AI-900 Practice Test 28 — 15 Questions

Question 1

What types of documents does Azure AI Document Intelligence's prebuilt 'receipt' model extract data from?

Accepted Answer

Sales receipts from stores and restaurants, extracting merchant details, items, and totals. Azure AI Document Intelligence's prebuilt 'receipt' model is specifically designed to extract key information from sales receipts, such as merchant details, transaction items, and totals. It uses optical character recognition (OCR) and deep learning models to parse both printed and handwritten receipts from stores and restaurants, handling various formats and layouts.

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Only digital PDF receipts with standardized formatting

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Medical receipts and prescription records only

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Electronic bank transfer receipts for financial transactions

Question 2

A company needs to automatically extract text from scanned invoices that contain both printed text and handwritten notes. Which Azure AI service is specifically designed to handle this type of document?

Accepted Answer

Azure AI Document Intelligence (formerly Form Recognizer). Azure AI Document Intelligence (formerly Form Recognizer) is specifically designed to extract text, key-value pairs, and tables from scanned documents, including invoices with both printed text and handwritten notes. It uses optical character recognition (OCR) combined with deep learning models to handle mixed content, making it the correct choice for this scenario.

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Azure Face API

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Azure Custom Vision

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Azure Video Indexer

Question 3

What is 'real-time speech translation' in Azure AI Speech?

Accepted Answer

Converting spoken words in one language to text or speech in another language instantly. Real-time speech translation in Azure AI Speech is designed to translate spoken language into another language with minimal latency, enabling live conversations. Option B correctly describes this capability, as it converts spoken words in one language to text or speech in another language instantly, leveraging the Speech Translation API with streaming audio input.

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Translating pre-recorded audio files overnight in a batch processing job

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Generating subtitles for pre-existing videos stored in Azure Media Services

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Converting text written in one language into spoken audio in the same language

Question 4

What is 'pronunciation assessment' in Azure AI Speech?

Accepted Answer

Scoring a speaker's accuracy, fluency, and completeness against native pronunciation norms. Pronunciation assessment in Azure AI Speech evaluates a speaker's spoken audio against native pronunciation norms, providing scores for accuracy, fluency, and completeness. This is a feature of the Speech-to-Text API that uses a reference script and phoneme-level comparison to generate detailed feedback, making it ideal for language learning and accent reduction applications.

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Checking whether a text-to-speech voice pronounces technical terms correctly

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Detecting regional accents to route calls to the appropriate customer service team

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Generating a list of commonly mispronounced words in a specific language

Question 5

What is 'max_tokens' parameter in Azure OpenAI and how does it affect responses?

Accepted Answer

A limit on the model's generated response length — stopping output at the specified token count. The 'max_tokens' parameter in Azure OpenAI sets a hard limit on the number of tokens (words or subwords) the model can generate in its response. Once this token count is reached, the model stops producing further output, effectively controlling response length. This is distinct from input processing limits, as 'max_tokens' applies solely to the generated completion.

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The maximum number of tokens in the input prompt the model can process

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The total number of API calls allowed per Azure subscription per hour

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The maximum number of conversation turns before the session resets

Question 6

What is 'AI transparency' in Microsoft's Responsible AI principles?

Accepted Answer

Ensuring people understand when they're interacting with AI, how it works, and what its limitations are. AI transparency, as defined in Microsoft's Responsible AI principles, is about ensuring that users understand when they are interacting with an AI system, how the system makes decisions, and what its limitations are. This principle focuses on clear communication and documentation, not on open-sourcing code or financial reporting.

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Making AI model source code publicly available as open source

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Reporting all AI project costs transparently in financial statements

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Making all training data publicly available for independent researchers to audit

Question 7

What is 'named entity recognition' (NER) in Azure AI Language?

Accepted Answer

Identifying and classifying real-world entities (people, organisations, locations) mentioned in text. Named entity recognition (NER) is a feature of Azure AI Language that identifies and categorizes real-world entities such as people, organizations, locations, dates, and quantities within unstructured text. It uses pre-trained machine learning models to extract these entities, enabling downstream tasks like information retrieval and content summarization. Option B correctly describes this core functionality.

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Renaming database fields to follow a consistent naming convention

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Recognising the named author of a document for copyright purposes

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Detecting when a user provides their name in a chatbot conversation

Question 8

A hospital deploys an AI system to assist with diagnosing diseases from medical images. A doctor disagrees with the system's diagnosis and overrules it. The hospital wants to document this interaction for legal and audit purposes. Which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Accountability. The scenario involves documenting a human override of an AI system's diagnosis for legal and audit purposes, which directly relates to accountability. Accountability in responsible AI ensures that organizations can answer for their AI systems' decisions by maintaining clear records of interactions, including when humans overrule AI outputs. This principle requires traceability and governance mechanisms, such as audit trails, to assign responsibility for outcomes.

