MCQs

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Overall MCQs for Reference

1. Which type of padding does not add any extra data to the input in a convolutional neural network?
   a. Valid Padding
   b. Same Padding
   c. Zero Padding
   d. Full Padding
   Answer: a. Valid Paddingdeepai+2​
2. What is the main purpose of padding in convolutional neural networks?
   a. Increase output size
   b. Maintain spatial dimensions
   c. Reduce memory usage
   d. Improve randomization
   Answer: b. Maintain spatial dimensionsbaeldung+2​
3. Same padding ensures that the output feature map size is:
   a. Smaller than input
   b. Equal to input
   c. Larger than input
   d. Zero
   Answer: b. Equal to inputstudocu+2​
4. Zero padding in neural networks refers to:
   a. Adding ones around the input
   b. Removing borders
   c. Adding zeros around the input
   d. Multiplying input by zero
   Answer: c. Adding zeros around the inputgeeksforgeeks+1​
5. Which padding technique leads to a reduction in output feature map size?
   a. Same Padding
   b. Random Padding
   c. Full Padding
   d. Valid Padding
   Answer: d. Valid Paddingdeepai+2​
6. What type of padding is often used in time series and sequential models?
   a. Same Padding
   b. Reflection Padding
   c. Causal Padding
   d. Full Padding
   Answer: c. Causal Paddingbaeldung​
7. Reflection padding works by:
   a. Adding zeros
   b. Replicating the input
   c. Reflecting input along borders
   d. Removing noise
   Answer: c. Reflecting input along bordersgeeksforgeeks​
8. Replication padding involves:
   a. Duplicating border pixels
   b. Adding noise
   c. Removing data
   d. Upsampling
   Answer: a. Duplicating border pixelsgeeksforgeeks​
9. When using valid padding, the filter covers:
   a. All pixels including borders
   b. Only the valid parts
   c. Only the central pixels
   d. Only the top rows
   Answer: b. Only the valid partsbaeldung​
10. Why is zero padding commonly used?
    a. Increases computation time
    b. Reduces border information loss
    c. Decreases model accuracy
    d. Increases output size
    Answer: b. Reduces border information lossgeeksforgeeks​
11. Same padding is preferable when designing:
    a. Shallow neural networks
    b. Deep neural network architectures
    c. Recurrent neural networks
    d. Linear regression models
    Answer: b. Deep neural network architecturesstudocu​
12. Valid padding is also known as:
    a. No padding
    b. Zero padding
    c. Replication padding
    d. Full padding
    Answer: a. No paddinggopenai+2​
13. Which padding technique will keep the feature map dimensions unchanged after convolution?
    a. Valid
    b. Same
    c. Full
    d. Random
    Answer: b. Samedeepai+1​
14. What is a downside of valid padding?
    a. Increases computational cost
    b. Information loss at borders
    c. Overfitting
    d. Bias in predictions
    Answer: b. Information loss at bordersgopenai​
15. Padding in machine learning helps to:
    a. Avoid overfitting
    b. Prevent loss of border information
    c. Enhance model nonlinearity
    d. Directly regularize weights
    Answer: b. Prevent loss of border informationgopenai+2​
16. Which of the following is NOT a commonly used padding type in CNN?
    a. Same
    b. Valid
    c. Odd
    d. Causal
    Answer: c. Oddstudocu+1​
17. What does ‘padding=valid’ mean in a convolution operation?
    a. Border values are padded
    b. Filter covers only valid overlaps
    c. Entire input is padded
    d. Output size matches input
    Answer: b. Filter covers only valid overlapsdeepai+1​
18. Padding in RNNs for sequences is typically applied to:
    a. The longest sequence
    b. The shortest sequence
    c. All sequences equally
    d. Only the central values
    Answer: a. The longest sequencebaeldung​
19. Which padding is useful for handling variable-length inputs in NLP tasks?
    a. Same
    b. Random
    c. Sequence padding
    d. Reflection
    Answer: c. Sequence paddingbaeldung​
20. The main padding used in image processing convolution is usually:
    a. Zero
    b. One
    c. Random
    d. Gaussian
    Answer: a. Zerogeeksforgeeks​
21. Which padding type is best suited for causal convolutions?
    a. Same
    b. Causal
    c. Valid
    d. Mirror
    Answer: b. Causalbaeldung​
22. In Keras, how do you specify ‘no padding’?
    a. padding=”full”
    b. padding=”zero”
    c. padding=”valid”
    d. padding=”same”
