AI0-001 Practice Questions 2 — 50 Questions

Question 1

A company is building a recommendation system for an e-commerce platform. They want the system to learn from user purchase history and browsing behavior to suggest products. Which type of machine learning is most appropriate for this task?

Accepted Answer

Unsupervised learning. Unsupervised learning is the most appropriate because the system must discover hidden patterns and groupings in user purchase history and browsing behavior without labeled outcomes. Recommendation systems often use clustering or association rule mining (e.g., market basket analysis) to identify product affinities and user segments, which are core unsupervised techniques. This allows the system to suggest products based on learned co-occurrence patterns rather than predefined categories.

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Supervised learning

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Semi-supervised learning

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Transfer learning

Question 2

An AI system for autonomous vehicles uses reinforcement learning (RL) to navigate. The reward function encourages reaching the destination quickly but penalizes collisions heavily. The agent learns to drive aggressively, causing minor accidents. Which modification to the reward function would best align the agent's behavior with desired safe driving?

Accepted Answer

Add a penalty term for high acceleration and jerky movements.. Option D is correct because adding a penalty for high acceleration and jerky movements directly addresses the root cause of the aggressive driving behavior—smoothness and safety—without undermining the primary goal of reaching the destination. This modification shapes the reward function to penalize unsafe driving patterns, aligning the agent's learned policy with desired safe navigation while preserving the time-based incentive for efficiency.

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Increase the collision penalty to a very large negative value.

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Remove the time-based reward and only reward reaching the destination.

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Use a potential-based reward shaping to encourage progress toward destination.

Question 3

Based on the exhibit, what issue should the team address?

Accepted Answer

Potential fairness bias across groups. Option B is correct because the exhibit likely shows a confusion matrix or performance metrics broken down by demographic groups (e.g., race, gender), revealing that the model's false positive or false negative rates differ significantly across groups. This disparity indicates a potential fairness bias, which must be addressed to ensure equitable outcomes, especially in high-stakes AI applications like hiring or lending.

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Model accuracy below threshold

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High latency

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Low throughput

Question 4

A data scientist is building a natural language processing model to classify customer reviews as positive or negative. Which TWO preprocessing steps are most essential before tokenization? (Select two.)

Accepted Answer

Remove punctuation and special characters.. Removing punctuation and special characters (Option B) is essential because tokenizers typically split on whitespace, so punctuation attached to words (e.g., 'great!', 'bad.') would create noisy tokens like 'great!' and 'bad.' instead of clean tokens 'great' and 'bad'. Converting all text to lowercase (Option C) ensures that words like 'Great', 'great', and 'GREAT' are all mapped to the same token, preventing the model from treating them as distinct features and reducing vocabulary size.

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Perform stemming or lemmatization.

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Remove stop words from the text.

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Replace missing values with a placeholder.

Question 5

Refer to the exhibit. A data scientist observes the training output. Which issue is most likely?

Accepted Answer

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

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Underfitting

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Data augmentation failure

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Model compression

Question 6

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

Accepted Answer

Dropout. Dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents the network from relying too heavily on any single neuron and forces it to learn more robust features. This reduces overfitting by introducing noise that improves generalization.

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Boosting

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Increasing the learning rate

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Increasing the number of hidden layers

Question 7

A marketing team uses a recommendation system to suggest products to customers. The system currently uses collaborative filtering. Which scenario would most likely cause the cold-start problem?

Accepted Answer

A new product is added to the catalog with no purchase history.. The cold-start problem occurs when a recommendation system lacks sufficient data to make accurate predictions. In collaborative filtering, recommendations rely on historical user-item interactions (e.g., purchase history). A new product with no purchase history has no interaction data, so the system cannot find similar users or items to generate recommendations, directly causing the cold-start problem.

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The system switches from collaborative filtering to content-based filtering.

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The website interface is redesigned, affecting user navigation.

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A seasonal product experiences a sudden spike in sales.

Question 8

A hospital deploys an AI system to detect pneumonia from chest X-rays. The model achieves 95% accuracy on the test set but later is found to be less accurate for patients under 18. The development team suspects bias. Which step should be taken first to investigate?

Accepted Answer

Compute and compare performance metrics for different age subgroups in the test set.. Option C is correct because the first step in investigating suspected model bias is to perform a disaggregated analysis of performance metrics across relevant subgroups, such as age brackets. This directly identifies whether the model's accuracy, precision, recall, or other metrics differ significantly for pediatric patients versus adults, confirming the presence and nature of the bias before any remediation is attempted.

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Automatically retrain the model with a balanced dataset including more pediatric cases.

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Expand the test set with more pediatric X-rays and re-evaluate overall accuracy.

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Add more features to the model to capture age-related anatomical differences.

Question 9

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.. Option C is correct because 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 10

Which THREE of the following are key considerations when deploying an AI model in a production environment?

Accepted Answer

Monitoring model performance for data drift. Option C is correct because data drift refers to the change in the statistical properties of the input data over time, which can degrade model accuracy. Continuous monitoring for data drift is essential in production to detect when the model's assumptions about the data distribution are no longer valid, triggering retraining or alerts.

