{"description":"Evaluate your understanding of how to prepare and structure datasets for effective training of deep learning models.","questions":[{"answer":"It improves model accuracy and generalization","number":1,"options":["It reduces model parameters","It ensures that neural networks can connect to the internet","It improves model accuracy and generalization","It prevents hardware damage"],"question":"Why is data preparation critical in deep learning?"},{"answer":"Scaling or normalizing input features","number":2,"options":["Deploying the model","Scaling or normalizing input features","Running inference","Encrypting the dataset"],"question":"Which of the following is a typical first step in data preprocessing?"},{"answer":"Rank-2 tensor (2D array)","number":3,"options":["Plain text","Rank-2 tensor (2D array)","Scalar values only","Audio files"],"question":"What kind of data format do deep learning models typically expect for input features?"},{"answer":"The expected output or class","number":4,"options":["A metric to evaluate training speed","An ID for each training example","The expected output or class","A system process name"],"question":"What does a label represent in supervised learning tasks?"},{"answer":"A continuous numerical value","number":5,"options":["A class category","A string identifier","A continuous numerical value","A Boolean decision"],"question":"In regression tasks, what kind of value is typically predicted?"},{"answer":"Classification predicts discrete labels; regression predicts continuous values","number":6,"options":["Classification uses more layers","Regression tasks always require image inputs","Classification predicts discrete labels; regression predicts continuous values","Regression does not require training"],"question":"What is one key difference between classification and regression tasks?"},{"answer":"It helps the model generalize to unseen data","number":7,"options":["It allows for higher compression rates","It helps the model generalize to unseen data","It enables hardware optimization","It speeds up the GPU"],"question":"Why is having a diverse dataset important for deep learning?"},{"answer":"The width of a flower petal in cm","number":8,"options":["The model's learning rate","The name of the algorithm","The width of a flower petal in cm","The version of Python used"],"question":"Which of the following would be considered a feature in a dataset?"},{"answer":"Normalization","number":9,"options":["Feature labeling","Encoding","Normalization","Dimensionality increase"],"question":"What preprocessing step is commonly used to bring all input features to a similar scale?"},{"answer":"Decreased accuracy and poor generalization","number":10,"options":["Increased training speed","Improved model robustness","Decreased accuracy and poor generalization","Reduced number of epochs required"],"question":"What is a common effect of poor data quality in deep learning?"}],"title":"Data Preparation for Training Deep Learning Models"}