AI0-001 AI Concepts and Foundations Practice Test 6 — 15 Questions

Question 1

An AI model achieves high accuracy on training data but performs poorly on new test data. The data scientist suspects the model has memorized noise. Which technique directly adds a penalty term to the loss function to address this?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

L2 regularization. L2 regularization (also known as weight decay) directly adds a penalty term proportional to the squared magnitude of the model's weights to the loss function. This discourages the model from fitting the noise in the training data by keeping weights small, thereby reducing overfitting and improving generalization to new test data.

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Batch normalization

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Data augmentation

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Dropout

Question 2

A company deploys a chatbot using a large language model (LLM). After launch, users report that the chatbot sometimes generates plausible but false information. This phenomenon is known as:

Accepted Answer

Hallucination. Hallucination in LLMs refers to the generation of plausible but factually incorrect or nonsensical information. This occurs when the model's probabilistic next-token prediction produces confident-sounding outputs that deviate from training data or real-world facts, often due to insufficient grounding or training data gaps.

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Gradient explosion

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Overfitting

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Concept drift

Question 3

In the AI lifecycle, which phase involves splitting data into training, validation, and test sets?

Accepted Answer

Data preprocessing. Data preprocessing is the phase where raw data is cleaned, transformed, and prepared for modeling. Splitting the dataset into training, validation, and test sets is a critical step during this phase to ensure unbiased evaluation and prevent data leakage. This split occurs before any model training begins, making it part of preprocessing rather than training or evaluation.

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Model training

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Data collection

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Model evaluation

Question 4

A self-driving car uses an AI model that learns by trial and error, receiving rewards for correct actions and penalties for mistakes. This type of learning is:

Accepted Answer

Reinforcement learning. Reinforcement learning (RL) is the correct answer because the self-driving car's AI model learns through trial and error, receiving rewards for correct actions and penalties for mistakes. This feedback-driven process, where an agent interacts with an environment to maximize cumulative reward, is the defining characteristic of reinforcement learning, not supervised or unsupervised learning.

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Supervised learning

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Unsupervised learning

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Transfer learning

Question 5

An AI team notices that their model's performance degrades over time because the statistical relationship between input features and the target variable changes. This issue is called:

Accepted Answer

Concept drift. Concept drift occurs when the statistical relationship between input features and the target variable changes over time, causing model performance to degrade. This is distinct from data drift, which involves changes in the input data distribution alone. In the AI0-001 context, concept drift directly addresses the shift in the underlying mapping from features to labels.

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Data drift

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Overfitting

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Model drift

Question 6

A company wants to create an AI system that can identify objects in images. They have a large dataset of labeled images. Which type of neural network architecture is most suitable?

Accepted Answer

Convolutional neural network (CNN). Convolutional neural networks (CNNs) are specifically designed to process grid-like data such as images. They use convolutional layers to automatically learn spatial hierarchies of features (edges, textures, objects) from pixel data, making them the most suitable architecture for image classification tasks with labeled datasets.

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Transformer

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Generative adversarial network (GAN)

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Recurrent neural network (RNN)

Question 7

A data scientist is preparing a dataset for supervised learning. Which TWO steps are essential?

Accepted Answer

Labeling the data. Labeling the data is essential for supervised learning because the algorithm requires input-output pairs to learn a mapping function. Without labeled data, the model cannot be trained to predict outcomes, as supervised learning relies on ground-truth targets for error correction during training.

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One-hot encoding all features

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Normalizing features

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Removing outliers

Question 8

Which THREE are common machine learning algorithms used for regression?

Accepted Answer

Linear regression. Linear regression (C) is the canonical regression algorithm, modeling a continuous target as a linear combination of input features by minimizing squared error. Decision tree (D) regressors recursively split data on feature thresholds to predict continuous values, e.g., via variance reduction or mean-squared-error minimization at each node. K-nearest neighbors (E) performs regression by averaging the target values of the k closest training points under a chosen distance metric, making it a standard non-parametric regressor. Logistic regression (A) is excluded because it is a classification algorithm that outputs class probabilities via the sigmoid/softmax function, not a continuous value. K-means (B) is excluded because it is an unsupervised clustering algorithm that partitions data into groups, not a supervised regression method.

