AI0-001 Practice Test 40 — 15 Questions

Question 1

A company wants to train a language model on sensitive customer data without transferring the raw data to a central server. Which privacy-preserving technique should they use?

Accepted Answer

Federated learning. Federated learning is the correct technique because it trains a shared model across decentralized edge devices holding local data, without transferring raw customer data to a central server. Only model updates (gradients) are sent to the aggregation server, preserving data locality and reducing exposure. This directly addresses the requirement of avoiding raw data transfer while still enabling collaborative model training.

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Differential privacy

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Data minimisation

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Anonymisation

Question 2

A developer is building a natural language processing system to classify customer reviews as positive, neutral, or negative. They have 50,000 labeled reviews. Which model architecture is MOST appropriate for this task?

Accepted Answer

Fine-tune a pre-trained BERT model. Fine-tuning a pre-trained BERT model is most appropriate because BERT is a transformer-based model pre-trained on a large corpus and can be fine-tuned on the 50,000 labeled reviews to achieve high accuracy with relatively little data. It captures bidirectional context, which is crucial for sentiment classification, and avoids the need for training from scratch.

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Use a convolutional neural network (CNN) on raw text

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Train a recurrent neural network (RNN) from scratch

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Word2vec embeddings followed by logistic regression

Question 3

While training a deep neural network, the loss function fails to converge and oscillates wildly. Which adjustment is most likely to stabilize training?

Accepted Answer

Reduce the learning rate. When the loss function oscillates wildly and fails to converge, it typically indicates that the learning rate is too high, causing the optimizer to overshoot the minima. Reducing the learning rate allows the gradient descent updates to take smaller, more stable steps, which helps the loss converge smoothly. This is a fundamental hyperparameter tuning step in deep learning training.

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Increase the number of hidden layers

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Decrease the batch size

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Use a test set

Question 4

A company deploys an AI chatbot that generates product descriptions. The company wants to be transparent about AI-generated content. Which practice should they follow?

Accepted Answer

Clearly label AI-generated content as such. Transparency about AI-generated content is a core principle of AI governance and ethics. Labeling AI-generated outputs as such allows users to make informed decisions about the content they consume, aligning with responsible AI practices.

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Publish a model card, but not label individual outputs

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Add an invisible watermark but do not inform users

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Do not disclose that content is AI-generated to avoid user confusion

Question 5

An AI system must extract text from scanned invoices and output structured fields (invoice number, date, total amount). Which type of AI application is this?

Accepted Answer

Document intelligence. Document intelligence (D) is the correct answer because it specifically refers to AI systems that extract, classify, and structure data from documents like invoices, receipts, and forms. This application uses optical character recognition (OCR) combined with natural language processing (NLP) to identify and output structured fields such as invoice number, date, and total amount, which is exactly what the question describes.

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Chatbot/virtual assistant

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Code generation

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Image classification/object detection

Question 6

A data scientist is preparing a dataset for supervised learning. Which TWO steps are essential?

Accepted Answer

Labeling the data. Labeling the data is essential for supervised learning because the algorithm requires input-output pairs to learn a mapping function. Without labeled data, the model cannot be trained to predict outcomes, as supervised learning relies on ground-truth targets for error correction during training.

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One-hot encoding all features

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Normalizing features

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Removing outliers

Question 7

Which THREE are common pitfalls when operationalizing AI models? (Select THREE.)

Accepted Answer

Training-serving skew due to differences in data preprocessing. Training-serving skew occurs when the data preprocessing logic used during model training differs from that used during inference in production. This is a common pitfall in operationalizing AI models, as even minor discrepancies in feature engineering, normalization, or encoding can cause significant performance degradation. For example, using different libraries or versions for tokenization between training and serving pipelines directly leads to skew.

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Using simpler models that are easier to debug

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Automating the model retraining process

Question 8

An e-commerce company needs to update its recommendation model continuously as user preferences change. The model currently retrains from scratch every night, but the training time is too long. Which approach would reduce training time while keeping the model up-to-date?

Accepted Answer

Implement incremental learning using online gradient descent.. Incremental learning using online gradient descent updates the model parameters with each new data point or mini-batch, avoiding the need to retrain from scratch. This approach significantly reduces training time while continuously adapting to changing user preferences, making it ideal for real-time recommendation systems.

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Use dimensionality reduction on features.

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Switch to a simpler model.

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Increase the batch size for retraining.

Question 9

A developer wants to secure an AI API service. Which practice is MOST effective for preventing unauthorized access to the model?

