MCQs

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Overall MCQs for Reference

What is the main use of Bayes Theorem in machine learning?
a. Classification
b. Regression
c. Clustering
d. Dimensionality Reduction
Answer: a. Classificationappliedaicourse+2
Bayes Theorem updates probabilities based on what?
a. Frequency of occurrence
b. No prior information
c. New evidence
d. Random selection
Answer: c. New evidenceupgrad+2
The formula for Bayes Theorem is:
a.
b.
c.
d.
Answer: a. geeksforgeeks+2
In Bayes Theorem, what does represent?
a. Posterior probability
b. Prior probability
c. Evidence probability
d. Marginal likelihood
Answer: b. Prior probabilitygeeksforgeeks+1
In the context of Bayes Theorem, is called:
a. Likelihood
b. Prior
c. Posterior
d. Marginal
Answer: a. Likelihoodupgrad+1
What is the posterior probability in Bayes Theorem?
a.
b.
c.
d.
Answer: c. geeksforgeeks+1
Which algorithm is grounded on Bayes Theorem for classification?
a. Naive Bayes
b. Decision Trees
c. K-Means
d. SVM
Answer: a. Naive Bayesappliedaicourse+1
The “naive” assumption in Naive Bayes refers to:
a. No learning needed
b. Equal probability for all classes
c. Feature independence
d. Prior probability is unknown
Answer: c. Feature independencesanfoundry+2
Bayes Theorem is instrumental in which area of machine learning?
a. Deep learning
b. Probabilistic modeling
c. Reinforcement learning
d. Time series forecasting
Answer: b. Probabilistic modelinggeeksforgeeks+1
If , , , what is ?
a. 0.56
b. 0.35
c. 0.73
d. 0.28
Answer: b. 0.56geeksforgeeks+1
Which component in Bayes theorem represents new evidence?
a.
b.
c.
d.
Answer: b. geeksforgeeks
The prior probability reflects:
a. Data after analysis
b. Belief before seeing data
c. Probability based only on evidence
d. Random chance
Answer: b. Belief before seeing dataappliedaicourse+1
What is the key difference between prior and posterior probability?
a. Posterior uses only old data
b. Posterior includes new evidence
c. Prior is always greater
d. No difference
Answer: b. Posterior includes new evidenceupgrad+1
Which of the following is NOT a real-world application of Bayes Theorem?
a. Spam detection
b. Weather prediction
c. Web page ranking
d. Medical diagnosis
Answer: c. Web page rankingappliedaicourse+1
What type of data does Naive Bayes handle especially well?
a. Sequential
b. Categorical
c. Temporal
d. Continuous
Answer: b. Categoricalsanfoundry+1
The denominator in Bayes theorem formula, , is known as:
a. Prior probability
b. Marginal probability
c. Joint probability
d. Conditional probability
Answer: b. Marginal probabilitygeeksforgeeks+1
In spam filtering using Bayes theorem, what is the hypothesis tested?
a. Whether the message is too long
b. If the message is spam
c. The sender identity
d. The message date
Answer: b. If the message is spamupgrad+1
Bayes’ theorem helps update probability of:
a. Evidence given the hypothesis
b. Hypothesis given the evidence
c. Random event occurrence
d. Pure chance
Answer: b. Hypothesis given the evidencegeeksforgeeks+1
In a medical context, the prior might be:
a. Test result
b. Probability of a disease in population
c. Patient’s age
d. Number of doctors
Answer: b. Probability of a disease in populationallen+1
The normalizing constant in Bayes Theorem ensures that:
a. All probabilities add up to 1
b. All values are integers
c. Calculations are faster
d. Probabilities can be negative
Answer: a. All probabilities add up to 1geeksforgeeks
A common problem with Naive Bayes is:
a. Model underfitting
b. Data overfitting
c. Zero probability problem
d. Overestimation of variance
Answer: c. Zero probability problemsanfoundry+1
Smoothing techniques are used in Naive Bayes to:
a. Increase bias
b. Avoid zero probabilities
c. Reduce computation
d. Ignore prior
Answer: b. Avoid zero probabilitiessanfoundry+1
In Naive Bayes, Laplace smoothing addresses which issue?
a. Data duplication
b. Zero-frequency
c. Multicollinearity
d. Overfitting
Answer: b. Zero-frequencygeeksforgeeks+1
What does a higher likelihood indicate in Bayesian analysis?
a. More probable evidence for hypothesis
b. Greater prior influence
c. Lower posterior probability
d. Randomness
Answer: a. More probable evidence for hypothesisupgrad+1
Bayesian inference is used to:
a. Randomize predictions
b. Estimate posterior probabilities
c. Discard prior probabilities
d. Maximize entropy only
Answer: b. Estimate posterior probabilitiesappliedaicourse+1
The MAP (Maximum a Posteriori) is:
a. Most probable prior
b. Least uncertain posterior
c. Hypothesis with highest posterior probability
d. Random guess
Answer: c. Hypothesis with highest posterior probabilityupgrad
In machine learning, evidence refers to:
a. Input variables (features)
b. Output class
c. Training size
d. Model loss
Answer: a. Input variables (features)geeksforgeeks+1
Bayes Theorem enables what kind of learning?
a. Inductive learning
b. Instance-based learning
c. Probabilistic learning
d. Reinforcement learning
Answer: c. Probabilistic learningappliedaicourse+1
In a Bayesian network, nodes denote:
a. Random variables
b. Decision boundaries
c. Clusters
d. Data points
Answer: a. Random variablesaimcqs+1
Which distribution is commonly assumed in Gaussian Naive Bayes?
a. Poisson
b. Bernoulli
c. Binomial
d. Normal
Answer: d. Normalaimcqs+1
A key assumption in Naive Bayes is:
a. Features are dependent
b. Features are conditionally independent
c. Evidence is disregarded
d. Priors are unknown
Answer: b. Features are conditionally independentaimcqs+1
Which of the following is NOT a variant of Naive Bayes classifier?
a. Gaussian
b. Multinomial
c. Bernoulli
d. Logistic
Answer: d. Logisticaimcqs+1
When is Bayes Theorem especially useful?
a. When data is missing
b. With huge bias
c. When new data emerges
d. When there is no evidence
Answer: c. When new data emergesgeeksforgeeks+1
What is the function of marginal probability in Bayes Theorem?
a. Normalizes posterior
b. Ignores evidence
c. Adds priors
d. Ignores likelihood
Answer: a. Normalizes posteriorgeeksforgeeks+1
In Bayesian networks, conditional probability tables represent:
a. Model coefficients
b. Variable dependencies
c. Joint distributions
d. Marginal losses
Answer: b. Variable dependenciesscribd+1
The law of total probability is used to:
a. Calculate all possible posterior probabilities
b. Normalize prior
c. Find all marginal probabilities
d. Randomize likelihood
Answer: c. Find all marginal probabilitiesgeeksforgeeks+1
In Naive Bayes, which scenario might cause poor performance?
a. Features highly correlated
b. Small dataset
c. Perfect independence
d. Balanced class distribution
Answer: a. Features highly correlatedsanfoundry+1
What does a Bayesian classifier provide for each class label?
a. A random guess
b. Posterior probability estimate
c. Only prior
d. Evidence sum
Answer: b. Posterior probability estimateappliedaicourse+1
The sum of posteriors over all possible hypotheses is:
a. More than 1
b. Less than 1
c. Exactly 1
d. Indeterminate
Answer: c. Exactly 1geeksforgeeks
The core principle of Bayesian algorithms is to:
a. Minimize loss
b. Use only current data
c. Incorporate prior knowledge
d. Avoid probabilities
Answer: c. Incorporate prior knowledgeaimcqs
The output of a Naive Bayes classifier is:
a. A set of random values
b. Most likely class label
c. Probability distribution over all classes
d. Value between 0 and 1
Answer: b. Most likely class labelappliedaicourse
When is Bayes theorem commonly used in sequential learning?
a. When there is fixed data
b. When updating beliefs as new observations arrive
c. When there’s no prior
d. When all features are identical
Answer: b. When updating beliefs as new observations arriveupgrad+1
Which of the following is Bayesian statistics NOT used for?
a. Estimating probabilities
b. Random forest optimization
c. Hypothesis testing
d. Updating beliefs
Answer: b. Random forest optimizationgeeksforgeeks+1
What is a potential pitfall of strong prior in Bayesian learning?
a. Too much influence from past
b. Increased variance
c. Faster convergence
d. Decreased bias
Answer: a. Too much influence from pastupgrad+1
What is the best prior to use if you have no information?
a. Informative prior
b. Non-informative prior
c. Maximum likelihood
d. Posterior prior
Answer: b. Non-informative priorgeeksforgeeks+1
Which learning scenario benefits most from Bayes Theorem?
a. Dynamic environments
b. Static datasets
c. Offline computation only
d. Shuffled labels
Answer: a. Dynamic environmentsappliedaicourse+1
If the likelihood of evidence given a hypothesis is low, what happens to the posterior?
a. Increases
b. Decreases
c. Stays the same
d. Divides the prior
Answer: b. Decreasesupgrad+1
MAP estimate in Bayes theorem refers to:
a. Maximum likelihood of evidence
b. Most probable posterior
c. Most probable prior
d. Minimum loss
Answer: b. Most probable posteriorupgrad
In machine learning, Bayes theorem is essential for:
a. Deterministic rule engines
b. Probabilistic reasoning
c. Exact optimization
d. Pattern matching only
Answer: b. Probabilistic reasoninggeeksforgeeks+1
Which of the following is an example of Bayesian updating?
a. Tossing a fair coin many times
b. Adjusting fraud detection as new incidents occur
c. Sorting numbers in order
d. Taking a mean of numbers
Answer: b. Adjusting fraud detection as new incidents occurappliedaicourse+1

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