ARTIBA Artificial Intelligence Engineer Certification (AIEC) Practice Exam – Full 300 Questions

ARTIBA Artificial Intelligence Engineer
Certification (AIEC) Practice Exam – Full
300 Questions
 Aspiring AI Engineers seeking ARTIBA AIEC certification
 Professionals preparing for AI-related roles
 Students and practitioners seeking to validate knowledge of AI concepts, techniques, and
ethical practices

Exam Overview
The ARTIBA AIEC Practice Exam is a comprehensive tool designed to assess and reinforce
knowledge in Artificial Intelligence engineering. This exam covers:

1. Fundamental AI Concepts:
o AI definitions, types, and applications
o Key algorithms and AI paradigms
2. Machine Learning & Deep Learning:
o Supervised, unsupervised, and reinforcement learning
o Neural networks, CNNs, RNNs/LSTMs, and autoencoders
o Model evaluation metrics, overfitting, and hyperparameter tuning
3. Data Processing & Feature Engineering:
o Data preprocessing techniques (scaling, normalization, one-hot encoding)
o Dimensionality reduction (PCA)
o Word embeddings and NLP feature representations
4. Model Evaluation & Metrics:
o Classification and regression metrics (Accuracy, F1 Score, MSE, Precision,
Recall)
o Handling imbalanced datasets
o Overfitting prevention methods (dropout, regularization)
5. AI Applications:
o NLP chatbots, recommendation systems, computer vision, predictive analytics
o Sequential forecasting using LSTMs
o Anomaly detection and clustering
6. AI Ethics & Bias:
o Fairness, accountability, and explainability
o Identifying and mitigating sampling bias, label bias, and algorithmic bias
7. Ensemble Methods & Optimization:
o Bagging, boosting, and stacking
o Improving accuracy through model combination

 Each question includes the correct answer (bolded) and a clear rationale

,Question 1

Which AI technique involves training models on labeled data?
A. Supervised Learning
B. Unsupervised Learning
C. Reinforcement Learning
D. Semi-Supervised Learning

Rationale: Supervised learning uses labeled datasets to train models for prediction.



Question 2

Which method reduces overfitting by penalizing large weights in a neural network?
A. Dropout
B. L1/L2 Regularization
C. ReLU Activation
D. Feature Scaling

Rationale: L1/L2 regularization adds penalties to weights, preventing overfitting.



Question 3

Which neural network type is best suited for sequential data like time series or text?
A. CNN
B. RNN / LSTM
C. Autoencoder
D. Feedforward Neural Network

Rationale: RNNs and LSTMs capture temporal dependencies in sequential data.



Question 4

Which preprocessing technique replaces missing data with mean, median, or mode?
A. Scaling
B. Normalization
C. Imputation
D. One-Hot Encoding

,Rationale: Imputation fills missing values to maintain dataset integrity.



Question 5

Which AI application is used to forecast equipment failures in manufacturing?
A. Text Summarization
B. Predictive Maintenance
C. GAN Generation
D. Clustering

Rationale: Predictive maintenance uses AI to anticipate failures from sensor data.



Question 6

Which metric balances precision and recall for classification tasks?
A. Accuracy
B. Mean Squared Error (MSE)
C. F1 Score
D. R-Squared

Rationale: F1 Score is ideal for imbalanced datasets, combining precision and recall.



Question 7

Which AI model generates realistic images using a generator and discriminator?
A. CNN
B. Autoencoder
C. GAN (Generative Adversarial Network)
D. RNN

Rationale: GANs produce realistic synthetic data through adversarial training.



Question 8

Which ensemble method sequentially improves weak learners?
A. Bagging
B. Random Forest

, C. Boosting
D. PCA

Rationale: Boosting iteratively corrects errors from weak models to create a strong ensemble.



Question 9

Which AI method groups unlabeled data into clusters?
A. Linear Regression
B. Logistic Regression
C. K-Means Clustering
D. Random Forest

Rationale: K-Means is an unsupervised method that partitions data into similar groups.



Question 10

Which principle ensures AI decisions are interpretable by humans?
A. Privacy
B. Fairness
C. Explainability
D. Accountability

Rationale: Explainability makes AI models’ reasoning understandable to humans.



Question 11

Which neural network type is commonly used for image classification?
A. RNN
B. CNN
C. Autoencoder
D. Feedforward NN

Rationale: CNNs extract spatial features from images efficiently.



Question 12