About this Study Set
This study set covers Data Science through
20 practice questions.
A challenging collection of factual questions covering algorithms, statistical theory, and historical data science milestones. Every question includes the correct answer so you can learn as you go — pick any format above to get started.
Questions & Answers
Browse all 20 questions from the
Advanced Data Science Fundamentals study set below.
Each question shows the correct answer — select a study format above to practice interactively.
1
Which statistical method is specifically designed to handle multicollinearity by adding a penalty proportional to the square of the magnitude of coefficients?
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A
Lasso Regression
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B
Ridge Regression
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C
Elastic Net
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D
Principal Component Regression
2
In the context of the Bias-Variance tradeoff, what does the term 'irreducible error' represent?
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A
The error remaining after model training
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B
The variance of the training dataset
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C
The noise inherent in the system
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D
The bias of the chosen algorithm
3
Who is credited with inventing the term 'exploratory data analysis' in 1977?
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A
John Tukey
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B
Ronald Fisher
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C
George Box
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D
C.R. Rao
4
Which algorithm is defined as a non-parametric method used for classification and regression that relies on the 'majority vote' of its constituent decision trees?
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A
Gradient Boosting Machines
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B
Random Forest
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C
Support Vector Machines
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D
Naive Bayes
5
What is the primary mathematical objective of the Expectation-Maximization (EM) algorithm?
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A
Finding maximum likelihood estimates of parameters in latent variable models
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B
Minimizing the residual sum of squares in linear models
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C
Reducing the dimensionality of high-rank matrices
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D
Calculating the exact global minimum of non-convex functions
6
In reinforcement learning, what does the Bellman equation describe?
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A
The probability distribution of hidden states
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B
The recursive relationship between the value of a state and its successor states
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C
The gradient descent update rule for deep Q-networks
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D
The rate of decay for an exploration-exploitation strategy
7
What is the 'Curse of Dimensionality' primarily characterized by in high-dimensional feature spaces?
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A
Exponential increase in computational requirements for matrix inversion
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B
The volume of the space increases so rapidly that available data becomes sparse
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C
A breakdown in the central limit theorem
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D
The inability to calculate eigenvectors for sparse matrices
8
Which distribution is the conjugate prior to the Bernoulli distribution in Bayesian statistics?
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A
Normal distribution
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B
Poisson distribution
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C
Beta distribution
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D
Gamma distribution
9
What is the definition of a 'stationary process' in time series analysis?
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A
A process where the mean, variance, and autocorrelation structure are constant over time
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B
A process that has undergone de-trending and seasonal adjustment
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C
A process with a unit root
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D
A process that cannot be forecasted
10
The Vapnik-Chervonenkis (VC) dimension is a measure of what property of a statistical classification algorithm?
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A
Training time complexity
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B
Model capacity or complexity
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C
Convergence rate
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D
Number of hyper-parameters
11
Which specific technique is used to prevent overfitting by penalizing large weights in neural networks?
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A
Dropout
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B
Batch Normalization
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C
Weight Decay
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D
Gradient Clipping
12
In the context of information theory, what does the Kullback-Leibler (KL) divergence measure?
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A
The mutual information between two random variables
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B
The difference between two probability distributions
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C
The entropy of a single distribution
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D
The variance of a sample population
13
What is the primary advantage of using a 'Leaky ReLU' activation function over a standard ReLU?
-
A
It eliminates the vanishing gradient problem entirely
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B
It prevents the 'dying ReLU' problem by allowing a small gradient when the input is negative
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C
It is computationally cheaper to compute the derivative
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D
It ensures the output is normalized between 0 and 1
14
Which algorithm is most commonly associated with the solution to the 'Traveling Salesperson Problem' using biological inspiration?
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A
Ant Colony Optimization
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B
K-Nearest Neighbors
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C
Apriori Algorithm
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D
Hidden Markov Models
15
In SQL, what is the fundamental difference between a 'LEFT JOIN' and an 'INNER JOIN' when the right table has no matching rows?
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A
The LEFT JOIN returns NULL for the right side, while INNER JOIN excludes the row
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B
They are functionally identical in modern databases
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C
LEFT JOIN duplicates the row, INNER JOIN deletes it
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D
INNER JOIN returns NULL, LEFT JOIN returns 0
16
Which theorem states that the sum of a large number of independent and identically distributed random variables tends toward a normal distribution?
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A
Law of Large Numbers
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B
Central Limit Theorem
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C
Bayes' Theorem
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D
Slutsky's Theorem
17
What is the 'Manhattan Distance' between two points (x1, y1) and (x2, y2) mathematically equivalent to?
-
A
The Euclidean distance squared
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B
The L1 norm of the difference vector
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C
The L2 norm of the difference vector
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D
The cosine similarity
18
What does the 'Support' metric represent in Association Rule Learning (e.g., in the Apriori algorithm)?
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A
The confidence of an implication
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B
The frequency of an itemset in the dataset
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C
The lift ratio relative to independent occurrence
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D
The error rate of the classification
19
Which dimensionality reduction technique is based on identifying orthogonal axes that maximize the variance of the projected data?
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A
Independent Component Analysis
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B
Principal Component Analysis
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C
Factor Analysis
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D
Multidimensional Scaling
20
In Python's Scikit-learn, what is the default solver used for Logistic Regression for small datasets?
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A
liblinear
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B
sag
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C
lbfgs
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D
newton-cg
