AI0-001 Practice Test 13 — 15 Questions

Question 1

A machine learning engineer notices that a fraud detection model's false positive rate has increased significantly over the past week. The model was retrained two weeks ago with new data. Which attack is MOST likely responsible?

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

Accepted Answer

Data poisoning. Data poisoning corrupts training data, causing the model to learn incorrect patterns. The retraining with new data introduces the poisoned samples, degrading performance. Adversarial examples are at inference time, model inversion reconstructs data, and prompt injection targets LLMs.

Answer

Prompt injection

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Adversarial examples

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Model inversion

Question 2

A developer is building an AI microservice that processes document intelligence requests asynchronously. Users upload PDFs, and the service extracts text and analyzes it with an LLM. The processing time per document can be up to 5 minutes. Which integration pattern is MOST appropriate?

Accepted Answer

Async processing with a message queue and separate worker service. Async processing queues decouple the frontend from long-running tasks, allowing the user to receive results later without blocking.

Answer

Synchronous REST API call that waits for the LLM response

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WebSocket connection for real-time streaming

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Serverless function triggered by HTTP request

Question 3

A team is building a document intelligence application that extracts key fields from invoices. They have 10,000 labeled invoices. What is the first step in the AI project lifecycle?

Accepted Answer

Problem definition – specify which fields to extract and accuracy targets. Problem definition must come first to scope the project, define success criteria, and decide what fields to extract before any data work.

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Model selection – choose a pre-trained vision transformer

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Data preparation – clean and normalize the invoice images

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Data acquisition – collect additional invoices from public sources

Question 4

A team is deploying an anomaly detection system for real-time monitoring of server metrics. The system should alert when metrics deviate significantly from normal patterns. Which type of AI model is MOST suitable?

Accepted Answer

Autoencoder neural network. Autoencoders learn normal patterns and detect anomalies by high reconstruction error. Linear regression predicts continuous values. Image classifiers are for images. Recommendation systems are for user-item interactions.

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Recommendation system model

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Linear regression model

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Image classification model

Question 5

An AI team is deploying a real-time document intelligence service that extracts key-value pairs from invoices. The pipeline includes an LLM that calls a function to parse structured output. Which TWO testing strategies are essential before production deployment?

Accepted Answer

An evaluation framework that compares extracted fields against ground truth for a test set of invoices. Integration tests for the API call ensure the function calling mechanism works end-to-end. An evaluation framework for LLM output quality measures extraction accuracy. Unit tests for data pipelines are important but less critical here; load testing is operational; model training tests are irrelevant.

Answer

Load testing to simulate peak invoice volume (e.g., end of month)

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Regression tests on the model training pipeline to ensure the base LLM hasn't changed

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Unit tests for the data pipeline that cleans and normalises invoice images

Question 6

An AI system performs anomaly detection on sensor data in a manufacturing plant. The model is deployed and running well. After two months, the plant installs new sensors that produce data with a different distribution. The anomaly detection starts failing with many false positives. Which action should the team take?

Accepted Answer

Retrain the model using data from the new sensors and re-deploy. The model needs monitoring and retraining on data from the new sensors. This falls under model monitoring and lifecycle management in deployment.

Answer

Roll back the model to the previous version and ignore the new sensors

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Increase the anomaly threshold to reduce false positives

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Use an ensemble of the old and a new model without retraining

Question 7

A team is building a recommendation system for an e-commerce platform. They need to update recommendations in real-time as users browse. Which integration pattern is MOST suitable?

Accepted Answer

AI microservices with a REST API. AI microservices deployed behind a REST API allow each recommendation request to be handled independently, scaling horizontally to meet real-time demands.

Answer

Streaming responses from a single monolithic model

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Batch processing with nightly updates

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Async processing queue with delayed responses

Question 8

A security researcher demonstrates that by adding small perturbations to an image of a stop sign, an autonomous vehicle's AI misclassifies it as a speed limit sign. This is an example of which type of attack?

Accepted Answer

Adversarial example attack. This is an adversarial example attack because the researcher adds imperceptible perturbations to the input image (the stop sign) to cause the AI model to output an incorrect classification (speed limit sign). Adversarial examples exploit the model's sensitivity to small, crafted changes in input data, leading to misclassification without altering the underlying task or training data.

Answer

Data poisoning attack

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Model extraction attack

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Membership inference attack

Question 9

An e-commerce company uses a gradient boosting model to forecast daily sales. Recently, the model's predictions have become less accurate, showing a significant drop in R-squared on validation data. The data scientist checks for data drift but finds no significant changes in feature distributions. The model was trained on data from the past 24 months and is retrained monthly. Upon inspecting the feature importance, the data scientist notices that the top feature 'promotion_flag' has decreased in importance over time. What is the most likely cause of the performance degradation, and what should be done?

