AI0-001 Practice Test 11 — 15 Questions

Question 1

A company deploys a computer vision model for quality inspection on a manufacturing line. After deployment, the model's accuracy drops from 95% to 80% over two weeks. Which action is most likely to address this issue?

Accepted Answer

Retrain the model using recently collected production data.. Option B is correct because data drift is a common cause of performance degradation over time, and retraining with recent data realigns the model. Option A is wrong because increasing threshold may reduce false positives but does not address drift. Option C is wrong because adjusting learning rate is irrelevant for inference. Option D is wrong because adding redundant models increases complexity without solving drift.

Answer

Increase the confidence threshold for predictions.

Answer

Decrease the learning rate of the training algorithm.

Answer

Deploy an additional ensemble of models for redundancy.

Question 2

A data scientist fine-tunes a large language model for a legal document summarization task. After fine-tuning, the model performs well on test data but produces summaries that include hallucinated legal clauses. Which mitigation strategy is most effective?

Accepted Answer

Implement retrieval-augmented generation (RAG) to provide factual context.. Option A is correct because RAG grounds the model in retrieved documents, reducing hallucinations. Option B is wrong as temperature affects creativity but does not eliminate hallucinations. Option C is wrong because it reduces output length but may not prevent false content. Option D is wrong because it does not address the model's tendency to fabricate.

Answer

Use a different tokenizer during fine-tuning.

Answer

Decrease the temperature parameter to 0.1 during inference.

Answer

Set a maximum token limit of 50 for each summary.

Question 3

A healthcare organization deploys an AI system to analyze medical images and detect anomalies. During a routine audit, the security team discovers that the AI model occasionally returns results that include data from patients who have opted out of data sharing. Which security control should be implemented to prevent this violation?

Accepted Answer

Apply data anonymization techniques to the training dataset.. Option B is correct because data anonymization ensures that patient identities are removed from training data, preventing re-identification of opt-out patients. Option A is incorrect because access control does not address data already in the model. Option C is incorrect because encryption protects data in transit/rest but does not prevent data leakage from model outputs. Option D is incorrect because differential privacy adds noise to queries but does not directly remove specific patient data from model results.

Answer

Implement role-based access control (RBAC) on the AI model's inference API.

Answer

Use differential privacy during model training.

Answer

Encrypt the training data at rest and in transit.

Question 4

A company uses a machine learning model to recommend products to customers. The marketing team notices that the model is recommending high-profit items more frequently than low-profit items, even when customers are likely to prefer the latter. This behavior is causing customer dissatisfaction. Which approach would best align the model with customer preferences while maintaining profitability?

Accepted Answer

Use a multi-objective optimization framework to balance profit and customer satisfaction.. Option D is correct because multi-objective optimization allows the model to balance multiple goals (e.g., profit and customer satisfaction) explicitly. Option A is incorrect because it still prioritizes profit, which may not address satisfaction. Option B is incorrect because it completely removes profit, which may harm business goals. Option C is incorrect because it only adjusts profit weights without true multi-objective framework.

Answer

Train the model with a loss function that weights profit more heavily than customer satisfaction.

Answer

Adjust the model's hyperparameters to reduce the influence of profit features.

Answer

Remove profit data from the training set and only use customer preference data.

Question 5

A company is developing an AI chatbot for customer service. The legal team is concerned that the chatbot might generate responses that violate privacy regulations. Which governance mechanism should be implemented to mitigate this risk?

Accepted Answer

Implement a human-in-the-loop review process for high-risk responses.. Option C is correct because a human-in-the-loop (HITL) review process directly addresses the risk of privacy violations by ensuring that high-risk responses are reviewed by a human before being sent to the customer. This governance mechanism provides a safety net for unpredictable outputs from the generative AI model, which may inadvertently leak personally identifiable information (PII) or violate data protection regulations like GDPR or CCPA. Unlike technical controls that only reduce the attack surface, HITL offers real-time compliance oversight for the chatbot's natural language generation (NLG) outputs.

Answer

Use explainable AI techniques to understand why the chatbot generates certain responses.

Answer

Encrypt all chatbot conversations at rest and in transit.

Answer

Anonymize the training data used to train the chatbot.

Question 6

A healthcare organization is deploying an AI system to analyze patient records and recommend treatment plans. To comply with data privacy regulations, what is the most important security measure to implement?

Accepted Answer

Anonymize patient data before processing. Anonymizing patient data before processing is the most important security measure because it directly addresses data privacy regulations like HIPAA and GDPR by removing personally identifiable information (PII) from the dataset. This ensures that even if a breach occurs, the data cannot be linked back to an individual, thereby minimizing compliance risk. While other measures like encryption and access control are essential, anonymization is the foundational step for lawful AI processing of sensitive health data.

Answer

Enable detailed audit logging

Answer

Encrypt all data at rest and in transit

Answer

Implement role-based access control

Question 7

A financial institution uses an AI model to approve loan applications. The model was trained on historical data that included biased lending practices. The bank's ethics committee wants to mitigate bias without removing protected attributes. Which approach best balances fairness and model performance?