Accepted Answer

Enforcing least-privilege API access with proper key management. Enforcing least-privilege API access with proper key management is the most effective practice because it ensures that each API key or token has only the minimum permissions necessary for its intended function, reducing the attack surface. Proper key management includes rotating keys, using scoped access tokens (e.g., OAuth 2.0 scopes), and storing keys securely (e.g., using a secrets manager like AWS Secrets Manager or HashiCorp Vault). This directly prevents unauthorized access by limiting what a compromised or misused key can do, unlike other options that address secondary concerns.

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Using a larger context window

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Enabling response logging

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Implementing rate limiting

Question 10

An e-commerce company deploys a deep learning model for product recommendation. After a new data pipeline is implemented, the model's online performance degrades: recall drops by 20% and the click-through rate decreases. The data scientists suspect data drift. They compare the distribution of the input features between the training data and recent production data. The Kolmogorov-Smirnov test shows significant differences for two numerical features (price and rating). The team also notices that the frequency of categorical feature 'category' has changed. Which of the following is the MOST appropriate first step? A. Immediately retrain the model on all available data including new production data. B. Roll back to the previous data pipeline and investigate the root cause of drift. C. Use feature selection to remove the drifting features and retrain. D. Implement a monitoring dashboard to track drift over time and set up alerts.

Accepted Answer

Roll back to the previous data pipeline and investigate the root cause of drift.. Since the drift occurred after a pipeline change, rolling back and investigating the root cause is the most prudent first step before making model changes. Retraining on drifted data (A) might incorporate a faulty distribution. Removing drifting features (C) could lose important information and may not fully address the issue. Implementing monitoring (D) is useful for long-term but does not address the immediate degradation.

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Implement a monitoring dashboard to track drift over time and set up alerts.

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Use feature selection to remove the drifting features and retrain.

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Immediately retrain the model on all available data including new production data.

Question 11

A startup wants to add an AI-powered virtual assistant to their mobile app. They have limited in-house AI expertise and need a solution that can be integrated quickly with minimal infrastructure management. Which deployment pattern is MOST suitable?

Accepted Answer

Use a cloud-based AI microservice (e.g., Amazon Lex, Azure Bot Service) with a pre-built model. Using AI microservices from a cloud provider (e.g., AWS, Azure, GCP) allows quick integration, scalability, and minimal management. Training on-premises requires expertise and resources. Edge deployment is complex. Async queues are for batch processing, not real-time assistant.

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Implement an asynchronous processing queue for all user requests

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Train and deploy a custom model on an on-premises server

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Deploy the model on edge devices for offline inference

Question 12

An AI system is being developed to diagnose diseases from medical images. The model achieves 99% accuracy on the test set, but when deployed in a different hospital, performance drops significantly. Which of the following is the MOST likely cause?

Accepted Answer

The training data does not represent the new hospital's population or imaging equipment.. The model's high accuracy on the test set but poor performance in a different hospital indicates a distribution shift between the training data and the deployment environment. This is a classic case of dataset shift, where the training data does not represent the new hospital's patient population or imaging equipment, leading to degraded model generalization.

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The model is being attacked by adversarial examples.

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The model is overfitted to the training data.

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Data leakage occurred during preprocessing.

Question 13

A large e-commerce company uses a recommendation engine trained on millions of user interactions. Recently, the marketing team noticed a sharp increase in click-through rates for a particular product category. Upon investigation, an engineer found that a competitor had injected fake user profiles that consistently clicked on their products, skewing the training data. The company needs to remediate the attack and prevent future occurrences. The team has limited time and budget. Which course of action should the company take first?

Accepted Answer

Identify and remove the fake user profiles from the training dataset, then retrain the model. The immediate priority is to remove the poisoned data from the training set and retrain the model, as the fake profiles are actively skewing predictions and causing incorrect click-through rate spikes. This direct remediation addresses the root cause with minimal time and budget, aligning with the team's constraints. Without cleaning the data, any further training or defensive measures would still operate on corrupted inputs.

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Implement adversarial training to make the model robust to future poisoning attempts

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Decrease the frequency of model retraining to limit exposure to new data

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Add differential privacy noise to the training data to mask the injected profiles

Question 14

An organization wants to centralize experiment tracking, model versioning, and deployment management across its data science team. Which MLOps platform is specifically designed for experiment tracking and model registry?

Accepted Answer

MLflow. MLflow is an open-source MLOps platform that provides a centralized experiment tracking API (MLflow Tracking) and a model registry (MLflow Model Registry) for versioning, staging, and deploying machine learning models. It is specifically designed to address the need for experiment tracking and model lifecycle management, making it the correct choice for this scenario.

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Apache Airflow

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Weights & Biases

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Kubeflow

Question 15

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.. 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.