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Logistic regression

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K-means

Question 9

A team is deploying an AI model for credit approval. Which TWO ethical considerations must be addressed?

Accepted Answer

Model interpretability. Model interpretability (B) is essential for credit approval because financial decisions must be explainable to regulators and customers under laws like GDPR or ECOA. A black-box model that cannot justify why a loan was denied violates compliance requirements, making interpretability a core ethical and legal necessity.

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Training speed

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Model accuracy

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Model size

Question 10

Refer to the exhibit. A data scientist observes the training output. Which issue is most likely?

Accepted Answer

Overfitting. The exhibit shows training loss decreasing while validation loss increases after a certain epoch, which is the classic signature of overfitting. The model is memorizing the training data rather than learning generalizable patterns, leading to poor performance on unseen data.

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Underfitting

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Data augmentation failure

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Model compression

Question 11

Refer to the exhibit. An AI auditor reviews the fairness configuration. What is the purpose of this policy?

Accepted Answer

Ensure equal positive prediction rates across groups. The policy sets a fairness constraint that requires the model's positive prediction rate (the fraction of instances predicted as the positive class) to be equal across all defined groups. This is a standard demographic parity requirement, which is implemented by adjusting the decision threshold or reweighting training data to ensure that each group receives the same proportion of positive predictions, regardless of the actual outcome distribution.

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Ensure equal error rates across groups

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Ensure equal accuracy across groups

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Ensure model interpretability

Question 12

A data scientist is training a neural network to classify images of animals. The training accuracy is 99%, but validation accuracy is only 65%. Which technique should the data scientist use to address this issue?

Accepted Answer

Add dropout layers to the network. The high training accuracy (99%) and low validation accuracy (65%) indicate overfitting, where the model memorizes the training data but fails to generalize. Adding dropout layers randomly drops neurons during training, which forces the network to learn more robust features and reduces overfitting. This technique is specifically designed to improve generalization without requiring more data or altering the learning rate.

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Apply batch normalization

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Increase the number of training epochs

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Increase the learning rate

Question 13

A company wants to use AI to automatically categorize customer support tickets into topics like 'billing', 'technical', 'account'. They have 10,000 labeled examples. Which algorithm is most suitable for this task?

Accepted Answer

Logistic regression. Logistic regression is a supervised learning algorithm that models the probability of a categorical outcome based on input features. With 10,000 labeled examples, it can efficiently learn decision boundaries to classify tickets into 'billing', 'technical', or 'account' by using a softmax (multinomial logistic regression) extension for multi-class classification.

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DBSCAN

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Apriori

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Principal component analysis (PCA)

Question 14

A machine learning team notices that their model's performance degrades when deployed to a new geographic region. The data distribution in the new region differs from the training data. Which concept best describes this issue?

Accepted Answer

Covariate shift. Covariate shift occurs when the distribution of the input features (covariates) changes between training and deployment, while the conditional relationship P(Y|X) remains the same. In this scenario, the model's performance degrades because the new geographic region has a different data distribution than the training data, which is the classic definition of covariate shift. This is a common issue in machine learning when models are deployed in environments not represented in the training set.

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Data leakage

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Underfitting

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Overfitting

Question 15

A team is building a natural language processing (NLP) model to analyze customer feedback. They have a large corpus of unlabeled text data and want to generate word embeddings that capture semantic meaning. Which approach should they use?

Accepted Answer

Word2Vec. Word2Vec is the correct approach because it learns dense, distributed word embeddings from large unlabeled corpora by training a shallow neural network to predict words in context (CBOW) or context from words (Skip-gram). This captures semantic relationships such as analogy and similarity, which is essential for analyzing customer feedback without labeled data.

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One-hot encoding

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TF-IDF vectorization

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Bag-of-words model