AI0-001 Practice Test 29 — 15 Questions

Question 1

A security team is evaluating the risk of adversarial examples against their image classification system. Which of the following BEST describes an adversarial example?

Accepted Answer

An input crafted with small, intentional perturbations that cause the model to output an incorrect prediction. An adversarial example is specifically an input that has been deliberately modified with small, often imperceptible perturbations to cause a machine learning model to misclassify it. This exploits the model's sensitivity to high-dimensional input spaces, where tiny changes in pixel values can shift the decision boundary without altering human perception of the image.

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A technique that reconstructs training data from the model's outputs

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An attack that injects malicious data into the training set to corrupt the model

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A method to determine if a specific data point was used in the training set

Question 2

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. Retrieval-Augmented Generation (RAG) is the most appropriate approach because it allows the chatbot to answer questions by retrieving relevant chunks from the policy documents stored in a vector store, without requiring model retraining. When documents are updated monthly, RAG simply re-indexes the new content, keeping the system current while avoiding the cost and complexity of fine-tuning or retraining a model each cycle.

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Train a custom model from scratch on the policy documents each month

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Use a larger foundation model with a longer context window and paste all documents into each prompt

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Fine-tune a base LLM on the policy documents monthly

Question 3

An organization is deploying an AI model on edge devices with limited computational resources. Which model optimization technique is most appropriate?

Accepted Answer

Apply model quantization. Model quantization reduces the precision of the model's weights and activations (e.g., from 32-bit floating point to 8-bit integer), which significantly decreases memory footprint and computational requirements. This makes it ideal for deployment on edge devices with limited resources, as it enables faster inference with minimal accuracy loss.

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Perform additional feature engineering

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Use an ensemble of models

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Increase the training dataset size

Question 4

A startup has developed a natural language processing model for sentiment analysis. Their CI/CD pipeline includes a step that runs unit tests on the model's output format and a validation step that checks accuracy on a static test dataset. Recently, the pipeline often fails during the validation step, but the failures are inconsistent—sometimes the same model version passes, sometimes fails. The team suspects the test dataset is small and randomly sampled. They need a reliable validation process to deploy models with confidence. Which approach should the team implement?

Accepted Answer

Fix the test dataset to be larger and more representative, and use a statistical test to compare against baseline. The core issue is that the static test dataset is too small and randomly sampled, leading to inconsistent validation results. By fixing the dataset to be larger and more representative, and using a statistical test (e.g., a paired t-test or McNemar's test) to compare the model's accuracy against a baseline, the team can reliably determine if performance changes are statistically significant, eliminating the randomness that causes pipeline failures to be inconsistent.

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Replace the static test set with k-fold cross-validation in each pipeline run

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Increase the accuracy threshold to 95% so only very good models pass

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Remove the validation step and rely on unit tests only

Question 5

A healthcare startup is developing a diagnostic system using medical images. The team has collected 10,000 labeled images of skin lesions. They plan to train a convolutional neural network (CNN) from scratch. However, training converges slowly, and the validation accuracy plateaus at 70%. The data scientist suspects overfitting. The dataset contains 8,000 images of benign lesions and 2,000 of malignant. The team has limited GPU resources. Which of the following is the MOST effective course of action to improve validation accuracy? A. Reduce the number of convolutional layers. B. Apply transfer learning using a pre-trained model on ImageNet. C. Increase the learning rate by a factor of 10. D. Add more dropout after every convolutional layer.

Accepted Answer

Apply transfer learning using a pre-trained model on ImageNet.. Transfer learning leverages a model pre-trained on a large dataset (e.g., ImageNet), which provides useful features for medical images and reduces the need for large amounts of data and computational resources. It is particularly effective when the dataset is small and imbalanced. Option A (increasing learning rate) might cause divergence or overshoot minima. Option B (reducing layers) may reduce capacity and underfit. Option C (adding dropout) can help with overfitting but is unlikely to jump from 70% to a significantly higher accuracy given limited data; transfer learning provides a stronger boost.

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Increase the learning rate by a factor of 10.

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Reduce the number of convolutional layers.

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Add more dropout after every convolutional layer.

Question 6

A team is developing a natural language processing model to classify customer feedback. The dataset contains text in multiple languages. Which THREE preprocessing steps are essential to ensure the model performs well across all languages?

Accepted Answer

Lowercasing. Lowercasing is essential because it normalizes text across languages by converting all characters to the same case, reducing vocabulary size and ensuring that words like 'Good' and 'good' are treated identically. This prevents the model from learning separate representations for case variations, which is critical for multilingual datasets where case usage may differ (e.g., German capitalizes nouns). Without lowercasing, the model's performance degrades due to sparsity and increased feature space.

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One-hot encoding

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Stemming

Question 7

A data scientist trains a regression model and notices the training loss is low but validation loss is high. Which technique should be applied FIRST to address this issue?

Accepted Answer

Apply L1 or L2 regularization to the model.. The scenario describes overfitting, where the model memorizes the training data but fails to generalize to unseen data. Applying L1 or L2 regularization (Option D) is the correct first step because it adds a penalty to the loss function for large weights, discouraging complexity and reducing overfitting without requiring additional data or architectural changes.

