AI0-001 AI Concepts and Foundations Practice Test 7 — 15 Questions

Question 1

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.

Answer

Convolutional neural network (CNN) on clinical notes

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Recurrent neural network (RNN) on structured data

Answer

Logistic regression on structured data only

Question 2

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.

Answer

Add more hidden layers

Answer

Decrease the learning rate to 0.0001

Answer

Increase the batch size

Question 3

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.

Answer

Remove the protected attribute from the training data

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Analyze model predictions after deployment

Answer

Collect more training data from the protected group

Question 4

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.

Answer

Gradient descent

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LSTM

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Dropout

Question 5

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.

Answer

Gradient boosting

Answer

Quantum computing

Question 6

Which TWO of the following are techniques used for reducing overfitting in neural networks? (Choose two.)

Accepted Answer

Dropout. Dropout (A) is correct because it randomly deactivates a fraction of neurons during each training iteration, which prevents the network from relying on specific neurons and forces it to learn more robust, generalizable features, thereby reducing overfitting. L2 regularization (C) is correct because it adds a penalty term proportional to the squared magnitude of the weights to the loss function, discouraging large weights and constraining model complexity to improve generalization. Boosting (B) is an ensemble meta-algorithm that combines weak learners to reduce bias, not a neural-network overfitting-reduction technique. Increasing the learning rate (D) typically causes unstable training or divergence rather than reducing overfitting. Increasing the number of hidden layers (E) raises model capacity and usually worsens overfitting rather than mitigating it.

Answer

Boosting

Answer

Increasing the learning rate

Answer

Increasing the number of hidden layers

Question 7

A startup is building a chatbot for customer service. They have 500 recorded conversations and want to use a pre-trained language model to generate responses. However, they have limited computational resources and need the chatbot to respond in real-time. They are considering fine-tuning a large model like GPT-3 or using a smaller model like DistilBERT. The conversation data contains industry-specific jargon. Which approach should they take?

Accepted Answer

Fine-tune DistilBERT on the conversation data. Fine-tuning DistilBERT on the 500 recorded conversations allows the model to adapt to industry-specific jargon while maintaining real-time responsiveness due to its smaller size. DistilBERT is a distilled version of BERT that retains 97% of BERT’s language understanding with 40% fewer parameters, making it suitable for limited computational resources. Fine-tuning on domain-specific data is essential here, as pre-trained models like GPT-3 lack exposure to the startup’s specialized terminology, and using a smaller model ensures low-latency inference for real-time chatbot responses.

Answer

Use GPT-3 via API without fine-tuning

Answer

Train a custom RNN from scratch on the conversations

Answer

Implement a rule-based system with keywords

Question 8

A hospital uses an AI system to predict patient deterioration from vital signs. The system currently uses a logistic regression model trained on data from the past year. Recently, the hospital adopted a new patient monitoring device that provides more accurate readings. The model's performance has dropped significantly. The data science team has access to the new device's data for the past month and wants to improve the model with minimal disruption. The team also wants to ensure the model remains interpretable for regulatory compliance. Which approach should they take?

Accepted Answer

Retrain the logistic regression model on a combined dataset of old and new device data. Retraining the logistic regression model on a combined dataset of old and new device data is the best approach because it leverages all available data to adapt the model to the new device's measurement distribution while preserving the model's inherent interpretability. Logistic regression is a linear model that remains fully transparent for regulatory compliance, and combining both datasets helps the model learn the systematic shift in vital sign readings without discarding valuable historical patterns. This minimizes disruption by avoiding a complete overhaul and directly addresses the performance drop caused by the change in input data distribution.

Answer

Continue using the current model and manually adjust predictions based on device differences

Answer

Build an ensemble of logistic regression and a neural network using new data only

Answer

Replace the logistic regression model with a gradient boosting model using only new device data

Question 9

An organization is deploying a deep learning model in production. Which THREE components are essential for maintaining model performance over time?

Accepted Answer

Performance monitoring. Performance monitoring (A) is essential because it provides continuous visibility into model metrics such as accuracy, latency, and throughput, enabling early detection of degradation. Without ongoing monitoring, teams cannot identify when a model's predictions deviate from expected behavior, which is critical for maintaining reliability in production.

Answer

Hyperparameter tuning

Answer

Feature importance analysis

Question 10

A government agency is deploying an AI model to screen loan applications. The model uses features like income, credit score, employment history, and zip code. During fairness auditing, the model is found to deny a disproportionately high number of applicants from a particular demographic group, even when controlling for legitimate financial factors. The agency wants to mitigate this bias without significantly reducing overall accuracy. Which approach should the data scientist prioritize?