Accepted Answer

Concept drift has occurred; retrain the model more frequently with recent data only, or use an online learning approach. (overfitting to promotions) does not explain the drop over time. Option C (hyperparameter tuning) is unlikely to fix the temporal change. Option D (leakage) would have caused issues from the start. Option B correctly identifies concept drift (changing relationship) and suggests retraining more frequently or using online learning to adapt.

Answer

The model is overfitting to historical promotions; apply more regularization

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The model's hyperparameters need tuning; perform a grid search

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The promotion_flag feature is leaking future information; remove it

Question 10

A healthcare startup is building an AI system to predict patient readmission risk. The team collects structured data from electronic health records (EHR) including age, diagnosis codes, lab results, and previous admissions. During initial training, the model achieves 95% accuracy on the validation set but only 60% accuracy on a holdout test set from a different hospital. The data scientist suspects overfitting. Which action should the team take first to improve generalization?

Accepted Answer

Apply L2 regularization to the model. The model's high accuracy on the validation set but poor accuracy on a holdout test set from a different hospital indicates overfitting to the training data's specific patterns, which do not generalize to new data. L2 regularization (ridge regression) adds a penalty proportional to the square of the weights, discouraging the model from fitting noise and encouraging simpler, more generalizable decision boundaries. This directly addresses overfitting by reducing variance without requiring more data or reducing model capacity too drastically.

Answer

Switch to a linear regression model

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Increase the model complexity by adding more layers

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Collect more data from the same hospital

Question 11

A manufacturing company uses a computer vision AI to inspect products on an assembly line for defects. The AI model was trained on images from a single camera angle under bright, uniform lighting. Recently, the company moved the inspection station to a different part of the factory where lighting is dimmer and varies due to nearby windows. The model now misclassifies many non-defective products as defective, causing false alarms and production delays. The team has limited labeled data from the new environment. Which action should the team take to restore inspection accuracy while minimizing downtime?

Accepted Answer

Apply domain adaptation techniques using a small set of labeled images from the new environment. Domain adaptation techniques allow a model trained on a source domain (bright, uniform lighting) to generalize to a target domain (dim, variable lighting) using only a small set of labeled images from the new environment. This approach minimizes downtime because it avoids the need for large-scale data collection or retraining from scratch, and it directly addresses the distribution shift that causes false positives.

Answer

Increase the defect classification threshold to reduce false positives

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Revert to the previous lighting setup by reinstalling bright, uniform lights

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Retrain the model from scratch using a large dataset of images from the new environment

Question 12

A data scientist is tuning hyperparameters for a support vector machine (SVM) with an RBF kernel. Which two hyperparameters most significantly affect model performance? (Select TWO.)

Accepted Answer

gamma (kernel coefficient). Gamma defines the influence of a single training example, with low values meaning a far reach and high values meaning a close reach. C controls the trade-off between achieving a low error on the training data and minimizing the margin, directly impacting overfitting. Together, they are the two most critical hyperparameters for an SVM with an RBF kernel.

Answer

learning rate

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epsilon (for epsilon-SVR)

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degree (for polynomial kernel)

Question 13

Which machine learning paradigm is best suited for training a model to play a game by learning from its own actions and rewards, without labeled data?

Accepted Answer

Reinforcement learning. Reinforcement learning learns via trial-and-error using rewards and penalties, ideal for game-playing agents. Supervised learning requires labeled data; unsupervised learning finds patterns without rewards; semi-supervised uses a mix.

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Unsupervised learning

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Semi-supervised learning

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Supervised learning

Question 14

A company wants to recommend products to users based on their past purchase history. Which machine learning paradigm is BEST suited for this task?

Accepted Answer

Supervised learning with regression. Recommender systems are a classic application of supervised learning (if using regression or classification to predict ratings) or unsupervised learning (collaborative filtering). Among the options, supervised learning with regression is appropriate for predicting purchase likelihood.

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Reinforcement learning

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Unsupervised clustering

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Self-supervised learning

Question 15

A company wants to deploy an LLM-based chatbot that can handle sensitive customer information. Which THREE measures should be implemented to mitigate prompt injection attacks? (Choose 3)

Accepted Answer

Use a system prompt that instructs the model to ignore any instructions in the user input. Input sanitization removes special characters or patterns; output filtering checks responses for sensitive data; system prompts with separation instructions can reduce injection risk. Restricting temperature only affects randomness; using a smaller model does not prevent injection.

Answer

Use a smaller model with fewer parameters

Answer

Set temperature to a low value