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

Question 1

Which THREE are common machine learning algorithms used for regression?

Accepted Answer

Linear regression. Linear regression is a fundamental supervised learning algorithm used for regression tasks, where the goal is to predict a continuous numeric output based on one or more input features. It models the relationship between the dependent and independent variables by fitting a linear equation to the observed data, making it a core algorithm for regression problems.

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Logistic regression

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K-means

Question 2

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 3

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 4

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 5

Refer to the exhibit. A system administrator reviews the deployment. Which action should be taken to meet the SLA?

Accepted Answer

Scale up the compute resources. The exhibit shows a deployment where inference latency exceeds the SLA requirement. Scaling up compute resources (e.g., adding more CPU cores, GPU memory, or increasing instance size) directly reduces per-request processing time by providing more parallel processing capacity, which is the most straightforward way to meet latency SLAs when the model is already optimized.

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Retrain the model

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Implement caching

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Reduce model input size

Question 6

A company is building a recommendation system for an e-commerce platform. They want the system to learn from user purchase history and browsing behavior to suggest products. Which type of machine learning is most appropriate for this task?

Accepted Answer

Unsupervised learning. Unsupervised learning is the most appropriate because the system must discover hidden patterns and groupings in user purchase history and browsing behavior without labeled outcomes. Recommendation systems often use clustering or association rule mining (e.g., market basket analysis) to identify product affinities and user segments, which are core unsupervised techniques. This allows the system to suggest products based on learned co-occurrence patterns rather than predefined categories.

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Supervised learning

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Semi-supervised learning

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Transfer learning

Question 7

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 8

An AI system is deployed to detect fraudulent transactions. The system flags 5% of transactions as fraudulent, but the actual fraud rate is 0.1%. The business sees many false positives and wants to reduce them without significantly increasing false negatives. Which metric should be prioritized for optimization?

Accepted Answer

F1 score. The F1 score balances precision and recall, making it ideal when false positives are costly but false negatives must not increase significantly. Optimizing precision alone would reduce false positives but could increase false negatives, while recall alone would not address the false positive problem. The F1 score ensures both metrics are jointly optimized, aligning with the business requirement.

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Recall

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Accuracy

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Precision

Question 9

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 10

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

Question 11

A healthcare provider wants to use AI to predict patient readmission risk. They have structured data (age, diagnosis, lab results) and unstructured clinical notes. Which approach is most appropriate?

Accepted Answer

Multimodal model combining structured and text embeddings. The scenario involves both structured data (age, diagnosis, lab results) and unstructured clinical notes. A multimodal model can process both types by combining embeddings from text (e.g., via a transformer or RNN) with structured features, enabling the model to learn cross-modal patterns that improve readmission risk prediction. This approach leverages the complementary strengths of structured and unstructured data, which is essential for capturing the full clinical picture.

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Convolutional neural network (CNN) on clinical notes

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Recurrent neural network (RNN) on structured data

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Logistic regression on structured data only

Question 12

An AI engineer is tuning a deep learning model and observes that the training loss decreases very slowly. The learning rate is set to 0.001. Which adjustment is most likely to speed up convergence?

Accepted Answer

Increase the learning rate to 0.01. A learning rate of 0.001 is causing the model to take very small steps toward the minimum of the loss function, resulting in slow convergence. Increasing the learning rate to 0.01 allows larger weight updates per iteration, which typically speeds up training. However, care must be taken not to overshoot the optimum, as an excessively high learning rate can cause divergence.

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Add more hidden layers

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Decrease the learning rate to 0.0001

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Increase the batch size

Question 13

A company develops an AI model that recommends job candidates. The model inadvertently discriminates against a protected group. Which approach is most effective for mitigating this bias?

Accepted Answer

Use a fairness-aware machine learning algorithm. Fairness-aware machine learning algorithms explicitly incorporate fairness constraints or objectives during model training, directly addressing and mitigating bias against protected groups. Unlike simple removal of protected attributes, these algorithms can detect and correct for proxy discrimination and disparate impact, ensuring the model's recommendations are equitable by design.

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Remove the protected attribute from the training data

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Analyze model predictions after deployment

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Collect more training data from the protected group

Question 14

Which TWO of the following are common activation functions used in neural networks? (Choose two.)

Accepted Answer

ReLU. ReLU (Rectified Linear Unit) is a widely used activation function that outputs the input directly if it is positive, and zero otherwise, introducing non-linearity while mitigating the vanishing gradient problem. Sigmoid is another common activation function that maps any real-valued input to a value between 0 and 1, making it useful for binary classification output layers. Both are fundamental building blocks in neural network architectures.

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Gradient descent

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LSTM

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Dropout

Question 15

Which THREE of the following are types of machine learning paradigms? (Choose three.)

Accepted Answer

Reinforcement learning. Reinforcement learning is a correct machine learning paradigm where an agent learns to make decisions by interacting with an environment, receiving rewards or penalties based on its actions. This trial-and-error approach is distinct from supervised and unsupervised learning, as it focuses on maximizing cumulative reward through exploration and exploitation.

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Gradient boosting

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Quantum computing