ARTIBA ARTIFICIAL INTELLIGENCE ENGINEER CERTIFICATION
NEWEST EXAM ACTUAL 2025/2026 WITH COMPLETE QUESTIONS
WITH ANSWERS / VERIFIED/GRADED A+/ UPDATED
k-Nearest Neighbors (k-NN) (Classification) - --Answers----A
non-parametric algorithm that classifies data based on the k
closest neighbors.
Decision Tree Classifier - --Answers----A tree structure where
internal nodes represent decisions and leaf nodes represent
outcomes. • Can handle both classification and regression
K-Means Clustering - --Answers----Groups data into k clusters
based on feature similarity. • Each point belongs to the cluster
with the nearest centroid
A. Clustering DBSCAN (Density-Based Spatial Clustering) - --
Answers----Groups closely packed points and marks outliers as
noise. • Doesn't require the number of clusters to be specified
Regression - --Answers----MSE, RMSE, MAE, R. Measures
accuracy of continuous predictions
1 | Page
, Classification - --Answers----Accuracy, Precision, Recall, F1-
score, Confusion Matrix. Measures performance on classification
tasks
What are the three main types of Machine Learning? - --
Answers----Supervised, Unsupervised, and Reinforcement
Learning.
What's the key difference between supervised and unsupervised
learning? - --Answers----Supervised uses labeled data (input
→ output), while unsupervised uses unlabeled data to find
patterns.
Example of supervised learning algorithms? - --Answers----
Linear Regression, Logistic Regression, Decision Trees, k-NN.
Example of unsupervised learning algorithms? - --Answers----
K-Means, DBSCAN, PCA (Dimensionality Reduction).
Linear Regression - --Answers----A method of finding the best
model for a linear relationship between the explanatory and
response variable.
Polynomial Regression - --Answers----Captures non-linear
relationships by transforming input features. • Example: 𝑦
=𝑎+𝑏𝑥+𝑐𝑥2
2 | Page
NEWEST EXAM ACTUAL 2025/2026 WITH COMPLETE QUESTIONS
WITH ANSWERS / VERIFIED/GRADED A+/ UPDATED
k-Nearest Neighbors (k-NN) (Classification) - --Answers----A
non-parametric algorithm that classifies data based on the k
closest neighbors.
Decision Tree Classifier - --Answers----A tree structure where
internal nodes represent decisions and leaf nodes represent
outcomes. • Can handle both classification and regression
K-Means Clustering - --Answers----Groups data into k clusters
based on feature similarity. • Each point belongs to the cluster
with the nearest centroid
A. Clustering DBSCAN (Density-Based Spatial Clustering) - --
Answers----Groups closely packed points and marks outliers as
noise. • Doesn't require the number of clusters to be specified
Regression - --Answers----MSE, RMSE, MAE, R. Measures
accuracy of continuous predictions
1 | Page
, Classification - --Answers----Accuracy, Precision, Recall, F1-
score, Confusion Matrix. Measures performance on classification
tasks
What are the three main types of Machine Learning? - --
Answers----Supervised, Unsupervised, and Reinforcement
Learning.
What's the key difference between supervised and unsupervised
learning? - --Answers----Supervised uses labeled data (input
→ output), while unsupervised uses unlabeled data to find
patterns.
Example of supervised learning algorithms? - --Answers----
Linear Regression, Logistic Regression, Decision Trees, k-NN.
Example of unsupervised learning algorithms? - --Answers----
K-Means, DBSCAN, PCA (Dimensionality Reduction).
Linear Regression - --Answers----A method of finding the best
model for a linear relationship between the explanatory and
response variable.
Polynomial Regression - --Answers----Captures non-linear
relationships by transforming input features. • Example: 𝑦
=𝑎+𝑏𝑥+𝑐𝑥2
2 | Page