Accepted Answer

Use adversarial debiasing during model training. Adversarial debiasing is the correct approach because it directly optimizes the model to remove sensitive information (e.g., demographic group membership) from its internal representations while preserving predictive accuracy. This technique trains a primary model to predict the target (loan approval) and an adversary to predict the protected attribute from the model's learned features, forcing the primary model to learn representations that are both accurate and unbiased. It addresses the root cause of bias—correlation between protected attributes and model predictions—without requiring post-hoc threshold adjustments or sacrificing overall performance.

Answer

Adjust the decision threshold for the affected group

Answer

Remove the zip code feature from the model

Answer

Apply sample weighting to balance the demographic groups

Question 11

A cybersecurity firm is developing an AI system to detect zero-day malware using behavior analysis. The team collects a dataset of 1,000 malware samples and 10,000 benign files from corporate endpoints. The model is a random forest classifier. After deployment, the false positive rate is 5%, which is acceptable, but the detection rate for new malware variants drops to 30%. The security analyst suspects the model is overfitting to the specific malware families in the training set. Which improvement should the team implement first?

Accepted Answer

Engineer features that capture generic behavioral patterns. The core issue is that the model has overfitted to the specific malware families in the training set, causing poor generalization to unseen zero-day variants. Engineering features that capture generic behavioral patterns (e.g., API call sequences, file system interactions, network connection anomalies) reduces reliance on family-specific signatures, improving detection of novel malware. This directly addresses the root cause of the 30% detection rate drop without introducing new model complexity or data imbalance issues.

Answer

Use a boosting ensemble instead of bagging

Answer

Collect more malware samples from the same families

Answer

Replace the random forest with a deep neural network

Question 12

Refer to the exhibit. The training log shows loss and accuracy for a binary classification model. What is the most likely issue with this model?

Accepted Answer

Overfitting. The training log shows that training loss continues to decrease while validation loss increases after a certain point, and training accuracy approaches 100% while validation accuracy plateaus or drops. This divergence is the classic signature of overfitting, where the model memorizes noise in the training data rather than learning generalizable patterns.

Answer

Insufficient epochs

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Underfitting

Answer

Data leakage

Question 13

A retail company deploys a machine learning model to predict customer churn. The model outputs a probability between 0 and 1, and churn is predicted if probability > 0.5. After deployment, the model has a high false positive rate (many non-churning customers labeled as churn), which leads to unnecessary retention offers and increased costs. The data science team confirms the model was trained on historical data with a balanced class distribution. The business team wants to reduce false positives while maintaining a reasonable true positive rate. However, they cannot retrain the model because the original training data is no longer available. What is the best course of action to reduce false positives?

Accepted Answer

Increase the decision threshold to a higher value, such as 0.7.. Increasing the decision threshold to a higher value, such as 0.7, reduces false positives because the model will only predict churn when it is more confident. Since the model cannot be retrained, adjusting the threshold is the only way to trade off between precision and recall without modifying the model itself.

Answer

Retrain the model using only the most recent three months of data.

Answer

Collect new labeled data and perform transfer learning from the original model.

Answer

Decrease the decision threshold to a lower value, such as 0.3.

Question 14

A hospital wants to deploy an AI system that analyzes chest X-rays to detect pneumonia. The radiology team insists that the system provide a confidence score alongside each diagnosis so they can decide whether to trust the output. Which AI concept are they primarily concerned with?

Accepted Answer

Model confidence calibration. Confidence calibration ensures that the probability a model assigns to its prediction reflects the true likelihood of correctness. In medical imaging, calibrated confidence scores allow clinicians to set actionable thresholds and decide when to rely on the AI. Explainability and interpretability address why a decision was made, while bias mitigation addresses fairness, not certainty.

Answer

Model bias mitigation

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Model explainability

Answer

Model interpretability

Question 15

A hospital wants an AI system to review chest X-ray images and flag those that may show pneumonia, but a radiologist will make the final diagnosis. The IT team must classify this system for documentation. Which category of AI best describes this deployment?

Accepted Answer

Narrow AI. The system performs one bounded task—identifying possible pneumonia in chest X-rays—so it is narrow AI. General AI implies human-level breadth across many domains, supervised learning describes how a model is trained rather than what the system is, and reactive machine is an outdated capability category that does not fit a trained deep learning model.

Answer

Supervised learning

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General AI

Answer

Reactive machine