Accepted Answer

Apply adversarial debiasing during training. Adversarial debiasing is the best approach because it directly optimizes the model to reduce bias during training while preserving predictive accuracy. It uses an adversarial network that tries to predict the protected attribute from the model's predictions, forcing the main model to learn representations that are less correlated with that attribute. This allows the bank to keep protected attributes in the data (as required by the ethics committee) while actively mitigating bias.

Answer

Retrain the model using a balanced dataset

Answer

Remove all protected attributes from the training data

Answer

Post-process model outputs to adjust for demographic parity

Question 8

A retail company wants to build a model to predict customer churn based on purchase history and demographics. The dataset includes categorical features like region and gender, and numerical features like total spend. What is the best initial step before training the model?

Accepted Answer

One-hot encode categorical variables and normalize numerical variables. One-hot encoding categorical variables and normalizing numerical variables is standard preprocessing to convert categorical data into numeric format and scale features, which many algorithms require for optimal performance.

Answer

Train a deep neural network directly on raw data

Answer

Remove all categorical features to simplify the model

Answer

Perform principal component analysis (PCA) on all features

Question 9

A self-driving car company is developing an object detection system using a convolutional neural network (CNN). The system needs to detect pedestrians and vehicles in real-time with high accuracy. Which technique can reduce inference time while maintaining accuracy?

Accepted Answer

Apply model pruning and quantization. Model pruning removes redundant or less important weights from the CNN, reducing computational load, while quantization converts floating-point weights to lower-precision integers (e.g., INT8). Together, they shrink model size and speed up inference without significantly degrading accuracy, making them ideal for real-time object detection in resource-constrained environments like autonomous vehicles.

Answer

Use a pre-trained model and fine-tune it

Answer

Add more convolutional layers

Answer

Increase number of filters in each layer

Question 10

A data analyst needs to select two appropriate unsupervised learning techniques for clustering unlabeled data. (Choose two.)

Accepted Answer

Hierarchical clustering. K-means and hierarchical clustering are both unsupervised learning algorithms for clustering data into groups without labels.

Answer

Linear regression

Answer

Support vector machine

Answer

Decision tree

Question 11

An AI model is being developed for medical diagnosis from X-ray images. The dataset contains only frontal chest X-rays. The model achieves high accuracy on test set but fails on lateral views. What is the most likely cause?

Accepted Answer

Dataset bias. The model was trained only on frontal views, so it did not learn features from lateral views, resulting in dataset bias and poor generalization to unseen perspectives.

Answer

Underfitting

Answer

Label noise

Answer

Overfitting

Question 12

A data scientist notices the model overfits. Which change to the exhibit's configuration would most likely reduce overfitting?

Accepted Answer

Add L2 regularization to dense layers. Adding L2 regularization to dense layers penalizes large weights by adding a squared magnitude term to the loss function, which forces the model to learn simpler patterns and reduces overfitting. This directly addresses the core issue of the model memorizing noise in the training data.

Answer

Remove dropout layers

Answer

Increase learning rate to 0.01

Answer

Increase units in the first dense layer to 512

Question 13

A financial institution uses a regression model to predict credit risk. The model has a high R-squared on training data but low R-squared on test data. Which of the following is the most likely cause?

Accepted Answer

The model is overfitting the training data.. A high R-squared on training data combined with a low R-squared on test data is the classic symptom of overfitting. The model has memorized noise and specific patterns in the training set rather than learning generalizable relationships, causing poor performance on unseen data.

Answer

The features were not standardized before training.

Answer

The model is underfitting the training data.

Answer

There is multicollinearity among the input features.

Question 14

A data scientist is training a model to classify customer support tickets into categories. The dataset has 10,000 labeled examples, but the 'billing' category contains 8,000 examples while the 'technical' category contains 2,000. Which technique is most appropriate to address this imbalance before training?

Accepted Answer

Apply random oversampling on the 'technical' category.. Option A is correct because random oversampling duplicates examples from the minority class ('technical') to balance the class distribution, preventing the model from becoming biased toward the majority class ('billing'). This technique directly addresses the class imbalance before training, which is critical for classification tasks where the minority class is underrepresented.

Answer

Remove all examples except 'billing' and use a one-class classifier.

Answer

Use accuracy as the only evaluation metric.

Answer

Train the model as is, then adjust thresholds post-training.

Question 15

A company is implementing an AI solution for fraud detection. The dataset is highly imbalanced (only 1% fraudulent transactions). Which THREE techniques are most appropriate to address class imbalance? (Select three.)

Accepted Answer

Apply cost-sensitive learning by assigning a higher misclassification cost to the minority class.. Option A is correct because cost-sensitive learning directly addresses class imbalance by assigning a higher misclassification cost to the minority class (fraudulent transactions). This forces the model to penalize false negatives more heavily, thereby improving recall for the minority class without altering the dataset distribution.

Answer

Reduce the number of features using principal component analysis (PCA).

Answer

Use accuracy as the primary evaluation metric.