AI0-001 Hard Questions — 25 Difficult Questions

Question 1

An AI system used for autonomous driving is found to have a lower accuracy in detecting pedestrians with darker skin tones. The development team wants to address this ethical issue. Which action is most effective?

Accepted Answer

Augment the training dataset with more images of pedestrians with darker skin. Option B is correct because augmenting the training dataset with more images of pedestrians with darker skin directly addresses the root cause of the bias: underrepresentation in the training data. By providing a more balanced and diverse dataset, the model can learn more robust features for all skin tones, reducing accuracy disparity without altering the algorithm's core logic or introducing arbitrary thresholds.

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Conduct additional testing to measure the disparity

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Replace the object detection algorithm with a different one

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Adjust the model's decision threshold for pedestrian detection

Question 2

A model trained on a dataset with imbalanced classes achieves 98% accuracy but only 50% recall for the minority class. Which technique should be applied first to address the imbalance?

Accepted Answer

Apply cost-sensitive learning. Cost-sensitive learning directly modifies the model's loss function to penalize misclassifications of the minority class more heavily than those of the majority class. This approach addresses the root cause of the imbalance—the model's bias toward the majority class—without altering the dataset distribution, making it the most immediate and effective first step.

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Reduce the majority class size

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Use SMOTE to generate synthetic samples

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Collect more data for the minority class

Question 3

An MLOps team automates model deployment with a CI/CD pipeline. A performance regression is detected after deploying a new model version. The team needs to automatically roll back to the previous version. Which approach best enables safe automated rollback?

Accepted Answer

Use a blue/green deployment with automated health checks and traffic switching. Blue/green deployment with automated health checks and traffic switching is the best approach because it allows the team to instantly route all traffic back to the previous (green) environment if the new (blue) version fails health checks. This ensures zero-downtime rollback without manual intervention, directly addressing the need for safe automated rollback in a CI/CD pipeline.

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Maintain a manual rollback script that the operations team can run

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Deploy new models as canary releases and monitor for 24 hours

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Automatically keep the previous model version in storage for later use

Question 4

A data scientist is evaluating a binary classification model for fraud detection. The dataset is highly imbalanced (99% non-fraud, 1% fraud). Which TWO metrics are most appropriate for assessing model performance? (Choose two.)

Accepted Answer

Precision. Precision is appropriate because it measures the proportion of predicted fraud cases that are actually fraudulent, which is critical when false positives (flagging legitimate transactions as fraud) are costly. In a highly imbalanced dataset like this (99% non-fraud), precision directly evaluates the model's ability to avoid overwhelming fraud analysts with false alarms.

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F1 score

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Area under the ROC curve (AUC-ROC)

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Accuracy

Question 5

Refer to the exhibit. A deep learning model is being trained. Based on the training log, which problem is most evident?

Accepted Answer

Overfitting. The training log shows that the training loss continues to decrease while the validation loss increases after a certain epoch, which is a classic sign 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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Vanishing gradients

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Underfitting

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Data leakage

Question 6

A large e-commerce company uses a recommendation system based on collaborative filtering. The system uses a matrix factorization model that is trained nightly on the entire user-item interaction history. Recently, the company launched a flash sale with thousands of new products. Users are reporting that the recommendations are not showing the new products, even for users who have purchased them during the sale. The data engineering team notices that the new products have very few interactions in the training data. The model's loss on the validation set has increased, and the recall@10 metric has dropped from 0.45 to 0.32. The team needs to improve the recommendation of new items without retraining the entire model from scratch every hour. Which approach should the team take?

Accepted Answer

Use a hybrid model that combines collaborative filtering with content-based features from product metadata. Option A is correct because a hybrid model that combines collaborative filtering with content-based features (e.g., product metadata like category, price, or description) can recommend new products even with zero or very few user interactions. The content-based component leverages item attributes to compute similarity between new and existing items, enabling the system to surface new products without requiring extensive interaction history. This approach addresses the cold-start problem for new items while preserving the collaborative filtering signal for established items, and it does not require retraining the entire model from scratch every hour.

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Retrain the model every hour to incorporate new interactions quickly

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Remove the new products from the recommendation pool until they accumulate enough interactions

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Increase the number of latent factors in the matrix factorization model

Question 7

A data scientist is training a convolutional neural network (CNN) for object detection. The training loss decreases rapidly but then plateaus at a high value, and the validation loss starts increasing. Which action should the scientist take to improve the model?

Accepted Answer

Reduce the model complexity. The training loss decreasing rapidly then plateauing at a high value while validation loss increases is classic overfitting. Reducing model complexity (Option C) directly addresses overfitting by decreasing the number of parameters or applying regularization (e.g., dropout, L2), which forces the network to learn more generalizable features rather than memorizing noise in the training data.

