AI0-001 Practice Test 30 — 15 Questions

Question 1

A financial institution is implementing an AI-based fraud detection system. The compliance officer is concerned about potential bias in the model that could lead to unfair treatment of certain customer groups. Which governance practice should be prioritized to address this concern?

Accepted Answer

Schedule regular bias audits using fairness metrics.. Regular bias audits using fairness metrics (Option B) are the correct governance practice because they provide a systematic, quantitative method to detect and measure disparate impact across protected groups. Unlike simply collecting more data, audits directly evaluate model outputs for statistical parity, equal opportunity, or other fairness definitions, enabling the institution to identify and remediate bias proactively. This aligns with regulatory expectations for ongoing monitoring and accountability in AI governance.

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Increase the diversity of the training data by collecting more samples from underrepresented groups.

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Retrain the model every month with the latest transaction data.

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Use SHAP values to provide explanations for each prediction.

Question 2

Which TWO of the following are effective techniques to detect data poisoning attacks in a training dataset?

Accepted Answer

Performing cross-validation to check for inconsistent model performance.. Option A is correct because cross-validation can reveal data poisoning by exposing inconsistent model performance across folds. If a poisoned subset causes the model to perform well on certain folds but poorly on others, it indicates that the training data may have been tampered with, as the model's behavior becomes unstable due to maliciously injected samples.

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Normalizing features to zero mean and unit variance.

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Using ensemble methods like random forest for training.

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Applying PCA to reduce dimensionality.

Question 3

A company wants to deploy a chatbot that uses natural language understanding (NLU) to answer customer queries. Which AI technique is most suitable for understanding the intent of user input?

Accepted Answer

Sequence-to-sequence model with attention. Option C is correct because sequence-to-sequence models with attention are specifically designed to handle variable-length input sequences (like user queries) and map them to output sequences (like intent labels or responses). The attention mechanism allows the model to focus on the most relevant parts of the input when determining intent, which is critical for understanding nuanced or long user queries in NLU tasks.

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K-means clustering

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Linear regression

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Decision tree

Question 4

A data scientist trains a deep neural network for image classification. The training loss decreases but validation loss starts increasing after 50 epochs. What should the data scientist do to improve generalization?

Accepted Answer

Apply dropout and early stopping. The increasing validation loss while training loss decreases is a classic sign of overfitting. Dropout randomly deactivates neurons during training, which prevents co-adaptation and forces the network to learn more robust features. Early stopping halts training when validation performance stops improving, directly addressing the overfitting by selecting the model with the best generalization before it degrades.

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Decrease batch size

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Add more hidden layers

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Increase learning rate

Question 5

A marketing team wants to segment customers into groups based on purchasing behavior without predefined categories. Which algorithm should they use?

Accepted Answer

K-means clustering. K-means clustering is an unsupervised learning algorithm that groups data points into clusters based on similarity without requiring predefined labels. Since the marketing team wants to segment customers based on purchasing behavior without predefined categories, K-means is the correct choice as it discovers natural groupings in the data.

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Naive Bayes classifier

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Logistic regression

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Support vector machine

Question 6

A team is training a deep learning model for natural language processing using a large corpus. They notice the model has a very high number of parameters and training is slow. Which technique can reduce the number of parameters without significant performance loss?

Accepted Answer

Apply embedding compression. Embedding compression reduces the dimensionality of the embedding layer, which often contains the majority of the model's parameters in NLP tasks. By using techniques like low-rank factorization or pruning, the model retains most of its representational power while significantly decreasing the parameter count and training time.

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Add more dropout layers

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Use a larger batch size

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Increase learning rate

Question 7

When evaluating a binary classification model, which two metrics are most appropriate for imbalanced datasets? (Choose two.)

Accepted Answer

Recall. Recall (Option C) is correct because it measures the proportion of actual positive cases correctly identified, which is critical in imbalanced datasets where the minority class is of primary interest. Precision (Option E) is correct because it measures the accuracy of positive predictions, helping to avoid false positives when the positive class is rare. Together, recall and precision provide a balanced view of model performance on the minority class, unlike accuracy which can be misleadingly high by simply predicting the majority class.

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Accuracy

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Mean absolute error

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R-squared

Question 8

Based on the exhibit, what is the most likely issue with the model training?

