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

Question 1

A data scientist is building a model to predict whether a transaction is fraudulent. The dataset has 99.9% legitimate transactions and 0.1% fraudulent ones. Which evaluation metric is MOST appropriate to assess model performance given this class imbalance?

Accepted Answer

F1-score. With 99.9% legitimate transactions and only 0.1% fraudulent ones, accuracy would be misleadingly high (99.9%) even if the model never predicts fraud. The F1-score is the harmonic mean of precision and recall, making it robust to class imbalance by penalizing both false positives and false negatives. This makes it the most appropriate metric for evaluating fraud detection performance.

Answer

BLEU score

Answer

Accuracy

Answer

Perplexity

Question 2

Which machine learning paradigm involves training an agent to make decisions by interacting with an environment and receiving rewards or penalties based on its actions?

Accepted Answer

Reinforcement learning. Reinforcement learning (RL) is the correct paradigm because it explicitly involves an agent learning a policy through trial-and-error interactions with an environment, receiving scalar reward signals (positive or negative) to maximize cumulative reward. This matches the question's description of making decisions based on rewards or penalties, which is the defining characteristic of RL, as opposed to learning from labeled data or discovering hidden patterns without feedback.

Answer

Unsupervised learning

Answer

Supervised learning

Answer

Self-supervised learning

Question 3

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.

Answer

Add L1 regularization

Answer

Increase the batch size

Answer

Increase the number of epochs

Question 4

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.

Answer

Use a larger base model without additional training

Answer

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

Answer

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

Question 5

Which neural network architecture is specifically designed to process sequential data, such as time series or sentences, by maintaining a hidden state that captures information about previous inputs?

Accepted Answer

Recurrent Neural Network (RNN). Recurrent Neural Networks (RNNs) are specifically designed for sequential data because they maintain a hidden state that is updated at each time step, allowing information about previous inputs to persist and influence current and future outputs. This feedback loop makes them ideal for tasks like time series forecasting, natural language processing, and speech recognition, where order and context matter.

Answer

Transformer

Answer

Convolutional Neural Network (CNN)

Answer

Multi-layer Perceptron (MLP)

Question 6

A company wants to automatically group customer support tickets into categories (e.g., billing, technical, account) without pre-labeled data. Which machine learning approach should they use?

Accepted Answer

Unsupervised clustering using K-means. Option C is correct because the company has no pre-labeled data, which means supervised learning (which requires labeled examples) is not feasible. Unsupervised clustering, such as K-means, groups data points into clusters based on feature similarity without needing any labels, making it ideal for automatically discovering categories like billing, technical, or account from raw ticket text.

Answer

Supervised classification with logistic regression

Answer

Semi-supervised learning with a small labeled set

Answer

Reinforcement learning with a reward function

Question 7

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.

Answer

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

Answer

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

Answer

Diffusion model; use a larger batch size during training

Question 8

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.

Answer

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

Answer

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

Answer

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

Question 9

A team is training a deep learning model for image classification. They observe that training accuracy is high but validation accuracy is low, indicating overfitting. Which TWO techniques should they apply to reduce overfitting? (Select TWO)

Accepted Answer

Use L2 regularization. L2 regularization (also known as weight decay) adds a penalty proportional to the square of the magnitude of the weights to the loss function. This discourages the model from learning overly complex patterns that fit the training data noise, effectively reducing overfitting by keeping weights small and the decision boundary simpler.

Answer

Increase learning rate

Answer

Increase the number of layers

Answer

Reduce training data size

Question 10

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. Option B is correct because 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.

Answer

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

Answer

Use a higher temperature setting during generation

Question 11

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.

Answer

Ridge regression (L2 regularization)

Answer

Linear regression

Answer

K-Nearest Neighbors

Question 12

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.

Answer

Set temperature to 0.0 for all queries

Answer

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

Question 13

A machine learning engineer is training a convolutional neural network (CNN) for object detection in satellite imagery. The training loss is not decreasing significantly. Which TWO adjustments could help the model converge? (Select TWO)

Accepted Answer

Normalize the pixel values to zero mean and unit variance. Normalizing pixel values to zero mean and unit variance (A) ensures that input features have similar scales, which prevents certain weights from updating disproportionately and stabilizes gradient descent. This is especially important for CNNs processing satellite imagery, where raw pixel intensities can vary widely across bands and scenes, leading to poor convergence.

Answer

Remove dropout layers to allow more gradient flow

Answer

Use a smaller batch size to reduce memory

Answer

Increase the learning rate by 10x

Question 14

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.

Answer

Principal Component Analysis (PCA)

Answer

Naive Bayes

Answer

Linear regression

Question 15

A data scientist is preparing a dataset for a binary classification model. The dataset has 1000 samples, with 800 positives and 200 negatives. To evaluate the model properly, which THREE steps should they take? (Select THREE)

Accepted Answer

Use a stratified train-test split to preserve class proportions. Option B is correct because stratified train-test splitting ensures that the class distribution (80% positive, 20% negative) is preserved in both training and test sets. This prevents the model from being evaluated on a test set that has a different class ratio, which could give a misleading impression of performance, especially in imbalanced datasets.

Answer

Remove the minority class samples to make the dataset balanced

Answer

Report only accuracy as the evaluation metric