MCQs

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Overall MCQs for Reference

Which library is most commonly used for numerical computation in Python-based AI development?
a. Pandas
b. NumPy
c. Matplotlib
d. NLTK
Answer: b. NumPyupgrad+1
What is the main purpose of gradient descent in AI numerical programming?
a. Data visualization
b. Model optimization
c. Clustering
d. Data cleaning
Answer: b. Model optimizationdev+1
Which mathematical technique is crucial for reducing the dimensionality of datasets in AI?
a. Linear regression
b. K-means clustering
c. Principal Component Analysis
d. Decision Trees
Answer: c. Principal Component Analysisdev
Which function in NumPy is used to compute the mean of an array?
a. np.sum()
b. np.mean()
c. np.average()
d. np.max()
Answer: b. np.mean()dev
Which method estimates the area under a curve using discrete points in numerical integration?
a. Gradient descent
b. K-means
c. Trapezoidal rule
d. Principal components
Answer: c. Trapezoidal ruledev
What is the primary advantage of using vectorized operations in numerical programming?
a. Reduced readability
b. Slower performance
c. Enhanced performance
d. Increased memory usage
Answer: c. Enhanced performancehackr+1
Which method is applied for numerical differentiation in AI computations?
a. Central difference
b. Binary search
c. Random forests
d. Naive Bayes
Answer: a. Central differencedev
How does overfitting occur in numerical programming for AI?
a. Model is too simple
b. Model ignores data
c. Model fits noise
d. Model underestimates variance
Answer: c. Model fits noiseemtiyaz.github+1
What is the purpose of regularization in machine learning algorithms?
a. Increase accuracy
b. Penalize complex models
c. Reduce data size
d. Speed up convergence
Answer: b. Penalize complex modelsemtiyaz.github
Which operation does np.dot() perform in NumPy?
a. Addition
b. Subtraction
c. Matrix multiplication
d. Logical comparison
Answer: c. Matrix multiplicationhackr+1
The Singular Value Decomposition (SVD) is primarily used for:
a. Classification
b. Matrix factorization
c. Regression
d. Visualization
Answer: b. Matrix factorizationdigitalregenesys+1
In optimization algorithms, what does the learning rate control?
a. Number of features
b. Step size in updates
c. Number of outputs
d. Accuracy of algorithm
Answer: b. Step size in updatesdigitalregenesys+1
Which method is commonly used for feature scaling in numerical data preprocessing?
a. Min-max normalization
b. Bagging
c. Boosting
d. Cross-validation
Answer: a. Min-max normalizationdev
What is the role of eigenvalues in numerical programming?
a. Measure variance
b. Rank features
c. Data normalization
d. Identify directions of maximum variance
Answer: d. Identify directions of maximum variancedigitalregenesys+1
When is k-means clustering used in AI?
a. To solve classification problems
b. To group similar data points
c. For outlier detection only
d. For time series forecasting
Answer: b. To group similar data pointsdev
What does the np.random.randn() function output?
a. Uniform random numbers
b. Integers
c. Standard normal distributed numbers
d. Binary values
Answer: c. Standard normal distributed numbershackr+1
Which numerical method solves optimization by moving towards the steepest descent?
a. Gradient Descent
b. Genetic algorithms
c. Random Forests
d. PCA
Answer: a. Gradient Descentgeeksforgeeks+1
In neural networks, what is backpropagation primarily responsible for?
a. Model initialization
b. Parameter tuning
c. Hyperparameter search
d. Data splitting
Answer: b. Parameter tuninginterviewkickstart+1
What is a consequence of a very high learning rate in gradient-based algorithms?
a. Faster convergence
b. Oscillation and divergence
c. More accurate convergence
d. Better generalization
Answer: b. Oscillation and divergencedev
Which of these is NOT a numerical programming task in AI?
a. Numerical integration
b. Data encoding
c. Function approximation
d. Matrix inversion
Answer: b. Data encodingdonaldlab.duke+1
Which language is most popular for implementing numerical algorithms in AI?
a. Python
b. JavaScript
c. Assembly
d. COBOL
Answer: a. Pythondatacamp+1
What is stochastic gradient descent (SGD) used for in AI?
a. Batch data loading
b. Model hyperparameter optimization
c. Faster, online optimization
d. Data encryption
Answer: c. Faster, online optimizationgeeksforgeeks+1
Which AI algorithm benefits greatly from efficient matrix operations?
a. Naive Bayes
b. Decision Trees
c. Deep learning neural networks
d. K-nearest neighbors
Answer: c. Deep learning neural networksdigitalregenesys+1
What is the output shape when multiplying a 2×3 matrix by a 3×4 matrix?
a. 2×3
b. 3×2
c. 2×4
d. 4×3
Answer: c. 2×4dev
Which loss function is commonly minimized in linear regression?
a. Cross-entropy
b. Hinge loss