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Basing acceptance solely on training accuracy

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Maximizing model complexity to achieve the best accuracy

Question 11

A chatbot developer uses a transformer-based model for customer service. Users complain that the chatbot sometimes gives offensive responses. Which technique should be applied first to mitigate this issue?

Accepted Answer

Review and filter the training dataset for offensive or biased language, then fine-tune the model.. Option D is correct because the root cause of offensive responses in transformer-based models is typically biased or toxic language present in the training data. Reviewing and filtering the dataset to remove such content, followed by fine-tuning the model, directly addresses the source of the problem. This approach aligns with the principle of data-centric AI, where improving data quality is the first step before modifying model architecture or inference parameters.

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Increase the model size to improve its understanding of context.

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Decrease the temperature parameter to make outputs more deterministic.

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Train a separate classifier to detect offensive outputs in real time.

Question 12

A team is deploying an AI model for credit approval. Which TWO ethical considerations must be addressed?

Accepted Answer

Model interpretability. Model interpretability (B) is essential for credit approval because financial decisions must be explainable to regulators and customers under laws like GDPR or ECOA. A black-box model that cannot justify why a loan was denied violates compliance requirements, making interpretability a core ethical and legal necessity.

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Training speed

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Model accuracy

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Model size

Question 13

Which THREE are common machine learning algorithms used for regression?

Accepted Answer

Linear regression. Linear regression is a fundamental supervised learning algorithm used for regression tasks, where the goal is to predict a continuous numeric output based on one or more input features. It models the relationship between the dependent and independent variables by fitting a linear equation to the observed data, making it a core algorithm for regression problems.

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Logistic regression

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K-means

Question 14

Which THREE practices are recommended for versioning machine learning models in a production environment?

Accepted Answer

Use a model registry like MLflow or DVC.. Option A is correct because a model registry like MLflow or DVC provides a centralized repository for tracking model versions, metadata, and lineage. This enables reproducibility, rollback, and auditability in production, which is essential for managing the lifecycle of machine learning models.

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Use Git to version model binaries.

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Keep only the latest model to save storage.

Question 15

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.

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Add more hidden layers

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Decrease the learning rate to 0.0001

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Increase the batch size

Question 16

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.

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Hyperparameter tuning

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Feature importance analysis

Question 17

An ML engineering team has a retraining pipeline that triggers automatically when model accuracy drops below a threshold. Recently, the model's accuracy has been fluctuating, causing frequent retraining and high compute costs. The team suspects the data distribution is changing slowly. Which approach should the team implement to reduce unnecessary retraining while maintaining model performance?

Accepted Answer

Implement a statistical drift detection method on input features. Option B is correct because implementing a statistical drift detection method (e.g., using KL divergence, PSI, or ADWIN) on input features allows the team to identify when the data distribution has genuinely changed, rather than reacting to random accuracy fluctuations. This reduces unnecessary retraining by triggering the pipeline only when statistically significant drift is detected, maintaining model performance without the high compute costs of frequent retraining.

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Use a simpler model to reduce variability

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Increase the frequency of model retraining

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Reduce the batch size for inference

Question 18

A company uses an AI model to predict equipment failures. The model outputs a probability of failure. To minimize false alarms, the operations team wants a high precision. Which deployment strategy should they implement?

Accepted Answer

Increase the decision threshold for positive classification. To minimize false alarms and achieve high precision, the operations team should increase the decision threshold for positive classification. A higher threshold means the model only predicts a failure when it is very confident, reducing the number of false positives (false alarms) at the cost of potentially missing some true failures (lower recall). This directly controls the precision-recall trade-off without changing the underlying model.

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Retrain the model on more recent data

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Decrease the decision threshold

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Use an ensemble of models with voting

Question 19

A data scientist is monitoring a deployed image classification model. Which TWO actions are best practices for detecting model drift? (Choose 2.)

Accepted Answer

Use a holdout test set to periodically evaluate model accuracy.. Option C is correct because periodically evaluating the model on a holdout test set that reflects the current production data distribution is a direct method to detect accuracy degradation caused by model drift. This approach measures whether the model's performance on unseen data has declined over time, which is a key indicator of drift.

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Schedule automatic weekly retraining of the model.

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Increase the model's complexity to improve generalization.

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Monitor the average prediction confidence of the model.

Question 20

A financial institution needs to integrate an AI-based credit scoring model into an existing mainframe system that processes transactions in COBOL. The model is deployed as a REST API. What is the best strategy to ensure minimal disruption and maintain data integrity?

Accepted Answer

Use an API gateway with versioning and circuit breaker patterns.. Option B is correct because an API gateway with versioning and circuit breaker patterns allows the COBOL mainframe to call the REST API without modifying its core transaction logic. The gateway handles protocol translation, rate limiting, and failover, ensuring minimal disruption to the existing mainframe system while maintaining data integrity through transactional consistency and graceful degradation.

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Copy all transaction data to a cloud database for the model to access.

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Rewrite the mainframe system in Java to directly call the model.

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Install a GPU on the mainframe to run the model natively.