AI0-001 AI Concepts and Techniques Practice Test 5 — 15 Questions

Question 1

A bank wants to detect fraudulent transactions in real-time. The dataset is highly imbalanced (99.9% legitimate, 0.1% fraud). Which evaluation metric is MOST appropriate for model performance?

Accepted Answer

AUC-ROC. AUC-ROC is the most appropriate metric because it evaluates the model's ability to distinguish between the minority fraud class (0.1%) and the majority legitimate class across all classification thresholds, without being biased by the extreme class imbalance. Unlike accuracy, AUC-ROC remains robust when the dataset is 99.9% legitimate, as it measures the true positive rate against the false positive rate, providing a comprehensive view of model performance for rare event detection.

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Accuracy

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Recall

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Precision

Question 2

Which type of neural network is BEST suited for processing sequential data such as time series or natural language?

Accepted Answer

Recurrent Neural Network (RNN). RNNs (including LSTMs) are designed for sequential data with temporal dependencies. CNNs excel at spatial data; transformers are also used but RNNs are the classic answer.

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Generative Adversarial Network (GAN)

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Multi-layer Perceptron (MLP)

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Convolutional Neural Network (CNN)

Question 3

A model trained on customer reviews achieves 98% accuracy on the test set. However, when deployed, it performs poorly on real-world data. The data scientist suspects distribution shift. Which action is MOST important to address this?

Accepted Answer

Implement a monitoring system to detect data drift and retrain with fresh data. Distribution shift (data drift) causes the model's training distribution to differ from the real-world distribution, degrading performance despite high test accuracy. Implementing a monitoring system to detect drift and retraining with fresh data directly addresses this by ensuring the model adapts to the current data distribution, which is the most critical action for maintaining performance in production.

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Reduce the learning rate during training

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Add more features to the model

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Increase the number of cross-validation folds

Question 4

A team is using a pre-trained BERT model for a sentiment analysis task on product reviews. They want to adapt it to their specific domain with limited labeled data. Which approach is MOST effective?

Accepted Answer

Fine-tune the pre-trained BERT model on the small labeled dataset. Fine-tuning the pre-trained model on the small labeled dataset is standard for transfer learning with BERT. Training from scratch would require massive data; feature extraction with a linear classifier is possible but less effective with limited data; data augmentation alone does not adapt the model.

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Use BERT as a feature extractor and train a logistic regression on top

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Apply data augmentation to increase the dataset and then train from scratch

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Train a new BERT model from scratch on the domain data

Question 5

An organization's AI system uses a decision tree model for loan approval. The compliance team requires explanations for each decision. Which property of decision trees makes them suitable for this requirement?

Accepted Answer

The decision rules are transparent and can be visualized as a tree. Decision trees inherently provide interpretable decision rules by splitting data based on feature thresholds at each node. The entire model can be visualized as a tree structure, allowing compliance teams to trace the exact path and logic behind each loan approval or rejection, which directly satisfies explainability requirements.

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They can handle nonlinear relationships

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They are robust to outliers

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They can handle missing values

Question 6

A data scientist is building a recommendation system for an e-commerce platform. The dataset includes user purchase history, product descriptions, and user demographics. The goal is to recommend products that a user is likely to purchase. Which TWO techniques are most appropriate for this task? (Select TWO.)

Accepted Answer

Content-based filtering. Collaborative filtering uses user-item interactions; content-based filtering uses item features. Association rule mining is for basket analysis; regression is not typically used for recommendations; anomaly detection is for outliers.

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Association rule mining

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Linear regression

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Anomaly detection

Question 7

A team is deploying a sentiment analysis model that must achieve high precision and high recall. They have a labeled dataset of 10,000 samples. They want to minimize overfitting. Which THREE actions are most appropriate? (Select THREE.)

Accepted Answer

Apply L2 regularization to the model weights. L2 regularization (option B) penalizes large weights by adding a squared magnitude term to the loss function, which discourages the model from fitting noise in the training data. This directly reduces overfitting while maintaining high precision and recall by keeping the decision boundary smooth and generalizable.

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Decrease the learning rate

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Increase the training batch size

Question 8

A developer is fine-tuning a large language model for a legal document summarization task. They notice that during training, the loss decreases rapidly in the first few epochs but then plateaus with high variance. Which hyperparameter adjustment is MOST likely to help stabilize training?