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Fairness

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Reliability and safety

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Transparency

Question 9

What is Optical Character Recognition (OCR) and which Azure AI service provides it?

Accepted Answer

Technology that extracts text from images; provided by Azure AI Vision. Optical Character Recognition (OCR) is the technology that extracts printed or handwritten text from images, such as scanned documents or photos, and converts it into machine-readable text. This capability is provided by the Azure AI Vision service, specifically through its Read API, which can process both printed and handwritten text from a variety of image formats.

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Speech recognition; provided by Azure AI Speech

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Language translation; provided by Azure AI Translator

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Handwriting analysis for personality assessment; provided by Azure AI Face

Question 10

What is a large language model (LLM)?

Accepted Answer

An AI model trained on large amounts of text data that can generate and understand language. A large language model (LLM) is a type of AI model trained on vast amounts of text data using deep learning techniques, typically based on transformer architectures. It learns patterns, grammar, context, and even reasoning from the data, enabling it to generate coherent and contextually relevant text, as well as understand and respond to natural language inputs. This makes option B correct because it captures both the training foundation (large amounts of text data) and the core capabilities (generation and understanding).

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A database that stores large volumes of text documents

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A programming library for processing natural language

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A cloud service for translating documents

Question 11

What does Azure AI Vision return when it detects that an image may contain adult content?

Accepted Answer

Boolean flags and confidence scores for adult, racy, and gory content categories. Azure AI Vision's content moderation feature analyzes images for adult, racy, and gory content. It returns Boolean flags (indicating whether content is detected) and confidence scores (ranging from 0 to 1) for each category, allowing applications to make policy-based decisions without deleting or altering the original image.

Answer

The image is immediately deleted from Azure Storage

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A list of specific body parts detected in the image

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An age verification requirement for the requesting user

Question 12

What is the purpose of Azure AI Vision's 'color analysis' feature?

Accepted Answer

Identifying dominant colors, accent colors, and whether images are black and white. Azure AI Vision's color analysis feature is designed to extract color information from images, including the dominant foreground and background colors, accent colors, and whether the image is black-and-white. This helps in understanding the visual composition and mood of an image, which is useful for applications like branding, content moderation, and image categorization.

Answer

Detecting color defects in manufactured products

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Converting images to grayscale for accessibility

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Measuring the color accuracy of display screens

Question 13

A company wants to build a chatbot that answers customer questions using a large language model. The company has an extensive internal knowledge base with accurate, up-to-date product information. To ensure the chatbot's answers are based on this reliable source rather than the model's internal knowledge, which technique should they use?

Accepted Answer

Grounding with retrieval-augmented generation. Grounding with retrieval-augmented generation (RAG) retrieves relevant, up-to-date chunks from the internal knowledge base and provides them as context to the large language model (LLM) at inference time. This ensures the chatbot's answers are factually based on the company's reliable source rather than relying on the model's potentially outdated or incorrect parametric memory.

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Fine-tuning the model on the knowledge base

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Zero-shot learning

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Prompt engineering with few-shot examples

Question 14

What is 'AI in agriculture' (precision agriculture) and what AI technologies are applied?

Accepted Answer

Crop yield prediction, pest detection, irrigation optimisation, and crop health monitoring using ML and vision. Precision agriculture leverages machine learning (ML) and computer vision to analyze data from sensors, drones, and satellites for tasks like predicting crop yields, detecting pests, optimizing irrigation, and monitoring crop health. These AI technologies enable data-driven decisions that improve efficiency and sustainability in farming.

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AI that writes farming blogs and social media content for agricultural businesses

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Automating all farming tasks with AI-powered robots that replace farm workers

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Using AI to trade agricultural commodity futures on financial markets

Question 15

What does it mean to 'export' a model from Azure AI Custom Vision?

Accepted Answer

Downloading the trained model as a file for offline inference on edge devices. Exporting a model from Azure AI Custom Vision means downloading the trained model as a file (e.g., TensorFlow, ONNX, CoreML, or Docker container) so it can be run locally on edge devices without requiring an internet connection to the cloud API. This enables offline inference, reduced latency, and data privacy for scenarios like manufacturing or retail.

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Sharing the model configuration with other Azure subscriptions

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Moving the model from Custom Vision to Azure Machine Learning

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Submitting the model for Microsoft certification review