to in deep learning? A) Perform multiple tasks B) Simplify architecture C) Adjust weights D) Increase complexity 21 / 45 21) What is a common use case for CNNs? A) Text classification B) Anomaly detection C) Time-series forecasting D) Image processing 22 / 45 22) What is a common reason for using a pretrained model? A) Simplify architecture B) Increase complexity C) Save time and resources D) Train from scratch 23 / 45 23) Which method is commonly used to visualize the training process of a model? A) Seaborn B) TensorBoard C) Plotly D) Matplotlib 24 / 45 24) What is the function of a pooling layer in CNNs? A) Increase complexity B) Reduce spatial size C) Normalize outputs D) Simplify architecture 25 / 45 25) What is the goal of using early stopping during training? A) Adjust weights B) Normalize inputs C) Prevent overfitting D) Increase complexity 26 / 45 26) 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 27 / 45 27) What is a key benefit of using ensemble methods in deep learning? A) Adjust learning rates B) Improve performance C) Reduce complexity D) Simplify architecture 28 / 45 28) What does “weight decay” refer to in regularization? A) Simplify architecture B) Increase complexity C) Normalize outputs D) Penalty for large weights 29 / 45 29) What is one drawback of using very deep neural networks? A) Increases training time B) Enhances performance C) Prone to overfitting D) Improves interpretability 30 / 45 30) What does the term “overfitting” refer to in machine learning? A) Underfitting B) Simplifying architecture C) Learning noise D) Generalizing 31 / 45 31) What does the term “fine-tuning” refer to in deep learning? A) Adjust pre-trained models B) Simplify architecture C) Normalize outputs D) Train from scratch 32 / 45 32) Which metric is used to evaluate the performance of a classification model? A) Recall B) Precision C) F1 Score D) Accuracy 33 / 45 33) What is an epoch in deep learning? A) Weight update step B) A single iteration C) Backpropagation step D) One complete pass 34 / 45 34) Which of the following is a common optimization algorithm for training deep learning models? A) SGD B) RMSProp C) Adagrad D) Adam 35 / 45 35) What is the purpose of the Softmax function? A) Optimizes weights B) Increases loss C) Converts logits to probabilities D) Reduces overfitting 36 / 45 36) Which layer is primarily responsible for feature extraction in CNNs? A) Convolutional layer B) Fully connected layer C) Dropout layer D) Pooling layer 37 / 45 37) What is the role of the optimizer in a neural network? A) Reduces training time B) Simplifies calculations C) Increases accuracy D) Adjusts weights 38 / 45 38) What does the term “attention mechanism” refer to? A) Focuses on input parts B) Adjusts weights C) Reduces training time D) Increases complexity 39 / 45 39) What is a primary feature of transformers in deep learning? A) Self-attention mechanisms B) Feature extraction C) Data normalization D) Sequential processing 40 / 45 40) Which of the following is a common performance metric for classification models? A) Recall B) Precision C) Accuracy D) F1 Score 41 / 45 41) What does “early stopping” prevent during training? A) Model complexity B) Overfitting C) Underfitting D) Gradient issues 42 / 45 42) What does the term “hyperparameter” refer to? A) Adjusted during training B) Control learning process C) Learned from data D) Normalized outputs 43 / 45 43) What does “data leakage” refer to in model evaluation? A) Overfitting B) Adjust weights C) Information from test set used D) Simplify architecture 44 / 45 44) What is the role of embeddings in deep learning? A) Reduce dimensions B) Generate random features C) Convert categorical data D) Increase noise 45 / 45 45) What is the goal of using a validation dataset during training? A) Simplify architecture B) Adjust weights C) Increase complexity D) Assess performance 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