Accepted Answer

Decrease the learning rate. A high-variance loss plateau after rapid initial convergence typically indicates that the learning rate is too large, causing the optimizer to overshoot the minima and oscillate. Decreasing the learning rate allows smaller, more stable weight updates, reducing variance and enabling smoother convergence.

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Add L1 regularization

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Increase the batch size

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Increase the number of epochs

Question 9

A team is deploying a sentiment analysis model for social media posts. The model currently performs well on English text but poorly on code-switched text (e.g., Spanglish). Which approach is MOST effective for improving performance on code-switched data without starting from scratch?

Accepted Answer

Fine-tune the existing model on a corpus of code-switched text. Fine-tuning the existing model on a corpus of code-switched text adapts the model to the new language pattern efficiently.

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Use a larger base model without additional training

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Apply data augmentation by translating all code-switched posts to English

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Train a new model from scratch on a mix of English and code-switched data

Question 10

A generative AI model produces images from text prompts. The outputs are often blurry and lack fine details. Which model type is MOST likely being used, and which improvement would best address this issue?

Accepted Answer

Variational Autoencoder (VAE); switch to a diffusion model. Variational Autoencoders (VAEs) are known for producing blurry outputs because their loss function (ELBO) encourages pixel-wise averaging, which smooths out fine details. Diffusion models, by contrast, iteratively denoise a random field, learning to reconstruct high-frequency details through a multi-step reverse process, directly addressing the blurriness issue.

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Variational Autoencoder (VAE); switch to a Generative Adversarial Network (GAN)

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Generative Adversarial Network (GAN); increase the discriminator's capacity

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Diffusion model; use a larger batch size during training

Question 11

Which of the following best describes the difference between narrow AI and general AI?

Accepted Answer

Narrow AI is designed for a specific task; general AI aims to perform any cognitive task a human can.. Narrow AI specializes in one task, while general AI would possess human-like cognitive abilities across domains.

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Narrow AI relies on supervised learning; general AI uses unsupervised learning exclusively.

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Narrow AI requires large datasets; general AI can learn from few examples.

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Narrow AI can perform any intellectual task; general AI is limited to specific tasks.

Question 12

An AI engineer is fine-tuning a transformer-based language model for a domain-specific task. They want to improve the model's factual accuracy and reduce hallucinations. Which THREE strategies should they consider? (Select THREE)

Accepted Answer

Fine-tune the model on a curated domain-specific corpus. Fine-tuning on a curated domain-specific corpus directly aligns the model with the factual patterns and terminology of the target domain. This supervised learning process adjusts the model's weights to reduce the probability of generating incorrect or hallucinated content by reinforcing ground-truth examples from the domain.

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Increase the model's context window size beyond the training limit

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Use a higher temperature setting during generation

Question 13

A data scientist needs to select a regression model to predict house prices. The dataset contains many features, some of which are irrelevant. Which TWO algorithms are BEST suited for this scenario, and why? (Select TWO)

Accepted Answer

Lasso regression (L1 regularization). Random Forest handles irrelevant features well via feature importance. Lasso (L1) regression performs automatic feature selection by shrinking coefficients to zero.

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Ridge regression (L2 regularization)

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Linear regression

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K-Nearest Neighbors

Question 14

A company is deploying an LLM-powered application that answers questions based on internal documents. They want to minimize prompt injection attacks where users trick the model into ignoring instructions. Which THREE measures should they implement? (Select THREE)

Accepted Answer

Use a system-level prompt that clearly defines allowed behavior and boundaries. A is correct because a system-level prompt establishes a foundational instruction set that defines the model's allowed behavior and boundaries. This acts as a first line of defense by explicitly instructing the model to ignore any user attempts to override its core directives, thereby reducing the risk of prompt injection attacks.

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Set temperature to 0.0 for all queries

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Allow the model to execute any code from user prompts for flexibility

Question 15

A company wants to use machine learning to recommend products to customers based on their purchase history. Which TWO techniques are appropriate for this task? (Select TWO)

Accepted Answer

Collaborative filtering. Collaborative filtering recommends based on user similarities. K-Nearest Neighbors can find similar users or items. Both are suitable for recommendation.

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Principal Component Analysis (PCA)

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Naive Bayes

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Linear regression