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Increase the learning rate

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Increase the number of epochs

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Add more convolutional layers

Question 8

A data scientist is training a multi-class classifier with 10 classes. The training log shows the above output for the first two epochs. What is the most likely cause?

Accepted Answer

The learning rate is set to zero. When the learning rate is set to zero, the optimizer makes no updates to the model weights regardless of the computed gradients. The training loss remains constant across epochs because the parameters never change, which matches the log showing identical loss values for both epochs. This is a common debugging scenario where a misconfigured learning rate prevents any learning from occurring.

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Batch normalization is disabled

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The dataset is imbalanced

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The model is overfitting

Question 9

An organization is implementing an AI-powered chatbot for customer service. The chatbot must comply with GDPR and handle data subject access requests (DSARs). Which design approach best ensures compliance?

Accepted Answer

Implement an audit trail that logs interactions with a unique user identifier, and provide a mechanism to delete logs upon user request.. Option C is correct because GDPR requires that personal data be stored only as long as necessary and that data subjects have the right to erasure. By logging interactions with a unique user identifier and providing a deletion mechanism, the chatbot can fulfill DSARs while maintaining an audit trail for compliance monitoring. This approach balances operational needs with regulatory obligations.

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Minimize data collection by not logging any user interactions.

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Anonymize all user data before logging interactions.

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Encrypt all chat logs and store them indefinitely for audit purposes.

Question 10

A team is deploying a deep learning model that uses a convolutional neural network (CNN) for image recognition. The model achieves high accuracy but is very slow to infer on edge devices. Which THREE optimization techniques should the team consider to speed up inference without significant accuracy loss? (Select three.)

Accepted Answer

Use weight pruning to remove unnecessary connections in the network.. Weight pruning removes redundant or less important connections (weights) from the neural network, reducing the number of computations required during inference. This directly speeds up inference on edge devices while typically causing only a minor drop in accuracy if done carefully, making it a standard optimization technique for deploying CNNs on resource-constrained hardware.

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Use larger convolutional filters (e.g., 7x7 instead of 3x3) to capture more context.

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Increase the number of convolutional layers to improve feature extraction.

Question 11

A self-driving car company is testing an AI model for pedestrian detection. During simulation, the model fails to detect pedestrians in low-light conditions. The safety team wants to improve robustness without retraining the entire model from scratch. Which approach is most appropriate?

Accepted Answer

Apply data augmentation techniques to simulate low-light conditions in the training dataset.. Option B is correct because data augmentation techniques, such as adjusting brightness, contrast, and adding noise, can synthetically create low-light training examples from existing data. This improves the model's robustness to low-light conditions without requiring a full retraining from scratch, as it directly addresses the distribution shift in the input data.

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Replace the convolutional layers with transformer layers to improve attention.

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Use adversarial training to add imperceptible perturbations to training images.

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Increase the model's depth by adding more convolutional layers.

Question 12

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 13

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 14

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 15

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 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.

Answer

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 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.

Question 19

A deep learning model for image classification is overfitting due to a small dataset. The team decides to apply data augmentation. Which augmentation technique is least likely to preserve the label?

Accepted Answer

Random cropping and rescaling. Random cropping and rescaling is least likely to preserve the label because it can cut out the primary object or distort its proportions, potentially removing the discriminative features needed for correct classification. For example, cropping a dog image to show only the background or a leg could change the semantic meaning, making the label 'dog' incorrect. In contrast, other techniques like noise, rotation, or flipping typically retain the core subject and its label.

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Adding random noise

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Random rotation

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Horizontal flip

Question 20

A healthcare startup is deploying a machine learning model to predict patient readmission within 30 days using electronic health records (EHR). The data pipeline uses Apache Spark for preprocessing and training on an Amazon EMR cluster. The training dataset is 50 GB and composed of structured numeric and categorical features, along with unstructured clinical notes. The data scientist observes that training takes over 12 hours and frequently fails due to out-of-memory (OOM) errors, especially when processing the clinical notes via TF-IDF vectorization. The cluster has 10 nodes with 64 GB RAM each. The data engineer has already tried increasing spark.sql.shuffle.partitions to 400 and using Kryo serialization, but OOM persists. Which action should the data engineer take next to resolve the OOM errors?

Accepted Answer

Repartition the clinical notes data into 2000 partitions before TF-IDF. Option B is correct because repartitioning the clinical notes data into 2000 partitions before TF-IDF vectorization increases parallelism and reduces the memory pressure per partition. The default partition count (often based on spark.default.parallelism) is too low for 50 GB of data, causing individual partitions to exceed executor memory limits. By increasing partitions, each executor processes smaller chunks, preventing OOM errors during the memory-intensive TF-IDF stage.

Answer

Broadcast the TF-IDF model to all executors to avoid shuffling

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Add 10 more nodes to the cluster to increase total memory

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Use a single executor with 64 GB and increase driver memory to 128 GB