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Increase the learning rate.

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Add more layers to the neural network.

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Increase the size of the training dataset.

Question 8

An AI team is deploying a predictive maintenance model for industrial equipment. The model predicts failure within a 30-day window. The cost of a false positive is 10% of the cost of a false negative. Which evaluation metric should the team prioritize?

Accepted Answer

F2 score (beta=2) to prioritize recall over precision.. The F2 score (beta=2) weights recall four times more than precision, which is appropriate because a false negative (missing a failure) costs 10 times more than a false positive (unnecessary maintenance). Prioritizing recall ensures the model captures as many true failures as possible, minimizing the higher-cost error type.

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Area under the ROC curve (AUC-ROC) to measure overall discrimination.

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F1 score to balance precision and recall equally.

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Precision to minimize false positives.

Question 9

A healthcare startup deploys an AI model to predict patient readmission rates. An internal audit reveals that the model consistently underestimates readmission risk for non-native English speakers. According to AI ethics principles, what is the most appropriate course of action?

Accepted Answer

Retrain the model with a more representative dataset that includes diverse language backgrounds. It directly addresses the root cause of the bias: the training data lacks sufficient representation from non-native English speakers, leading to systematic underestimation of readmission risk for that group. Retraining with a more representative dataset aligns with the AI ethics principle of fairness by ensuring the model learns patterns across all demographic groups equally, rather than masking the issue with disclaimers or manipulating sample sizes.

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Add a confidence score disclaimer to model outputs

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Reduce the sample size of non-native English speakers to balance the dataset

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Continue using the model as is, since overall accuracy is acceptable

Question 10

A company wants to build a system that can generate new product images for an online catalog. Which TWO generative AI approaches are most suitable?

Accepted Answer

Diffusion models. Generative Adversarial Networks (GANs) are widely used for image generation, and diffusion models (like Stable Diffusion) have achieved state-of-the-art results in image synthesis. Variational autoencoders (VAEs) can generate images but often produce blurrier outputs. GPT is for text, and BERT is for understanding.

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Variational autoencoders (VAEs)

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BERT-based model

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GPT-style language model

Question 11

A team is deploying a fine-tuned LLM for generating code snippets. They want to test the system thoroughly before production. Which THREE testing types should they include in their test plan? (Select THREE)

Accepted Answer

Integration tests for API calls between the application and the LLM endpoint. Unit tests (E) validate data pipelines for training and inference data quality. Integration tests (A) verify that API calls between the application and the LLM endpoint function correctly. An evaluation framework (D) assesses LLM output quality, e.g., correctness or syntax using metrics like BLEU. Load testing (C) is for performance and scalability, not for testing correctness; regression testing (B) is important but typically performed after model updates, not as a primary testing type. Therefore, the three core testing types are integration tests, evaluation framework, and unit tests (A, D, E).

Answer

Regression testing only after model updates

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Load testing to determine maximum concurrent users

Question 12

An organization wants to detect if someone is trying to steal their proprietary machine learning model by querying its API. Which monitoring technique is MOST effective?

Accepted Answer

Monitoring for anomalous query patterns, such as high volume or systematic variations. Model extraction attacks rely on systematically querying the API to reconstruct the model's decision boundary. Monitoring for anomalous query patterns—such as high request volume, uniform input distributions, or systematic variations (e.g., grid-like sampling of feature space)—directly detects the behavioral signature of extraction attempts, unlike passive controls that do not address the attack vector.

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Output filtering to remove sensitive information from responses

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Rate limiting on the number of API requests per user

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Input validation to reject malformed requests

Question 13

An AI system for fraud detection shows a gradual decline in precision over several weeks, though recall remains stable. Which type of model drift is most likely occurring?

Accepted Answer

Concept drift. Concept drift occurs when the statistical relationship between input features and the target variable changes over time, causing the model's decision boundary to become less accurate. In this scenario, precision is declining while recall remains stable, indicating that the model is producing more false positives even though it still catches the same proportion of true positives. This is a classic sign of concept drift, where the underlying definition of fraud has shifted, not the data distribution itself.

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Data drift

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Covariate shift

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Label drift

Question 14

A company wants to reduce the carbon footprint of training large AI models. Which practice is MOST effective for achieving 'Green AI'?

Accepted Answer

Prune the model to reduce its size before training. Green AI practices focus on reducing computational and environmental costs. Using model pruning reduces model size and computational requirements. Using more GPUs increases energy consumption. Training with larger datasets increases compute. Using older hardware is often less energy-efficient.

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Train on larger datasets to improve accuracy

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Use more powerful GPUs to speed up training

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Use older, less efficient hardware to save on manufacturing emissions

Question 15

A company is deploying a large language model for customer support. They want to reduce the number of off-topic or nonsensical responses while maintaining creativity. Which parameter adjustment would BEST achieve this?

Accepted Answer

Decrease temperature to 0.2. Lowering temperature makes the model more deterministic and less likely to produce random outputs. Top-p and top-k can also help but are secondary; temperature directly controls randomness.

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Set top-p to 0.1

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Increase top-k to 100

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Increase temperature to 0.9