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 “batch size” refer to in the context of deep learning training? A) Number of layers B) Total dataset size C) Number of training examples D) Number of epochs 2 / 45 2) Which loss function is often used in regression tasks? A) Mean Absolute Error B) Hinge loss C) Binary Cross-Entropy D) Mean Squared Error 3 / 45 3) What does the term “embedding” refer to in deep learning? A) Weight adjustment B) Data normalization C) Training supervision D) Vector representations 4 / 45 4) What does “model evaluation” typically involve? A) Increase complexity B) Simplify architecture C) Assess performance metrics D) Adjust weights 5 / 45 5) What type of neural network is typically used for text generation? A) GANs B) Autoencoders C) CNNs D) RNNs or transformers 6 / 45 6) What is the function of the “softmax” layer in a neural network? A) Adjust weights B) Normalize outputs C) Increase complexity D) Convert scores into probabilities 7 / 45 7) What is a common application of recurrent neural networks? A) Feature extraction B) Image classification C) Data compression D) Language processing 8 / 45 8) What is a common use of recurrent neural networks (RNNs)? A) Image classification B) Sequential data tasks C) Data normalization D) Feature extraction 9 / 45 9) Which of the following architectures is known for its use in image segmentation? A) CNN B) U-Net C) RNN D) GAN 10 / 45 10) What does “overfitting” indicate in a deep learning model? A) Increase complexity B) Poor performance on unseen data C) Simplify architecture D) High training accuracy 11 / 45 11) What is a common use of “autoencoders”? A) Data generation B) Feature extraction C) Dimensionality reduction D) Image classification 12 / 45 12) What is the effect of using ReLU activation in a neural network? A) Reduces vanishing gradients B) Increases complexity C) Normalizes outputs D) Simplifies architecture 13 / 45 13) What is the purpose of using “ensemble methods” in deep learning? A) Improve performance B) Simplify architecture C) Adjust weights D) Increase complexity 14 / 45 14) What is the role of attention mechanisms in deep learning? A) Focus on specific parts B) Normalize outputs C) Increase complexity D) Adjust weights 15 / 45 15) 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 16 / 45 16) What is the purpose of the “hidden layers” in a neural network? A) Transform inputs to outputs B) Simplify architecture C) Increase complexity D) Adjust weights 17 / 45 17) What is the primary function of the output layer in a neural network? A) Adjust weights B) Simplify architecture C) Normalize outputs D) Generate final predictions 18 / 45 18) What does the “learning rate” control in the training of a neural network? A) Change model response B) Increase model complexity C) Simplify architecture D) Adjust the model size 19 / 45 19) What is the purpose of the output layer in a neural network? A) Produces final output B) Normalizes inputs C) Reduces dimensions D) Adjusts weights 20 / 45 20) Which of the following is a common performance metric for classification models? A) Recall B) Precision C) Accuracy D) F1 Score 21 / 45 21) Which deep learning technique is used for anomaly detection? A) RNNs B) GANs C) CNNs D) Autoencoders 22 / 45 22) What does “data preprocessing” involve in deep learning? A) Simplify architecture B) Increase complexity C) Adjust weights D) Clean and transform data 23 / 45 23) Which deep learning technique is often used for speech recognition? A) RNNs B) CNNs C) GANs D) Autoencoders 24 / 45 24) Which architecture is suitable for sequence-to-sequence tasks? A) GAN B) CNN C) Autoencoder D) Encoder-decoder 25 / 45 25) Which model is typically used for time series prediction? A) Autoencoders B) RNNs C) CNNs D) GANs 26 / 45 26) Which of the following is a feature of the ReLU activation function? A) Adjusts weights B) Increases complexity C) Introduces non-linearity D) Normalizes outputs 27 / 45 27) What is the primary purpose of the encoder in a seq2seq model? A) Encode input sequences B) Decode output sequences C) Generate new sequences D) Normalize data 28 / 45 28) Which of the following techniques can be used for feature selection in deep learning? A) Increase complexity B) L1 regularization C) Simplify architecture D) Adjust weights 29 / 45 29) What does the term “overfitting” mean in deep learning? A) Good on training, poor on unseen B) Complex architecture C) Reduced dimensions D) Fast training 30 / 45 30) What is the main characteristic of a U-Net architecture? A) Adjust weights B) Increase complexity C) Simplify architecture D) Symmetric structure 31 / 45 31) What is the function of the gradient in training a neural network? A) Indicates weight adjustment B) Measures model accuracy C) Optimizes features D) Increases learning 32 / 45 32) What is the purpose of using a validation dataset? A) Fine-tune model parameters B) Increase complexity C) Simplify architecture D) Adjust weights 33 / 45 33) What does the term “feature extraction” refer to in deep learning? A) Identifying relevant features B) Simplifying architecture C) Reducing dimensions D) Increasing complexity 34 / 45 34) What is a common use case for reinforcement learning? A) Feature extraction B) Robotics and gaming C) Data normalization D) Image classification 35 / 45 35) Which of the following is a characteristic of deep learning models? A) Faster training B) Requires large labeled data C) Lower complexity D) Simpler architecture 36 / 45 36) Which of the following optimizers is adaptive? A) Adagrad B) Adam C) RMSProp D) SGD 37 / 45 37) What is the function of a pooling layer in CNNs? A) Increase complexity B) Reduce spatial size C) Normalize outputs D) Simplify architecture 38 / 45 38) Which technique is often used for visualizing deep learning model performance? A) Increase training speed B) Confusion matrices C) Reduce dimensions D) Normalize data 39 / 45 39) Which optimization method uses momentum to accelerate training? A) RMSprop B) Momentum C) SGD D) Adam 40 / 45 40) What does “gradient descent” help to achieve in deep learning? A) Normalize outputs B) Increase complexity C) Reduce data D) Minimize loss function 41 / 45 41) What is a common challenge when training deep learning models on limited data? A) Underfitting B) Reduced complexity C) Overfitting D) Faster training 42 / 45 42) What does the term “overfitting” refer to in machine learning? A) Underfitting B) Simplifying architecture C) Learning noise D) Generalizing 43 / 45 43) What is a common technique to enhance model robustness? A) Increase epochs B) Reduce data C) Simplify architecture D) Data augmentation and regularization 44 / 45 44) What does “transfer learning” allow a model to do? A) Reduce data B) Adapt to new tasks C) Simplify architecture D) Train from scratch 45 / 45 45) What is the significance of using a learning rate decay? A) Increases complexity B) Normalizes outputs C) Reduces dimensions D) Improves convergence 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 16Share on WhatsApp10Share on LinkedIn5Share on YouTube9Share on Facebook Comments 0
