AI-900 Practice Test 19 — 15 Questions

Question 1

A retail company wants to build a system that can verify the identity of customers by comparing their live photo with an uploaded government-issued ID photo. Which Azure Computer Vision service should they use to perform the face comparison?

Accepted Answer

Azure Face API. The Azure Face API is specifically designed for face detection, verification, and comparison tasks. It can compare a live photo against a reference photo (such as a government-issued ID) using its 'Verify' operation, which returns a confidence score indicating whether the two faces belong to the same person. This makes it the correct choice for identity verification scenarios.

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Azure Computer Vision - Image Analysis

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Azure Custom Vision

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Azure Form Recognizer

Question 2

A news agency receives thousands of articles daily from wire services. They want to automatically identify and tag mentions of people, organizations, and locations within each article to improve search and categorization. The agency has no labeled training data. Which built-in Azure AI Language feature should they use?

Accepted Answer

Named Entity Recognition (NER). Named Entity Recognition (NER) is the correct choice because it is specifically designed to identify and categorize mentions of people, organizations, locations, and other entity types in unstructured text. Since the agency has no labeled training data, NER's pre-trained model can be used out-of-the-box without any custom training, making it ideal for automatically tagging articles with these entity types to improve search and categorization.

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Sentiment Analysis

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Key Phrase Extraction

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Language Detection

Question 3

A warehouse uses ceiling-mounted cameras to monitor inventory shelves. The system needs to determine whether each shelf is 'full', 'half full', or 'empty' based on the entire image of the shelf. Which Azure Computer Vision capability should they use?

Accepted Answer

C) Image classification. Image classification (C) is the correct choice because the system needs to assign a single label (full, half full, or empty) to the entire image of a shelf. Azure Computer Vision's image classification analyzes the whole image and outputs a single category or tag, which directly matches the requirement of determining the overall state of the shelf. Object detection would identify and locate multiple objects within the image, not classify the entire scene, and semantic segmentation would assign a label to every pixel, which is overkill for this task.

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A) Optical Character Recognition (OCR)

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B) Object detection

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D) Semantic segmentation

Question 4

A security company needs to monitor a warehouse using video cameras. They want to detect whether any persons are present in a given frame and also know their approximate locations. Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct choice because it not only identifies whether persons are present in a video frame but also provides bounding box coordinates indicating their approximate locations. This capability is specifically designed to locate multiple objects of interest within an image, which directly matches the requirement of detecting persons and knowing where they are.

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Image classification

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Semantic segmentation

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Optical Character Recognition (OCR)

Question 5

An art gallery wants to build a mobile app that allows visitors to take a photo of a specific painting and receive detailed information about that artwork. The gallery has a library of high-quality images of each painting in their collection. Which Azure AI service should they use to build this identification capability?

Accepted Answer

Azure Custom Vision. Azure Custom Vision is the correct choice because it allows the gallery to train a custom image classification model using their library of high-quality painting images. This service enables the app to identify specific artworks from user-captured photos and return detailed information, as it is designed for custom classification scenarios where pre-built models are insufficient.

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Azure Computer Vision (pre-built image analysis)

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Azure Face API

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Azure Computer Vision (OCR)

Question 6

A social media platform uses Azure OpenAI Service to generate summaries of user comments. The development team discovers that sometimes the generated summaries include offensive or harmful language that was present in the original comments. The team wants to ensure that the generated output is always free of hate speech, profanity, and self-harm references. What should the team configure in the Azure OpenAI Service?

Accepted Answer

Configure a content filter. Option B is correct because Azure OpenAI Service provides built-in content filtering that can be configured to block hate speech, profanity, and self-harm references in both input prompts and generated completions. This ensures that even if offensive language appears in the original user comments, the generated summaries will be free of such harmful content. The content filter operates at the service level, applying predefined severity thresholds to filter out undesirable language.

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Set the temperature parameter to 0

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Increase the max_tokens parameter

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Use a grounding source

Question 7

An autonomous drone delivery company uses an AI model to navigate. During testing in a new city, the model fails to detect power lines and crashes into them. The company wants to ensure their system is robust to unusual conditions. Which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Reliability and Safety. The scenario describes a failure in an AI system that leads to a physical safety hazard (crashing into power lines). The Microsoft responsible AI principle of Reliability and Safety directly addresses the need for AI systems to operate reliably under a range of conditions and to fail safely when they encounter unexpected situations. Ensuring robustness to unusual conditions, such as unseen power lines in a new city, is a core requirement of this principle.

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Fairness

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Privacy and Security

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Inclusiveness

Question 8

A security system uses cameras to detect whether a person is present at a restricted door. Which Azure Computer Vision capability should they use to detect the presence of human faces in the camera images?

Accepted Answer

Face Detection. Face Detection is the correct choice because it is specifically designed to locate and identify human faces in images, returning bounding box coordinates for each detected face. This capability directly addresses the requirement to detect whether a person is present at a restricted door by identifying faces in camera images, without needing to recognize who the person is.

