AI-900 Practice Test 26 — 15 Questions

Question 1

What is 'form recognition' in Azure AI Document Intelligence and what types of forms does it support?

Accepted Answer

Extracting key-value pairs and tables from structured form documents using pre-built or custom models. Form recognition in Azure AI Document Intelligence (formerly Form Recognizer) is a specialized service that uses optical character recognition (OCR) and machine learning to extract key-value pairs, tables, and text from structured or semi-structured documents. It supports pre-built models for common forms like invoices and receipts, as well as custom models trained on user-provided form samples. Option B correctly describes this extraction capability.

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Recognising when a web form has been submitted by a user in a browser application

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Generating HTML forms automatically from a database schema

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Validating that completed forms meet schema and data type requirements

Question 2

What is 'training data' vs 'test data' in machine learning?

Accepted Answer

Training data fits the model; test data provides an unbiased estimate of real-world performance. Training data is used to fit the model's parameters (e.g., weights in a neural network or split criteria in a decision tree), while test data is held back and used only after training to evaluate the model's performance on unseen data. This separation provides an unbiased estimate of how the model will generalize to real-world data, which is critical for avoiding overfitting. In Azure Machine Learning, this split is typically managed via the `train_test_split` function or automated in AutoML pipelines.

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Training data is collected first; test data is older data from an archive

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Training data is labelled by humans; test data is labelled automatically by the model

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Test data is always larger than training data to ensure reliable evaluation

Question 3

What is 'Azure AI Immersive Reader' and what accessibility needs does it address?

Accepted Answer

An NLP-powered text accessibility tool with TTS, syllable highlighting, and translation for learners and readers with disabilities. Azure AI Immersive Reader is a natural language processing (NLP) tool designed to improve reading comprehension and accessibility. It uses text-to-speech (TTS), syllable highlighting, and real-time translation to support learners, individuals with dyslexia, and other reading disabilities, making option B correct.

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A VR headset that immerses users in a virtual reading environment

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A feature that reads 3D models and product descriptions aloud in e-commerce applications

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An AI that summarises long articles into concise bullet points for busy readers

Question 4

A manufacturing company uses an AI system to predict when machines will need maintenance. The system must work correctly under varying factory floor conditions such as temperature changes and noise levels. Which Microsoft responsible AI principle is most directly focused on ensuring the system performs reliably in these different conditions?

Accepted Answer

Reliability & Safety. B is correct because the Reliability & Safety principle ensures that AI systems operate consistently and predictably under varying conditions, such as temperature changes and noise levels on a factory floor. This principle mandates rigorous testing, monitoring, and fail-safe mechanisms to maintain performance and prevent harm when environmental factors deviate from expected ranges.

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Fairness

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Privacy & Security

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Inclusiveness

Question 5

A hospital uses an AI system to analyze patient records and provide treatment recommendations. They want to ensure that individual patients cannot be re-identified from the data used to train the model. Which Microsoft responsible AI principle is most directly relevant to this requirement?

Accepted Answer

Privacy and security. The requirement to prevent re-identification of individual patients from training data directly aligns with the Privacy and Security principle. This principle mandates that data be anonymized or de-identified to protect personal information, ensuring that individuals cannot be traced back from the dataset. In AI systems, this involves techniques like differential privacy, which adds noise to data to obscure individual contributions while preserving overall statistical patterns.

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Fairness

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Inclusiveness

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Accountability

Question 6

What is Azure AI Language's 'custom summarization' capability?

Accepted Answer

Fine-tuning the summarization model on domain-specific documents for improved specialized summaries. Azure AI Language's custom summarization allows you to fine-tune a pre-trained summarization model using your own domain-specific documents. This enables the model to generate more accurate and relevant summaries for specialized fields like legal, medical, or financial texts, rather than relying solely on generic training data.

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Generating summaries with custom fonts and formatting styles

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Setting a custom character limit for all generated summaries

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Automating summary creation for all documents in an Azure storage account

Question 7

A manufacturing company wants to use Azure Computer Vision to inspect products on an assembly line for defects. They have a labeled dataset with images of defective and non-defective products. They need to not only classify products as defective or not, but also identify the exact location of the defect (e.g., a crack) in the image. Which Azure Computer Vision capability should they use?

Accepted Answer

Custom Vision object detection. Custom Vision object detection is the correct choice because it not only classifies images (defective vs. non-defective) but also localizes defects by drawing bounding boxes around them. The labeled dataset with defect locations directly supports training a model to output both class labels and spatial coordinates, which is exactly what object detection provides.

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Custom Vision image classification

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Azure Face API

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Optical Character Recognition (OCR)

Question 8

A global company receives customer support tickets in over 60 languages. They need to automatically detect the language of each ticket so it can be routed to the appropriate language-specific team. The company has no labeled training data for language identification. Which Azure AI Language feature should they use?