    Answer: c. padding=”valid”deepai​
23. What is the effect of padding on receptive field size?
    a. No change
    b. Reduces it
    c. Increases it
    d. Flattens it
    Answer: c. Increases itdeepai​
24. In pooling layers, padding is:
    a. Never used
    b. Used as needed
    c. Mandatory
    d. Always same padding
    Answer: b. Used as neededscribd​
25. What does padding do in data preprocessing for ML?
    a. Normalizes features
    b. Handles missing sequences
    c. Discretizes data
    d. Reduces dimensionality
    Answer: b. Handles missing sequencessaidatascience​
26. For 1D convolution, padding is typically applied to:
    a. Both sides of the sequence
    b. Only center
    c. Only top
    d. Only bottom
    Answer: a. Both sides of the sequencebaeldung​
27. In PyTorch, padding in Conv2d defaults to:
    a. Zero
    b. Random
    c. Average
    d. Reflect
    Answer: a. Zerogeeksforgeeks​
28. When using same padding, the number of zeros added is determined by:
    a. Filter size
    b. Learning rate
    c. Batch size
    d. Epochs
    Answer: a. Filter sizedeepai+1​
29. Padding can also help in:
    a. Reducing computation
    b. Handling imbalanced datasets
    c. Improving border predictions
    d. Removing noise
    Answer: c. Improving border predictionsgopenai​
30. The use of padding is least relevant for:
    a. Image data
    b. Sequence data
    c. Fully connected layers
    d. Audio signal processing
    Answer: c. Fully connected layersbaeldung​
31. In TensorFlow, to pad sequences of unequal length, which function is generally used?
    a. pad_sequences
    b. zero_pad
    c. same_pad
    d. fill_na
    Answer: a. pad_sequencesbaeldung​
32. Padding regulates how much:
    a. Model overfits
    b. Data is ignored at boundaries
    c. Data is normalized
    d. Feature importance changes
    Answer: b. Data is ignored at boundariesgeeksforgeeks+1​
33. If ‘padding=same’ in a convolution layer, output size will be:
    a. Same as input
    b. Double input
    c. Half input
    d. Random
    Answer: a. Same as inputstudocu+2​
34. What is the default padding value when padding numeric data?
    a. Zero
    b. Negative one
    c. One
    d. Infinity
    Answer: a. Zerogeeksforgeeks​
35. Padding in LSTM sequence models is important for:
    a. Making all sequences the same length
    b. Increasing feature space
    c. Adding nonlinearity
    d. Removing outliers
    Answer: a. Making all sequences the same lengthbaeldung​
36. Causal padding especially benefits:
    a. Image recognition
    b. Time series prediction
    c. Regression analysis
    d. Clustering
    Answer: b. Time series predictionbaeldung​
37. Reflection padding uses what technique?
    a. Repeating border pixels
    b. Mirroring input at borders
    c. Randomly inserting noise
    d. Removing border rows
    Answer: b. Mirroring input at bordersgeeksforgeeks​
38. Which padding would you use to prevent shortening of input sequence length in text processing models?
    a. Valid
    b. Same
    c. Sequence
    d. Full
    Answer: c. Sequencebaeldung​
39. The padding size in convolution can affect:
    a. Output accuracy
    b. Model loss
    c. Output size
    d. All of the above
    Answer: d. All of the abovegopenai+2​
40. Padding is mandatory when:
    a. Input has uneven dimensions
    b. Model is always deep
    c. All features are numeric
    d. Regularization is needed
    Answer: a. Input has uneven dimensionsbaeldung​
