AI-900 Practice Exam 59 — 20 Questions

Question 1

A marketing team wants to use Azure AI to automatically generate unique product descriptions for thousands of items in an e-commerce catalog based on a few keywords provided by the inventory team. Which Azure service should they use?

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

Accepted Answer

A. Azure OpenAI Service. Azure OpenAI Service provides access to large language models (LLMs) like GPT-4, which are specifically designed for generative tasks such as creating unique, human-like text from a few input keywords. This makes it the ideal choice for automatically generating product descriptions at scale, as it can produce varied and contextually relevant content without requiring pre-labeled training data.

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B. Azure Computer Vision

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C. Language Understanding (LUIS)

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D. Azure Machine Learning

Question 2

A warehouse deploys cameras to automatically process incoming packages. The system must read the serial numbers printed on each package label to update inventory records. The labels often have varied fonts and sizes, and may be slightly rotated. Which Azure Computer Vision capability should be used to extract the serial numbers?

Accepted Answer

Optical Character Recognition (OCR). Optical Character Recognition (OCR) is the correct Azure Computer Vision capability because it is specifically designed to extract printed or handwritten text from images, including serial numbers with varied fonts, sizes, and rotations. Azure's OCR API (part of Computer Vision) can handle skewed or rotated text by automatically detecting and correcting orientation before recognizing characters, making it ideal for warehouse labels that are not perfectly aligned.

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Object detection

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Image classification

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Facial recognition

Question 3

A financial institution uses an AI model to assess creditworthiness for loan applications. After deployment, they discover that the model assigns higher risk scores to applicants from certain postal codes, which are predominantly low-income minority neighborhoods. The model's predictions are accurate according to historical data, but the bank is concerned about ethical implications. Which Microsoft responsible AI principle is most directly applicable to addressing this issue?

Accepted Answer

Fairness. The model's assignment of higher risk scores based on postal codes, which correlate with low-income minority neighborhoods, directly violates the Fairness principle. This principle requires AI systems to treat all groups equitably and avoid reinforcing societal biases, even if the model's predictions are statistically accurate according to historical data. The bank's ethical concern centers on disparate impact, which fairness assessments (e.g., demographic parity or equal opportunity metrics) are designed to detect and mitigate.

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Inclusiveness

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Reliability and Safety

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Privacy and Security

Question 4

A retail company wants to automatically analyze thousands of product reviews to identify the most frequently mentioned aspects, such as 'battery life', 'screen quality', and 'customer service'. They plan to use a prebuilt Azure AI Language feature without any custom training. Which feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct choice because it is specifically designed to identify and extract the most important words or phrases from unstructured text, such as product reviews. This prebuilt Azure AI Language feature requires no custom training and directly surfaces frequently mentioned aspects like 'battery life' or 'screen quality' by analyzing term frequency and relevance.

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Text Analytics for Health

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Entity linking

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Sentiment analysis

Question 5

A retail store wants to use Azure Computer Vision to count the number of people entering a store from live video feeds. Which prebuilt Azure Computer Vision capability should they use?

Accepted Answer

People Detection. People Detection is a specialized prebuilt capability within Azure Computer Vision that is designed specifically to detect and count people in images or video frames. Unlike generic object detection, it is optimized for identifying human figures regardless of pose or occlusion, making it ideal for counting store entries from live video feeds.

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Image classification

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Object detection

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Optical Character Recognition (OCR)

Question 6

A developer uses Azure OpenAI to generate marketing copy. They want the model to follow a very specific tone and style. They provide a few high-quality examples of desired output before the actual prompt. Which technique is the developer using?

Accepted Answer

Few-shot learning. The developer is using few-shot learning, which involves providing a small number of high-quality examples (the 'shots') in the prompt to guide the model's output toward a desired tone and style. This technique leverages the model's in-context learning ability without updating its weights, making it ideal for quick adaptation to specific formatting or voice requirements.

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Zero-shot learning

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Fine-tuning

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Reinforcement learning with human feedback (RLHF)

Question 7

A developer uses Azure OpenAI Service to generate conversation scripts for a chatbot. The developer wants to encourage the model to introduce new topics and avoid repeatedly discussing the same subject matter. Which parameter should the developer increase?

