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Online Technical Test _ Amadeus India

Quiz by Human Resources

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30 questions
Show answers
  • Q1
    In Deep Learning Power BI with Neural Networks, what is the process of training a neural network using a large dataset to learn the weights and biases that map inputs to outputs?
    Gradient descent
    Forward propagation
    Backpropagation
    Overfitting
    30s
  • Q2
    Which activation function is commonly used in the hidden layers of neural networks in Deep Learning Power BI?
    ReLU (Rectified Linear Unit)
    Sigmoid
    Leaky ReLU
    Tanh
    30s
  • Q3
    What is the purpose of using dropout regularization in neural networks for Deep Learning Power BI?
    To improve accuracy
    To speed up training
    To prevent overfitting
    To increase model complexity
    30s
  • Q4
    What is a common technique used to preprocess input data before feeding it into a neural network in Deep Learning Power BI?
    Feature selection
    Regularization
    Normalization
    One-hot encoding
    30s
  • Q5
    What is the role of the output layer in a neural network for Deep Learning Power BI?
    To adjust the weights
    To perform feature extraction
    To produce the final predictions or outputs
    To apply activation functions
    30s
  • Q6
    What is the importance of hyperparameter tuning in optimizing the performance of neural networks in Deep Learning Power BI?
    To train the neural network
    To visualize the data
    To find the best set of hyperparameters that improve model performance
    To preprocess input data
    30s
  • Q7
    What is the main advantage of using neural networks for deep learning in Power BI compared to traditional machine learning algorithms?
    Faster training speed
    Lower computational resources
    Ability to learn complex patterns in large datasets
    Higher interpretability
    30s
  • Q8
    What is the purpose of the activation function in a neural network for Deep Learning Power BI?
    To control the learning rate of the network
    To introduce non-linearity and enable the network to learn complex patterns
    To initialize the weights in the network
    To reduce the number of neurons in the network
    30s
  • Q9
    What is the purpose of using cross-validation in evaluating the performance of a neural network in Deep Learning Power BI?
    To assess the generalization ability of the model and prevent overfitting
    To increase the complexity of the model
    To speed up the training process
    To select the optimal hyperparameters
    30s
  • Q10
    What is the role of the loss function in training a neural network for Deep Learning Power BI?
    To adjust the learning rate during training
    To measure the error between the predicted outputs and the actual targets
    To reduce the number of hidden layers in the network
    To initialize the weights of the network
    30s
  • Q11
    Which activation function is commonly used in Deep Learning Power BI with Neural Networks?
    Softmax
    Tanh
    Sigmoid
    ReLU (Rectified Linear Activation)
    30s
  • Q12
    What is the purpose of using Neural Networks in Power BI for Deep Learning?
    To display simple visualizations
    To organize data in tables
    To perform complex data analysis and make accurate predictions
    To create basic charts
    30s
  • Q13
    Which step is essential before training a Neural Network in Power BI for Deep Learning?
    Adding more layers to the network
    Data preprocessing and cleaning
    Increasing the learning rate
    Randomly initializing weights
    30s
  • Q14
    In the context of Deep Learning Power BI with Neural Networks, what is overfitting?
    When a model has perfect accuracy on all data
    When a model performs uniformly poorly on all data
    When a model performs well on training data but poorly on unseen data
    When a model is underfitting the training data
    30s
  • Q15
    What is the purpose of using dropout in Neural Networks for Deep Learning in Power BI?
    To ensure every neuron is utilized
    To increase the model complexity
    To prevent overfitting by randomly dropping neurons during training
    To speed up the training process
    30s

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