Washington Machine Learning Professional
Exam Questions And Correct Answer with
Rational (100% verified answer) Q & A 2026 /
Instant download PDF
1. What is supervised learning?
A. Learning without labels
B. Learning with labeled data
C. Reinforcement learning only
D. Clustering data
Answer: B
Supervised learning uses labeled datasets to train models to predict outcomes.
2. Which algorithm is used for classification?
A. Linear Regression
B. K-Means
C. Decision Tree
D. PCA
Answer: C
Decision trees are commonly used for classification tasks.
3. What does overfitting mean?
A. Model performs well on test data
B. Model performs poorly on training data
,C. Model memorizes training data
D. Model ignores features
Answer: C
Overfitting occurs when a model learns noise and memorizes training data.
4. What is the purpose of train-test split?
A. Increase dataset size
B. Evaluate model performance
C. Remove features
D. Normalize data
Answer: B
It helps evaluate how well the model generalizes to unseen data.
5. Which metric is used for regression?
A. Accuracy
B. Precision
C. Mean Squared Error
D. F1-score
Answer: C
MSE measures average squared differences between predictions and actual
values.
6. What is unsupervised learning?
A. Learning with labels
B. Learning without labels
C. Learning with rewards
D. Learning with regression
,Answer: B
It finds hidden patterns in unlabeled data.
7. K-Means is used for?
A. Classification
B. Clustering
C. Regression
D. Prediction
Answer: B
K-Means groups similar data points into clusters.
8. What is bias in ML?
A. Random error
B. Systematic error
C. Data noise
D. Variance reduction
Answer: B
Bias is error from overly simplistic assumptions.
9. What is variance in ML?
A. Error from noise sensitivity
B. Constant error
C. Data normalization
D. Feature scaling
Answer: A
Variance measures model sensitivity to training data.
, 10. What is regularization used for?
A. Increase complexity
B. Reduce overfitting
C. Remove data
D. Increase bias only
Answer: B
Regularization penalizes large coefficients to prevent overfitting.
11. Which is a supervised learning algorithm?
A. K-Means
B. PCA
C. Linear Regression
D. DBSCAN
Answer: C
Linear regression uses labeled data for prediction.
12. Logistic regression is used for?
A. Clustering
B. Classification
C. Regression only
D. Dimensionality reduction
Answer: B
It predicts binary or categorical outcomes.
13. What is gradient descent?
A. Data cleaning method
B. Optimization algorithm
Exam Questions And Correct Answer with
Rational (100% verified answer) Q & A 2026 /
Instant download PDF
1. What is supervised learning?
A. Learning without labels
B. Learning with labeled data
C. Reinforcement learning only
D. Clustering data
Answer: B
Supervised learning uses labeled datasets to train models to predict outcomes.
2. Which algorithm is used for classification?
A. Linear Regression
B. K-Means
C. Decision Tree
D. PCA
Answer: C
Decision trees are commonly used for classification tasks.
3. What does overfitting mean?
A. Model performs well on test data
B. Model performs poorly on training data
,C. Model memorizes training data
D. Model ignores features
Answer: C
Overfitting occurs when a model learns noise and memorizes training data.
4. What is the purpose of train-test split?
A. Increase dataset size
B. Evaluate model performance
C. Remove features
D. Normalize data
Answer: B
It helps evaluate how well the model generalizes to unseen data.
5. Which metric is used for regression?
A. Accuracy
B. Precision
C. Mean Squared Error
D. F1-score
Answer: C
MSE measures average squared differences between predictions and actual
values.
6. What is unsupervised learning?
A. Learning with labels
B. Learning without labels
C. Learning with rewards
D. Learning with regression
,Answer: B
It finds hidden patterns in unlabeled data.
7. K-Means is used for?
A. Classification
B. Clustering
C. Regression
D. Prediction
Answer: B
K-Means groups similar data points into clusters.
8. What is bias in ML?
A. Random error
B. Systematic error
C. Data noise
D. Variance reduction
Answer: B
Bias is error from overly simplistic assumptions.
9. What is variance in ML?
A. Error from noise sensitivity
B. Constant error
C. Data normalization
D. Feature scaling
Answer: A
Variance measures model sensitivity to training data.
, 10. What is regularization used for?
A. Increase complexity
B. Reduce overfitting
C. Remove data
D. Increase bias only
Answer: B
Regularization penalizes large coefficients to prevent overfitting.
11. Which is a supervised learning algorithm?
A. K-Means
B. PCA
C. Linear Regression
D. DBSCAN
Answer: C
Linear regression uses labeled data for prediction.
12. Logistic regression is used for?
A. Clustering
B. Classification
C. Regression only
D. Dimensionality reduction
Answer: B
It predicts binary or categorical outcomes.
13. What is gradient descent?
A. Data cleaning method
B. Optimization algorithm
