AI0-001 Practice Test 12 — 15 Questions

Question 1

A hospital wants to train a diagnostic model using patient data from multiple hospitals without sharing raw patient records. Which technique enables collaborative model training while keeping data decentralised?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Federated learning. Federated learning enables multiple parties to collaboratively train a shared model without exchanging raw data. Each hospital trains a local model on its own patient records, and only model updates (e.g., gradients or weights) are sent to a central server for aggregation. This keeps sensitive data decentralised and reduces privacy risks, making it the correct choice for collaborative training across hospitals.

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Pseudonymisation

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Differential privacy

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Anonymisation

Question 2

A company implements a chatbot using a rule-based system. Users complain the chatbot cannot handle new queries. Which AI approach should be considered to improve flexibility?

Accepted Answer

Machine learning. Machine learning (ML) enables a chatbot to learn from new data and adapt to unseen queries, unlike a static rule-based system. By training on historical conversations, an ML model can generalize patterns and handle novel inputs without requiring explicit rules for every scenario.

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Expert system

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Natural language processing (NLP)

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Robotic process automation

Question 3

A team is using a cloud AI service with a pay-per-token pricing model. They want to minimize costs while maintaining response quality. Which strategy is MOST effective?

Accepted Answer

Use prompt caching for repeated query patterns. Prompt caching reduces costs by avoiding redundant token processing for repeated query patterns. The cloud AI service charges per token, so caching the prefix of frequent requests (e.g., system prompts or common context) means only the new, unique tokens are billed, directly lowering expenditure without sacrificing response quality.

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Switch to a smaller, less capable model

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Increase the batch size for API calls

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Reduce the model's max_tokens to a very low value

Question 4

A data scientist needs to explain why a specific loan application was rejected by a tree-based model. The model is complex and not inherently interpretable. Which method should the data scientist use to provide a local explanation for this single prediction?

Accepted Answer

LIME. LIME (Local Interpretable Model-agnostic Explanations) is the correct choice because it is specifically designed to provide local explanations for individual predictions by approximating the complex model with a simpler, interpretable surrogate model around that specific instance. For a tree-based model that is not inherently interpretable, LIME can explain why a single loan application was rejected by perturbing the input and observing the changes in predictions, making it ideal for this use case.

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SHAP values

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Model cards

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Attention visualization

Question 5

Refer to the exhibit. A team deploys a sentiment analysis model with this policy. After one month, the monitoring system triggers an alert for feature drift. Which action should the team take first?

Accepted Answer

Compare the current feature distributions with the training set to identify which features drifted.. When a monitoring system triggers an alert for feature drift, the first step is to diagnose which features have changed. Comparing current feature distributions with the training set identifies the specific features that drifted, enabling targeted remediation such as retraining with recent data or feature engineering. This aligns with the standard MLOps workflow for drift detection and response.

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Review the fairness check settings to ensure protected attributes are still relevant.

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Immediately retrain the model on recent data to adapt to the drift.

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Reduce the classification threshold to 0.5 to increase sensitivity.

Question 6

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 7

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 8

A company uses a large language model (LLM) to generate customer support responses. They notice the model sometimes produces harmful outputs. Which implementation strategy best reduces this risk while maintaining performance?

Accepted Answer

Fine-tune the model using reinforcement learning from human feedback. Reinforcement learning from human feedback (RLHF) directly trains the model to align its outputs with human preferences for safety and helpfulness, reducing harmful outputs while preserving performance. Unlike superficial filters or prompts, RLHF adjusts the model's internal behavior through reward modeling and policy optimization, making it the most effective strategy for sustained safety improvements.

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Implement a keyword-based output filter

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Use a smaller, less capable model

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Add system prompts instructing the model to be safe

Question 9

A data scientist is building a classification model to detect fraudulent transactions. The dataset is highly imbalanced with only 1% fraudulent cases. Which approach should the scientist use to evaluate model performance most effectively?

Accepted Answer

F1 score. In highly imbalanced datasets like fraud detection (1% positive class), accuracy is misleading because a model that predicts all transactions as legitimate would achieve 99% accuracy yet fail to detect any fraud. The F1 score (harmonic mean of precision and recall) is the most effective metric because it balances both false positives and false negatives, providing a single score that reflects the model's ability to correctly identify the minority class without being skewed by class imbalance.

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Accuracy

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Recall

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Precision

Question 10

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 11

A team is developing a recommendation system for an e-commerce platform. They want to use collaborative filtering but are concerned about cold-start problems for new users. Which approach would best mitigate the cold-start problem?

Accepted Answer

Incorporate user demographic features as side information. Incorporating user demographic features as side information allows the collaborative filtering model to generate initial recommendations for new users based on their demographic profile, effectively addressing the cold-start problem. This approach uses content-based features to bootstrap the recommendation process until sufficient user interaction data is collected.

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Increase the number of latent factors in matrix factorization

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Use a popularity-based baseline for all recommendations

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Use only item-based collaborative filtering

Question 12

A team is using a pre-trained language model for sentiment analysis. They want to adapt it to a specific domain with limited labeled data. Which approach is most efficient?

Accepted Answer

Fine-tune the pre-trained model on domain data. Fine-tuning a pre-trained language model on domain-specific labeled data is the most efficient approach because it leverages the general language understanding learned from large corpora while adapting to the target domain with minimal additional data. This process uses transfer learning, where only the final layers or a subset of parameters are updated, significantly reducing the amount of labeled data and compute required compared to training from scratch.

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Use the pre-trained model as is

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Train a new model from scratch

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Ensemble multiple pre-trained models

Question 13

A team is training a recurrent neural network (RNN) with LSTM units to predict stock prices. The validation loss is significantly higher than the training loss. Which action is MOST likely to reduce the gap?

Accepted Answer

Increase the dropout rate in LSTM layers. A significant gap between training and validation loss indicates overfitting, where the model memorizes training data but fails to generalize. Increasing dropout in LSTM layers is a regularization technique that randomly deactivates neurons during training, forcing the network to learn more robust features and reducing overfitting. This directly addresses the gap by improving validation performance.

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Increase the number of LSTM units

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Increase the number of training epochs

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Reduce the sequence length

Question 14

During an AI model deployment, the operations team notices that inference requests are taking longer than expected. Which component is most likely causing the bottleneck?

Accepted Answer

The machine learning model's size and architecture. The machine learning model's size and architecture directly determine the computational complexity of inference. Larger models with more parameters or deeper architectures require more matrix multiplications and memory bandwidth, which increases latency per request. This is the most common bottleneck in AI deployment because the model itself is the core computation unit, and its inference time scales with its complexity.

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Input data preprocessing pipeline

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API gateway rate limiting

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Database connection pool size

Question 15

In prompt engineering, which technique involves providing a few correct input-output examples in the prompt to guide the model's response?

Accepted Answer

Few-shot prompting. Few-shot prompting provides a small number of input-output examples directly in the prompt so the model can infer the desired task format and pattern without any weight updates. This is distinct from zero-shot (no examples) and chain-of-thought (which elicits step-by-step reasoning rather than demonstrating examples).

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System prompt engineering

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Chain-of-thought prompting

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Zero-shot prompting