AI-900 Practice Test 24 — 15 Questions

Question 1

What is facial recognition and what are the key responsible AI considerations for its use?

Accepted Answer

Facial recognition requires ethical consideration regarding accuracy disparities, privacy, and potential for misuse. Facial recognition is a computer vision technology that identifies or verifies individuals by analyzing facial features from images or video. The key responsible AI considerations include addressing accuracy disparities across demographic groups (e.g., higher false positive rates for certain ethnicities), ensuring privacy through data minimization and consent, and preventing misuse such as mass surveillance without oversight. Option B correctly captures these ethical imperatives, which are critical for trustworthy deployment.

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Facial recognition has no ethical concerns and should be deployed universally

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Facial recognition is only used for unlocking smartphones

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Facial recognition is 100% accurate across all demographics

Question 2

A bank is developing an AI system to automatically approve or reject small personal loans. To ensure the system treats applicants fairly regardless of race, gender, or age, which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Fairness. The Fairness principle is directly relevant because it requires AI systems to treat all individuals equitably, avoiding discrimination based on protected attributes like race, gender, or age. In this loan approval scenario, the system must be designed and tested to ensure its decisions do not systematically disadvantage any group, which is the core goal of fairness in AI.

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Inclusiveness

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Reliability and safety

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Transparency

Question 3

Which metric is MOST appropriate for evaluating a regression model's performance?

Accepted Answer

Root Mean Squared Error (RMSE). Root Mean Squared Error (RMSE) is the most appropriate metric for evaluating a regression model because it measures the average magnitude of prediction errors in the same units as the target variable, penalizing larger errors more heavily due to squaring. In Azure Machine Learning, regression models like Linear Regression or Decision Forest Regression are evaluated using RMSE to quantify how well the predicted continuous values match actual values.

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Accuracy

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Precision and recall

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AUC-ROC curve

Question 4

What is Azure Machine Learning?

Accepted Answer

A cloud platform for building, training, deploying, and monitoring ML models. Azure Machine Learning is a comprehensive cloud-based platform that provides end-to-end capabilities for the machine learning lifecycle, including building, training, deploying, and monitoring models. It supports various frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and offers features like automated ML, pipelines, and MLOps integration. This distinguishes it from pre-built AI services or specialized infrastructure offerings.

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A pre-built AI service for specific tasks like vision or language

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A database service optimized for storing ML training data

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A GPU-only service for deep learning training

Question 5

A nature conservation organization wants to create an app that automatically identifies different species of birds from photos uploaded by birdwatchers. They have thousands of labeled images of bird species. Which Azure service should they use to train a custom model?

Accepted Answer

Azure Custom Vision. Azure Custom Vision is the correct choice because it allows you to train a custom image classification model using your own labeled dataset of bird species. Unlike the pre-built Computer Vision Image Analysis service, Custom Vision specializes in fine-grained classification tasks where you need to distinguish between dozens or hundreds of visually similar categories, such as different bird species.

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Azure Computer Vision Image Analysis

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Azure Face API

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Azure Form Recognizer

Question 6

What is 'Azure Machine Learning environments' and why are they important for reproducibility?

Accepted Answer

Versioned software configurations (Python packages, dependencies) ensuring reproducible ML runs. Azure Machine Learning environments are versioned software configurations that specify the Python packages, dependencies, and runtime settings needed to execute a training script. They are critical for reproducibility because they ensure that every run uses the exact same software stack, eliminating variability from package version mismatches or missing dependencies.

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The physical Azure data centre locations where model training takes place

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Development, staging, and production deployment targets for Azure ML models

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The security boundaries that isolate different ML projects in the same Azure subscription

Question 7

What does the 'read' operation in Azure AI Vision do?

Accepted Answer

Extracts printed and handwritten text from images and documents. The 'read' operation in Azure AI Vision is specifically designed to extract printed and handwritten text from images and documents using Optical Character Recognition (OCR) technology. It returns the detected text along with bounding box coordinates and confidence scores, making it suitable for digitizing documents, processing forms, and extracting text from photos.

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Reads and describes what's happening in a video

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Reads and verifies digital signatures in documents

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Reads metadata (EXIF data) embedded in image files

Question 8

A retail company uses security cameras to monitor shelves. They want to identify whether a customer is holding a specific product (e.g., a green detergent bottle) and also determine the location of that product within the camera frame. Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct capability because it not only identifies the presence of a specific product (like a green detergent bottle) in an image but also returns bounding box coordinates that indicate the product's location within the camera frame. This dual output—classification plus localization—directly matches the requirement to both recognize the object and determine its position.

