ARTIBA Artificial Intelligence Engineer Certification Exam With Actual Questions & Verified Answers ,Plus Explained Rationales/Expert Verified For Guaranteed Pass 2026/Latest Update/Instant Download Pdf

ARTIBA Artificial Intelligence Engineer
Certification Exam With Actual
Questions & Verified Answers ,Plus
Explained Rationales/Expert Verified
For Guaranteed Pass 2026/Latest
Update/Instant Download Pdf

ARTIBA Artificial Intelligence Engineer Certification Exam
1. Which of the following is a supervised learning algorithm?
A) K-Means Clustering
B) Linear Regression
C) Principal Component Analysis
D) DBSCAN
B) Linear Regression
Rationale: Linear regression requires labeled data to learn a mapping
from input features to output targets, making it a supervised learning
algorithm.
2. What is the primary purpose of the activation function in a
neural network?
A) To initialize weights
B) To introduce non-linearity
C) To normalize input data
D) To reduce overfitting

,B) To introduce non-linearity
Rationale: Activation functions like ReLU or sigmoid allow the
network to capture complex, non-linear relationships in the data.
3. Which metric is most suitable for evaluating a classification
model with imbalanced classes?
A) Accuracy
B) Mean Squared Error
C) F1 Score
D) R-squared
C) F1 Score
Rationale: F1 Score balances precision and recall, making it more
informative than accuracy for imbalanced datasets.
4. In reinforcement learning, the agent learns by interacting with:
A) A training dataset
B) A reward-based environment
C) A pre-trained model
D) Unlabeled data
B) A reward-based environment
Rationale: Reinforcement learning involves an agent taking actions in
an environment to maximize cumulative reward.
5. Which of the following is a generative AI model?
A) Logistic Regression
B) GAN (Generative Adversarial Network)
C) Random Forest
D) SVM
B) GAN (Generative Adversarial Network)
Rationale: GANs are designed to generate new data instances that
resemble the training data.

, 6. What does overfitting in a model indicate?
A) Model performs well on unseen data
B) Model captures noise in the training data
C) Model has low complexity
D) Model underperforms on training data
B) Model captures noise in the training data
Rationale: Overfitting occurs when a model learns patterns specific to
the training set, failing to generalize.
7. Which technique is commonly used to prevent overfitting in
neural networks?
A) Data augmentation
B) Increasing learning rate
C) Reducing dataset size
D) Removing activation functions
A) Data augmentation
Rationale: Data augmentation increases dataset diversity, helping
the model generalize better.
8. What is the main function of backpropagation in a neural
network?
A) Forward pass of input data
B) Updating weights based on error
C) Selecting activation function
D) Splitting dataset into batches
B) Updating weights based on error
Rationale: Backpropagation calculates gradients of the loss function
and updates weights via gradient descent.
9. Which type of neural network is most suitable for sequential
data?

, A) Convolutional Neural Network (CNN)
B) Recurrent Neural Network (RNN)
C) Feedforward Neural Network
D) Autoencoder
B) Recurrent Neural Network (RNN)
Rationale: RNNs have memory cells that retain information from
previous inputs, making them suitable for sequences.
10. Which method is used to reduce dimensionality while
preserving variance?
A) Linear Regression
B) Principal Component Analysis (PCA)
C) K-Means Clustering
D) Decision Trees
B) Principal Component Analysis (PCA)
Rationale: PCA transforms data into a lower-dimensional space while
retaining most of the original variance.
11. Which loss function is commonly used for binary
classification?
A) Mean Squared Error
B) Cross-Entropy Loss
C) Hinge Loss
D) Kullback-Leibler Divergence
B) Cross-Entropy Loss
Rationale: Cross-entropy measures the difference between predicted
probabilities and actual class labels in classification tasks.
12. Which algorithm is an example of unsupervised learning?
A) Linear Regression
B) Decision Tree