Accepted Answer

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

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Vanishing gradient

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Learning rate too high

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Underfitting

Question 9

A research team is training a deep neural network for image classification. The training loss decreases rapidly for the first few epochs but then plateaus, while validation loss starts to increase after epoch 10. Which action would best address this issue?

Accepted Answer

Implement early stopping based on validation loss to prevent further overfitting.. The training loss decreasing rapidly then plateauing while validation loss increases after epoch 10 is a classic sign of overfitting. Early stopping monitors validation loss and halts training when it begins to rise, preventing the model from memorizing noise in the training data. This directly addresses the overfitting issue without requiring architectural or hyperparameter changes that could destabilize training.

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Reduce the batch size to introduce more noise during training.

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Increase the learning rate to help the model escape the plateau.

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Add more convolutional layers to increase model capacity.

Question 10

An AI system is being developed to diagnose diseases from medical images. The model achieves 99% accuracy on the test set, but when deployed in a different hospital, performance drops significantly. Which of the following is the MOST likely cause?

Accepted Answer

The training data does not represent the new hospital's population or imaging equipment.. The model's high accuracy on the test set but poor performance in a different hospital indicates a distribution shift between the training data and the deployment environment. This is a classic case of dataset shift, where the training data does not represent the new hospital's patient population or imaging equipment, leading to degraded model generalization.

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The model is being attacked by adversarial examples.

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The model is overfitted to the training data.

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Data leakage occurred during preprocessing.

Question 11

A data scientist is preparing a dataset for a classification task. The dataset contains 10,000 rows and 50 features, but many features have missing values. Which approach should the scientist take first to address the missing data?

Accepted Answer

Analyze the pattern and proportion of missing values to choose an appropriate imputation strategy.. Option B is correct because the first step in handling missing data is to understand the pattern and proportion of missingness (e.g., MCAR, MAR, MNAR) to select an appropriate imputation method. Blindly applying imputation or deletion without analysis can introduce bias or reduce model performance. This diagnostic step ensures the chosen strategy aligns with the data's underlying structure and the classification task's requirements.

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Use a deep learning model to predict missing values without preprocessing.

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Remove all rows with any missing values to ensure a clean dataset.

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Replace missing values with the mean of each feature immediately.

Question 12

An organization is developing an AI system to approve loan applications. They want to ensure the model does not discriminate based on race or gender. Which technique BEST addresses this concern?

Accepted Answer

Apply adversarial debiasing during model training.. Adversarial debiasing is a technique that explicitly trains the model to remove sensitive information (like race or gender) from its internal representations, preventing the model from learning discriminatory patterns even if correlated features remain. This directly addresses fairness by making the model's predictions independent of protected attributes, which is more robust than simply removing features (which can still allow proxy discrimination).

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Remove race and gender features from the training data.

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Use a more complex model to capture nuances.

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Collect more training data from diverse populations.

Question 13

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. Option D is correct because 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 14

A company built a speech-to-text model using a recurrent neural network (RNN). During deployment, the model performs poorly on accented speech. Which action would most effectively improve model robustness?

Accepted Answer

Augment the training dataset with various accented audio samples and retrain the model.. Option C is correct because augmenting the training dataset with diverse accented audio samples directly addresses the root cause of poor performance—distribution shift between training and deployment data. Retraining the model on this enriched dataset allows the RNN to learn invariant features across accents, improving generalization without altering the model architecture or risking catastrophic forgetting from fine-tuning on a tiny sample.

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Collect a small sample of accented speech and fine-tune the model on that sample only.

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Add dropout and reduce the number of RNN layers to prevent overfitting to the current data.

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Replace the RNN with a convolutional neural network (CNN) for feature extraction.

Question 15

Refer to the exhibit. The data scientist notices that the model achieves 98% accuracy on the training set but only 72% on the test set. Which change to the model parameters is most likely to reduce this gap?

Accepted Answer

Reduce max_depth to 3.. The model is overfitting: 98% training accuracy vs. 72% test accuracy. Reducing max_depth to 3 limits the depth of each decision tree, preventing them from memorizing noise and forcing them to learn more generalizable patterns. This is a standard regularization technique for tree-based ensembles.

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Increase n_estimators to 500.

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Set max_depth to None to allow trees to grow fully.

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Switch from RandomForest to a linear model like LogisticRegression.