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Image classification

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Optical character recognition (OCR)

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Semantic segmentation

Question 9

A healthcare organization needs to extract specific data elements (such as patient names, medication dosages, and dates) from unstructured doctors' notes. Which Azure Cognitive Service is best suited for this task?

Accepted Answer

Text Analytics. Text Analytics (now part of Azure AI Language) is the correct service because it provides pre-built entity extraction capabilities specifically designed to identify and extract named entities like people (patient names), quantities (medication dosages), and dates from unstructured text. This aligns directly with the requirement to extract specific data elements from doctors' notes without needing custom model training.

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Language Understanding (LUIS)

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Translator Text

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Speech

Question 10

What is 'product recognition' in Azure AI Vision for retail scenarios?

Accepted Answer

Identifying retail products and checking shelf placement compliance using computer vision. Product recognition in Azure AI Vision for retail scenarios is specifically designed to identify retail products and check shelf placement compliance using computer vision. It uses object detection and image analysis to recognize products in images or video streams, then compares their placement against a predefined planogram to ensure items are correctly stocked and positioned. This capability helps retailers automate inventory management and optimize shelf layouts.

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Scanning product barcodes to look up inventory information

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Generating product descriptions from images for e-commerce listings

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Detecting counterfeit or damaged products in a manufacturing quality line

Question 11

What is 'background removal' in Azure AI Vision and what is it used for?

Accepted Answer

Automatically separating the foreground subject from the image background. Background removal in Azure AI Vision uses deep learning models to automatically detect and separate the primary foreground subject (e.g., a person, object, or animal) from the rest of the image. The service outputs either a cut-out image with a transparent background or a binary mask, enabling downstream tasks like compositing, product catalog creation, or privacy-focused image processing. This is a core computer vision capability, not related to audio, metadata, or image sharpness.

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Removing background noise from audio in video recordings

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Deleting metadata embedded in image files before uploading to Azure

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Removing blurry or out-of-focus areas from photographs

Question 12

What is 'custom speech' in Azure AI Speech and when would you use it?

Accepted Answer

Fine-tuning speech recognition for domain-specific vocabulary, accents, or noisy environments. Custom speech in Azure AI Speech allows you to fine-tune the speech recognition model to better understand domain-specific vocabulary (e.g., medical or legal terms), unique accents, or noisy environments. By providing audio data and transcription text, you train the model to improve accuracy for your specific use case, which is not achievable with the base recognition model.

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Creating a custom voice persona that sounds different from the standard Azure voices

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Configuring speech recognition to only accept voice commands from authorised users

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Building a custom programming language for writing speech processing scripts

Question 13

What is precision in the context of binary classification model evaluation?

Accepted Answer

The proportion of positive predictions that are actually correct. Precision measures the accuracy of positive predictions: it is the ratio of true positives to the sum of true positives and false positives. Option B correctly defines this as 'the proportion of positive predictions that are actually correct,' which is the standard definition used in Azure Machine Learning's classification metrics.

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The proportion of actual positives that the model correctly identified

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The overall proportion of all predictions that are correct

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The number of decimal places in the model's confidence score

Question 14

A data scientist trains a regression model to predict house prices. The model achieves very low error on the training data but significantly higher error on a held-out test set. Which problem does this scenario best describe?

Accepted Answer

Overfitting. The scenario describes overfitting, where the model learns the training data too well, including noise and outliers, resulting in very low training error but poor generalization to new data. In Azure Machine Learning, this is often detected by comparing training and validation metrics; a large gap indicates overfitting. The correct answer is B.

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Underfitting

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High bias

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High variance

Question 15

A data scientist wants to group customers into segments based on purchasing behavior without using any labeled examples. Which type of machine learning is this?

Accepted Answer

Unsupervised learning. Unsupervised learning is the correct choice because the data scientist has no labeled examples and wants to discover hidden patterns or groupings in the data. Clustering algorithms, such as K-Means or DBSCAN, are used to segment customers based solely on their purchasing behavior features, without any predefined categories.

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Supervised learning

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Reinforcement learning

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Semi-supervised learning