Accepted Answer

Language Detection. Language Detection is the correct choice because it is a pre-built, zero-shot Azure AI Language feature that can automatically identify the language of text without requiring any labeled training data. The service uses a multilingual model trained on large datasets to detect over 100 languages, making it ideal for routing support tickets in over 60 languages with no prior customization.

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Custom Text Classification

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Key Phrase Extraction

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Entity Recognition

Question 9

What is the Azure AI Vision service's 'Image Analysis 4.0' major new capability compared to previous versions?

Accepted Answer

The Florence foundation model enabling detailed captions, dense captioning, background removal, and multimodal embeddings. Image Analysis 4.0 introduces the Florence foundation model, which significantly enhances image understanding capabilities. This model enables detailed captions, dense captioning (generating captions for multiple regions within an image), background removal, and multimodal embeddings that align images and text in a shared vector space. These features go far beyond the classification, object detection, and OCR capabilities of version 3.x.

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Support for processing video files, which was not available in version 3.x

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Support for the first time for color analysis features in images

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The ability to process images larger than 4MB for the first time

Question 10

What is 'image classification' in Azure AI Custom Vision?

Accepted Answer

Assigning a category label to an entire image based on its dominant visual content. Image classification in Azure AI Custom Vision involves training a model to assign a single category label (e.g., 'dog', 'cat') to an entire image based on its dominant visual content. This is a supervised learning task where the model learns from labeled images to predict the most likely class for new, unseen images. Option B correctly describes this core functionality.

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Organising image files into folders on Azure Blob Storage by date

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Converting colour images to black and white for accessibility purposes

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Sorting images by their file size and resolution metadata

Question 11

What is 'model registry' in Azure Machine Learning?

Accepted Answer

A centralised versioned store for tracking and managing trained models and their lineage. The model registry in Azure Machine Learning is a centralized, versioned store that tracks trained models along with their metadata, lineage, and lifecycle. It enables data scientists to register, version, and manage models, ensuring reproducibility and governance across the ML lifecycle.

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A public marketplace where organisations can buy pre-trained models from third parties

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A database of domain-specific vocabularies used for NLP model training

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A compliance register documenting AI models used by an organisation for audit purposes

Question 12

What is 'ensemble learning' in machine learning and why does it improve performance?

Accepted Answer

Combining predictions from multiple models to improve accuracy and robustness. Ensemble learning combines predictions from multiple models (e.g., bagging, boosting, stacking) to reduce variance, bias, or improve robustness. By aggregating diverse models, it often achieves higher accuracy than any single model, as errors from individual models are averaged out or corrected. This is a core technique in Azure Machine Learning, where ensembles like Random Forest or Gradient Boosting are commonly used.

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Training a single very large model on the full dataset without any data splitting

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Selecting the best model from a group of candidates after evaluation

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Running the same model multiple times with different random seeds to test stability

Question 13

What is the purpose of Azure AI Video Indexer's transcript feature?

Accepted Answer

To automatically convert speech in videos to searchable text with timestamps. Azure AI Video Indexer's transcript feature uses automatic speech recognition (ASR) to convert spoken audio in videos into a text transcript, which is then indexed with precise timestamps for each word or phrase. This enables users to search, navigate, and analyze video content by keyword or phrase, making the video's audio content fully searchable and accessible.

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To translate video subtitles into multiple languages

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To generate written scripts for producing new videos

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To extract text visible in video frames (on-screen text)

Question 14

What is 'machine translation' in Azure AI Translator and what languages does it support?

Accepted Answer

Automatically converting text from one natural human language to another using AI. Machine translation in Azure AI Translator refers to the automated conversion of text from one natural human language to another using AI models. This is the core functionality of the Translator service, which supports over 100 languages and dialects for text translation, enabling real-time or batch translation of written content.

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Translating programming code from one language to another (e.g., Python to JavaScript)

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Translating user requirements documents into technical specifications for developers

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Converting audio speech from one language to text in another language

Question 15

What is 'object tracking' in computer vision and how does it differ from object detection?

Accepted Answer

Maintaining the identity of detected objects across consecutive video frames with persistent IDs. Object tracking maintains the identity of detected objects across consecutive video frames by assigning persistent IDs, enabling the system to follow the same object over time. This differs from object detection, which identifies and locates objects in a single frame without preserving identity across frames. In Azure Video Indexer or Custom Vision, tracking is essential for scenarios like counting unique people or vehicles in a video stream.

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Detecting the same object across multiple images in a photo album

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Monitoring GPS location of physical objects using IoT sensors

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Detecting when a tracked object leaves the camera's field of view