41. Which is NOT an effect of padding in neural networks?
    a. Preventing loss of border info
    b. Adjusting output size
    c. Increasing input sparsity
    d. Changing memory usage
    Answer: c. Increasing input sparsitydeepai+1​
42. Padding can help in avoiding:
    a. Overfitting
    b. Vanishing gradients
    c. Information loss at edges
    d. Weight initialization issues
    Answer: c. Information loss at edgesgopenai+1​
43. Which library supplies built-in functions for padding in ML data preprocessing?
    a. NumPy
    b. pandas
    c. TensorFlow
    d. All of the above
    Answer: d. All of the abovebaeldung​
44. For audio data, padding can be used to:
    a. Shorten signals
    b. Equalize length for batch processing
    c. Remove silence
    d. Optimize sample rate
    Answer: b. Equalize length for batch processingbaeldung​
45. What happens when padding is not used in CNN and input is too small?
    a. Output is incomplete
    b. Output is too large
    c. Output is perfectly valid
    d. Output overfits
    Answer: a. Output is incompletedeepai+1​
46. Which padding should be used for odd filter sizes to maintain dimension?
    a. Valid
    b. Same
    c. Full
    d. None
    Answer: b. Samestudocu+1​
47. What does the term ‘causal’ refer to in causal padding?
    a. Padding with future data
    b. Padding with past data
    c. Padding with noise
    d. Padding with zeros
    Answer: b. Padding with past databaeldung​
48. Which padding method is best for edge-sensitive image tasks?
    a. Reflection
    b. Random
    c. Valid
    d. None
    Answer: a. Reflectiongeeksforgeeks​
49. Which padding technique is most commonly used during deep learning model inference?
    a. Same
    b. Valid
    c. Reflection
    d. Replication
    Answer: a. Samedeepai+2​
50. Padding helps to:
    a. Maintain feature consistency
    b. Make data non-linear
    c. Normalize input
    d. Increase randomness
    Answer: a. Maintain feature consistencydeepai+2​

1. https://deepai.org/machine-learning-glossary-and-terms/padding
2. https://www.studocu.com/in/messages/question/12857295/list-and-explain-the-types-of-padding-commonly-used-inconvolutional-neural-networkscnns
3. https://www.baeldung.com/cs/deep-neural-networks-padding
4. https://www.geeksforgeeks.org/python/types-of-padding-in-convolution-layer/
5. https://blog.gopenai.com/padding-in-neural-networks-why-and-how-b076ab0a4fc2
6. https://www.scribd.com/document/488694104/Deep-Learning-MCQ
7. https://saidatascience.com/data-handling-and-preprocessing/
8. https://www.sanfoundry.com/1000-machine-learning-questions-answers/
9. https://www.gyansetu.in/blog/machine-learning-mcq-interview-preparation/
10. https://www.geeksforgeeks.org/quizzes/machine-learning-quiz-questions-and-answers/
11. https://www.igmguru.com/blog/machine-learning-mcqs
12. https://www.interviewbit.com/machine-learning-mcq/
13. https://www.youtube.com/watch?v=_zQvHMXzwC4
14. https://www.placementpreparation.io/mcq/machine-learning/
15. https://www.sanfoundry.com/data-mining-questions-answers-data-preprocessing/
16. https://futureskillsacademy.com/blog/top-machine-learning-mcqs-with-answers/
17. https://www.examveda.com/data-science/practice-mcq-question-on-data-collection-and-preprocessing/?page=3
18. https://www.aionlinecourse.com/ai-quiz-questions/machine-learning/deep-learning-ml
19. https://gtu-mcq.com/BE/Computer-Engineering/Semester-6/3160714/5457/MCQs?q=bDFvcmdllQA%3D
20. https://interviewkickstart.com/blogs/articles/machine-learning-mcqs-answers