Accepted Answer

Presence penalty. The Presence penalty parameter penalizes tokens that have already appeared in the conversation, encouraging the model to introduce new topics and avoid repetition. By increasing this value, the developer reduces the likelihood of the model reusing the same subject matter, which is exactly the requirement described.

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Temperature

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Top_p (nucleus sampling)

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Frequency penalty

Question 8

A company uses Azure OpenAI Service to generate marketing copy. They want to ensure that the generated text does not contain offensive language or harmful stereotypes, even if the prompt inadvertently leads the model in that direction. Which Azure OpenAI feature should they configure to help prevent such outputs?

Accepted Answer

Content filtering. Content filtering in Azure OpenAI Service uses a set of pre-built, configurable filters to detect and block harmful content categories such as hate, violence, sexual, and self-harm. This feature operates at the service level, intercepting both prompts and completions to prevent offensive language or harmful stereotypes from being generated, regardless of how the prompt is phrased.

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Prompt engineering

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Fine-tuning

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Few-shot learning

Question 9

What is 'Copilot' in Microsoft's AI strategy and how does it relate to Azure OpenAI?

Accepted Answer

Microsoft's family of AI assistants embedded across products, powered by Azure OpenAI models. Microsoft's 'Copilot' is a family of AI assistants integrated into products like Microsoft 365, GitHub, and Windows, which leverage Azure OpenAI models (including GPT-4) to provide natural language interactions and task automation. This directly aligns with the AI-900 domain of describing generative AI workloads on Azure, as Copilot exemplifies how Azure OpenAI's capabilities are embedded into end-user experiences.

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A Microsoft flight simulation game that teaches users to pilot aircraft

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An open-source framework for building custom AI assistants independent of Microsoft

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A secondary AI model that reviews and validates the primary model's outputs

Question 10

A university uses an AI system to screen scholarship applications. The system was trained on historical data that mostly awarded scholarships to students from STEM majors. Consequently, the system consistently gives lower scores to equally qualified students from humanities and arts majors. Which Microsoft responsible AI principle is most directly being violated by this outcome?

Accepted Answer

Fairness. The AI system's training data caused it to learn a biased pattern that systematically disadvantages humanities and arts applicants, which directly violates the fairness principle. Fairness in responsible AI requires that systems treat all groups equitably and do not perpetuate or amplify existing biases, especially when making high-stakes decisions like scholarship awards.

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Reliability and safety

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Privacy and security

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Transparency

Question 11

A logistics company uses overhead cameras at a shipping dock to read labels on packages. The labels contain text in various fonts, sizes, and orientations, and sometimes the text is partially obscured. Which Azure Computer Vision capability should they use to extract the text from these labels?

Accepted Answer

Optical Character Recognition (OCR). Optical Character Recognition (OCR) is the correct choice because it is specifically designed to extract printed or handwritten text from images, handling variations in fonts, sizes, orientations, and partial occlusion. Azure Computer Vision's OCR API (Read API) uses deep-learning models to detect and digitize text from natural scenes, making it ideal for reading labels on packages in a logistics environment.

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Object detection

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Image classification

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Semantic segmentation

Question 12

What is 'Azure Percept' (now deprecated) and what role did it play in edge AI?

Accepted Answer

An edge AI hardware platform for deploying vision and speech AI models locally on devices. Azure Percept was a hardware and software platform designed to bring AI inference to the edge, specifically for vision and speech workloads. It included the Azure Percept DK (developer kit) with an Intel Movidius Myriad X VPU, enabling local processing of AI models without constant cloud connectivity. This made it ideal for low-latency, offline scenarios like manufacturing quality inspection or smart retail.

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A cloud-only AI service for high-accuracy computer vision inference

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A perception layer in the Azure networking stack for monitoring packet loss

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A service for perceiving user intent from mouse movements and keyboard patterns

Question 13

What is a vector database and why is it important for generative AI applications?

Accepted Answer

A database optimized for storing and searching high-dimensional embeddings for semantic similarity search. A vector database is specifically designed to store and index high-dimensional embeddings—numerical representations of data such as text, images, or audio—and to perform efficient similarity searches using distance metrics like cosine similarity or Euclidean distance. In generative AI applications, vector databases enable retrieval-augmented generation (RAG), where relevant context is retrieved from a knowledge base to ground the model's output, reducing hallucinations and improving accuracy.

