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ARTIBA Artificial Intelligence Engineer
Certification Practice Exam | 2025/2026 Edition |
Verified Questions & Answers
The ARTIBA Artificial Intelligence Engineer Certification Practice Exam is a
comprehensive study guide designed for professionals preparing for the AIEC
certification. This resource provides realistic, exam-style questions with verified
answers and detailed Rationales to help candidates master key AI engineering
concepts and confidently prepare for the certification exam.
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
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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)
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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
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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
Which AI method is best for sequential numeric forecasting, such as stock prices?
A. CNN
B. GAN
C. LSTM / RNN
D. PCA
Rationale: LSTMs capture temporal dependencies in sequential data.
Question 13
ARTIBA Artificial Intelligence Engineer
Certification Practice Exam | 2025/2026 Edition |
Verified Questions & Answers
The ARTIBA Artificial Intelligence Engineer Certification Practice Exam is a
comprehensive study guide designed for professionals preparing for the AIEC
certification. This resource provides realistic, exam-style questions with verified
answers and detailed Rationales to help candidates master key AI engineering
concepts and confidently prepare for the certification exam.
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
,2|Page
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)
,3|Page
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
, 4|Page
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
Which AI method is best for sequential numeric forecasting, such as stock prices?
A. CNN
B. GAN
C. LSTM / RNN
D. PCA
Rationale: LSTMs capture temporal dependencies in sequential data.
Question 13