c. Mean squared error
d. Log-likelihood
Answer: c. Mean squared errorinterviewkickstart+1
What does SVD decompose a matrix into?
a. Three matrices (U, S, V^T)
b. Two matrices (W, H)
c. One matrix
d. Four matrices
Answer: a. Three matrices (U, S, V^T)digitalregenesys+1
What is the function of numerical interpolation in AI?
a. Classifying objects
b. Estimating missing values
c. Reducing model complexity
d. Improving visualization
Answer: b. Estimating missing valuesdev
Which AI area heavily relies on numerical integration methods?
a. NLP
b. Reinforcement Learning
c. Computer Vision
d. Game Theory
Answer: b. Reinforcement Learninggeeksforgeeks+1
Linear programming methods in AI help with:
a. Image recognition
b. Optimizing objective functions
c. Data encryption
d. Data cleaning
Answer: b. Optimizing objective functionsgeeksforgeeks
What is the output type of np.linalg.eig() function?
a. Scalar value
b. List of lists
c. Eigenvalues and eigenvectors
d. Boolean array
Answer: c. Eigenvalues and eigenvectorshackr+1
Which is NOT an advantage of vectorized numerical operations?
a. Increased performance
b. Lower memory usage
c. Simpler syntax
d. Slower execution
Answer: d. Slower executionhackr+1
What is the main reason for data normalization in AI numerical programming?
a. Increase model complexity
b. Avoid bias due to scale differences
c. Reduce sample size
d. Improve clustering speed
Answer: b. Avoid bias due to scale differencesdev
In which situation should regularization NOT be increased in regression?
a. Overfitting is present
b. Model is underfitting
c. Large number of features
d. High variance in output
Answer: b. Model is underfittingemtiyaz.github
In a 1D array, what does np.std() compute?
a. Maximum value
b. Minimum value
c. Standard deviation
d. Mode
Answer: c. Standard deviationdev
Which of the following is a method for dimensionality reduction in AI?
a. k-means
b. PCA
c. Bagging
d. Decision Trees
Answer: b. PCAdev
What type of AI programming is the Newton’s Method associated with?
a. Symbolic computation
b. Numerical optimization
c. Rule-based systems
d. Logical inference
Answer: b. Numerical optimizationgeeksforgeeks
Which function in NumPy would you use to create an array of random integers?
a. np.arange()
b. np.random.randint()
c. np.linspace()
d. np.arange()
Answer: b. np.random.randint()hackr+1
What mathematical structure forms the backbone of linear algebra operations in AI?
a. Graphs
b. Arrays
c. Matrices
d. Trees
Answer: c. Matricesdigitalregenesys+1
Which iterative algorithm improves a model by updating parameters using derivatives?
a. Gradient Descent
b. Random Forest
c. K-means
d. PCA
Answer: a. Gradient Descentgeeksforgeeks+1
What is the purpose of the sigmoid activation function?
a. Normalize outputs between 0 and 1
b. Reduce model dimensionality
c. Eliminate non-linearity
d. Decompose matrices
Answer: a. Normalize outputs between 0 and 1interviewkickstart+1
What is the primary data structure used in NumPy for numerical programming?
a. Tensor
b. Matrix
c. Array
d. DataFrame
Answer: c. Arrayhackr+1
Which method is commonly used for solving systems of linear equations in AI?
a. Decision Trees
b. Gaussian elimination
c. Cross validation
d. Pruning
Answer: b. Gaussian eliminationdigitalregenesys+1
Why is batch normalization used in deep networks?
a. To increase overfitting
b. To normalize layer inputs
c. To increase randomness
d. To enhance linearity
Answer: b. To normalize layer inputsdigitalregenesys
In data processing, which library is NOT a numerical programming library in Python?
a. NumPy
b. Pandas
c. Seaborn
d. SciPy
Answer: c. Seabornupgrad+1
Which numerical method is best for finding the root of a continuous function?
a. Sigmoid
b. Newton-Raphson
c. Bagging
d. Cross entropy
Answer: b. Newton-Raphsongeeksforgeeks+1
What is the output of multiplying a matrix by its identity matrix?
a. Null matrix
b. The same matrix
c. A matrix of ones
d. Diagonal matrix
Answer: b. The same matrixdigitalregenesys+1
Which technique combines results from several weak learners to form a strong learner in AI?
a. PCA
b. Bagging
c. Ensemble averaging
d. Regularization
Answer: c. Ensemble averagingdatacamp
What function is used to compute eigenvectors in NumPy?
a. np.linalg.inv()
b. np.linalg.eig()
c. np.sum()
d. np.shape()
Answer: b. np.linalg.eig()hackr+1
Which helps prevent overflow or underflow during backpropagation in neural networks?
a. Softmax activation
b. Batch normalization
c. ReLU activation
d. Data encoding
Answer: b. Batch normalizationdigitalregenesys
The trapezoidal rule is used for which numerical programming task?
a. Sorting arrays
b. Numerical integration
c. K-means clustering
d. Model parameter tuning
Answer: b. Numerical integrationdonaldlab.duke+1

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