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Optical Character Recognition (OCR)

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Object Detection

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Image Classification

Question 9

What is 'k-fold cross-validation' specifically and how is k=10 different from k=5?

Accepted Answer

k=10 provides more reliable performance estimates at 2x the compute cost vs k=5. k-fold cross-validation splits the dataset into k equal folds, training on k-1 folds and validating on the remaining fold, repeating this process k times. With k=10, each model is trained on 90% of the data and validated on 10%, while k=5 uses 80% for training and 20% for validation. The key difference is that k=10 yields a performance estimate with lower variance (more reliable) because it averages over more folds, but it requires approximately twice the computational cost (10 training runs vs. 5).

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k=10 always produces a better model than k=5 because it uses more training data

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k=5 and k=10 produce identical results because the total data is the same

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k=10 requires 10 times more labelled data than k=5

Question 10

A real estate company has a dataset containing square footage, number of bedrooms, and location for 10,000 houses, along with their sale prices. They want to train a model that predicts the sale price of a new house based on these features. Which type of machine learning should they use?

Accepted Answer

Supervised regression. The goal is to predict a continuous numeric value (sale price) from input features (square footage, bedrooms, location). This is a classic supervised regression problem because the training data includes labeled target values (prices) and the output is a real number, not a category.

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Supervised classification

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Unsupervised clustering

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Reinforcement learning

Question 11

A wildlife research team uses drone imagery to monitor penguin populations in a remote area. The penguins are small, blend into the rocky background, and are often only partially visible. The team has a limited set of 500 labeled drone images showing penguins. They want to build a system that accurately detects and counts penguins. Which approach should they take using Azure AI services?

Accepted Answer

Train a Custom Vision object detection model using the labeled images.. The pre-built Computer Vision object detection API is optimized for common objects and may not perform well on small, camouflaged penguins in rocky terrain. Custom Vision allows the team to train a dedicated object detection model using their 500 labeled images, enabling the model to learn the specific visual features of penguins in this challenging environment. This approach is ideal for domain-specific detection tasks where off-the-shelf models lack accuracy.

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Use the pre-built Computer Vision object detection API directly.

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Use the Computer Vision Image Analysis API with the 'dense captioning' feature.

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Train a Custom Vision image classification model with the labeled images.

Question 12

A social media platform wants to automatically review user-uploaded images to flag any that contain explicit or suggestive adult content, as well as violent imagery. Which Azure Computer Vision feature should they use?

Accepted Answer

Image Analysis - Moderate content. Option C is correct because the 'Moderate content' feature of Azure Computer Vision is specifically designed to detect adult, suggestive, and violent content in images. It returns a binary flag and confidence scores for categories like adult, racy, and gory, making it the appropriate choice for automatically flagging explicit or violent user-uploaded images.

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Optical Character Recognition (OCR)

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Image Analysis - Tags

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Face Detection

Question 13

An autonomous drone needs to navigate a forest by identifying individual trees, including their exact shape and boundaries, to avoid colliding with branches. The drone also needs to distinguish between trees and other objects like rocks. Which Azure Computer Vision capability is best suited for this requirement?

Accepted Answer

Semantic segmentation. Semantic segmentation is the correct choice because it classifies every pixel in an image, assigning each pixel to a specific class (e.g., 'tree', 'rock', 'branch'). This pixel-level precision allows the drone to identify the exact shape and boundaries of individual trees, which is essential for collision avoidance in a forest environment.

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Image classification

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Object detection

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Optical character recognition (OCR)

Question 14

A security company needs to analyze live video feeds from multiple cameras to detect specific objects (e.g., vehicles, people) and also read license plate numbers from vehicles. Which combination of Azure Computer Vision capabilities should they use?

Accepted Answer

Object detection and Optical Character Recognition. Option A is correct because the scenario requires two distinct capabilities: detecting specific objects (vehicles, people) in live video feeds, which is handled by Azure Computer Vision's Object Detection feature, and reading license plate numbers, which requires Optical Character Recognition (OCR). Object detection identifies and locates objects within an image or video frame, while OCR extracts text from images, making this combination ideal for the use case.

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Image analysis and face detection

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Semantic segmentation and image captioning

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Spatial analysis and image classification

Question 15

A retail company wants to predict the exact number of units of a product that will be sold next month. They have historical sales data and information about promotions and holidays. The target variable is the number of units sold, which is a continuous value. Which type of machine learning task should they perform?

Accepted Answer

Regression. Regression is the correct choice because the target variable—number of units sold—is a continuous numeric value. Regression algorithms, such as linear regression or decision forest regression, are designed to predict a numeric quantity from historical features like sales data, promotions, and holidays. In Azure Machine Learning, regression models output a real number, making them ideal for this forecasting scenario.

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Binary classification

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Multiclass classification

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Clustering