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A database that stores traditional relational tables for AI training data

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A database that stores the weights of trained neural networks

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A database using vector graphics for visualizing AI models

Question 14

A hospital receives patient notes in free text. They need to automatically identify entities like disease names, medications, and dosages from these notes without requiring any custom training. Which Azure AI Language feature is specifically designed for this medical entity extraction task?

Accepted Answer

Text Analytics for Health. Text Analytics for Health is a pre-built Azure AI Language feature specifically designed to extract medical entities such as diseases, medications, dosages, symptoms, and procedures from unstructured clinical text without requiring any custom training. It uses specialized medical ontologies (e.g., UMLS, SNOMED CT) and natural language processing models trained on healthcare data, making it the correct choice for this task.

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Custom Named Entity Recognition

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Key Phrase Extraction

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Sentiment Analysis

Question 15

A global e-commerce company develops a chatbot to assist customers in multiple languages. The chatbot uses text-based responses. To ensure it serves diverse populations fairly, which Microsoft responsible AI principle should they prioritize?

Accepted Answer

Inclusiveness. Inclusiveness is the correct principle because the chatbot must serve customers in multiple languages without bias or exclusion. Microsoft's responsible AI principle of inclusiveness ensures that AI systems are designed to empower everyone, including people of diverse backgrounds, languages, and abilities. By prioritizing inclusiveness, the company ensures the chatbot's text-based responses are accessible and fair across all supported languages.

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Accountability

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Privacy and security

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Transparency

Question 16

A logistics company uses security cameras to monitor boxes on warehouse shelves. They need an AI solution that can count the number of boxes on each shelf and also identify if any box is red (indicating a priority shipment). Which Azure Computer Vision capability should they use?

Accepted Answer

Image Analysis (object detection). Image Analysis with object detection can identify and localize multiple objects (boxes) within an image, count them, and detect specific attributes like color (red boxes) by analyzing pixel values in the detected bounding boxes. This directly meets the requirement to count boxes and identify priority shipments based on color.

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Optical Character Recognition (OCR)

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Face detection

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Spatial analysis

Question 17

What is 'semantic kernel' in Microsoft's AI development ecosystem?

Accepted Answer

An open-source SDK for orchestrating LLMs with plugins, memory, and planning for AI applications. Semantic Kernel is an open-source SDK that enables developers to integrate large language models (LLMs) with their applications by providing abstractions for plugins, memory (vector storage), and planning (automatic orchestration of AI tasks). It is not a core algorithm, a database, or a kernel modification, but rather a lightweight orchestrator that works with Azure OpenAI and other LLM providers.

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The core algorithm that powers all Azure AI services internally

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A database for storing semantic embeddings in Azure

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A Linux kernel modification for optimized AI workloads

Question 18

Which AI principle ensures that AI systems are developed and used in ways that are transparent and understandable to affected stakeholders?

Accepted Answer

Transparency. Transparency is the correct answer because it directly addresses the requirement that AI systems be open, explainable, and understandable to stakeholders. This principle ensures that decisions made by AI models can be audited, interpreted, and communicated clearly, which is essential for building trust and enabling informed consent.

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Reliability

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Fairness

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Privacy

Question 19

What does Azure AI Language's 'language detection' feature return?

Accepted Answer

The language name, ISO code, and a confidence score. Azure AI Language's language detection feature returns the language name, its ISO 639-1 code, and a confidence score between 0 and 1. This allows applications to identify the language of input text with a quantifiable level of certainty, enabling downstream processing like routing to the correct language-specific model.

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Only the language name in English

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A translated version of the text in English

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Only a binary yes/no for whether the text is English

Question 20

A bike-sharing company wants to predict the number of rentals per hour. Their model's predictions are usually close but occasionally have large errors due to unexpected events like sudden rain. They want a metric that heavily penalizes these large errors to ensure the model is not overly confident. Which evaluation metric should they primarily use?

Accepted Answer

Mean Squared Error (MSE). Mean Squared Error (MSE) is the correct choice because it squares the residuals, which heavily penalizes large errors. Since the bike-sharing company wants to discourage occasional large prediction errors (e.g., due to sudden rain), MSE’s quadratic penalty ensures that models with even a few large outliers receive a much worse score, forcing the model to avoid overconfidence.

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Mean Absolute Error (MAE)

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Classification Accuracy

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R-squared