AI-900 Practice Exam 60 — 20 Questions

Question 1

A legal firm wants to use Azure OpenAI to generate summaries of lengthy contracts. The firm requires that the generated summaries are strictly based on the provided contract text and do not include any external knowledge or hallucinated facts. Which Azure OpenAI feature should the firm configure to meet this requirement?

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

Accepted Answer

Azure OpenAI on Your Data (data grounding). Azure OpenAI on Your Data (data grounding) restricts the model's responses to the content of the provided contract documents, preventing the generation of information not present in the source text. This feature uses a retrieval-augmented generation (RAG) approach, where the model only references the indexed contract data, effectively eliminating external knowledge or hallucinated facts.

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Content filtering

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Prompt engineering with system messages

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Fine-tuning the model on legal texts

Question 2

A company uses Azure OpenAI Service to generate marketing copy for a new product. They have a strict brand voice that requires formal, technical language and explicitly prohibits any humorous or informal phrases. They want to enforce these constraints without retraining the model. Which technique should they use?

Accepted Answer

B) Prompt engineering. Prompt engineering is correct because it allows the user to craft system messages or user prompts that explicitly instruct the model to use formal, technical language and avoid humor, all without modifying the underlying model weights. This technique leverages the model's instruction-following capability to enforce constraints at inference time, making it ideal for brand voice enforcement without retraining.

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A) Fine-tuning

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C) Reinforcement learning

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D) Transfer learning

Question 3

A city government deploys an AI system that automatically detects traffic violations (e.g., running red lights) from traffic camera footage. The system triggers fines without immediate human review. According to Microsoft's responsible AI principles, which principle is most directly concerned with ensuring there is human oversight and that the organization can be held liable for the system's decisions?

Accepted Answer

Accountability. The Accountability principle in Microsoft's responsible AI framework requires that organizations take ownership of AI system outcomes and ensure human oversight. In this scenario, the system automatically issues fines without human review, which directly challenges accountability because there is no mechanism for human intervention or liability assignment. This principle mandates that the organization must be able to answer for the system's decisions, including errors or biases.

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Transparency

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Reliability and Safety

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Privacy and Security

Question 4

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?

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 5

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 6

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 7

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 8

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 9

What is 'model deployment' in Azure OpenAI, and why are named deployments used?

Accepted Answer

A named instance of an AI model with allocated quota, enabling version control and quota management. Model deployment in Azure OpenAI creates a named instance of a specific model (e.g., GPT-4) with dedicated quota (tokens per minute, rate limits). Named deployments enable version control by pinning to a specific model version (e.g., 0613 vs. 1106) and allow separate quota management per deployment, which is critical for production workloads. This is distinct from simply calling an API endpoint; it provisions a dedicated inference endpoint with guaranteed capacity.

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The process of physically shipping AI hardware to Azure data centers

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Automatically scaling the number of model instances based on traffic

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The initial training step that produces an Azure OpenAI model

Question 10

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 11

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 12

What is conversational language understanding (CLU) in Azure AI Language?

Accepted Answer

A feature that trains models to understand user intent and extract entities from natural language. Conversational Language Understanding (CLU) is a feature within Azure AI Language that enables you to build custom models for extracting intents (what the user wants to do) and entities (key pieces of information) from natural language utterances. Unlike pre-built or translation services, CLU is specifically designed for training and deploying a natural language understanding model tailored to your application's domain.

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A service that translates chatbot responses into multiple languages

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A service that converts speech to text for voice assistants

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A pre-built AI for answering FAQ questions automatically

Question 13

A data scientist is training a regression model to predict house prices in Azure Machine Learning. The model uses features like square footage, number of bedrooms, and location (zip code). The data scientist notices that the model has a very low error on the training data but a high error on the test data. Which technique should the data scientist apply during model training to reduce overfitting by penalizing large coefficients?

Accepted Answer

Use a regularization algorithm like Lasso (L1).. Lasso (L1) regularization adds a penalty equal to the absolute value of the magnitude of coefficients, which can shrink some coefficients to zero, effectively performing feature selection and reducing overfitting. This directly addresses the problem of large coefficients causing the model to fit noise in the training data, leading to high test error.

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Use a smaller test set.

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Apply feature scaling only.

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Increase the number of training epochs.

Question 14

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 15

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 16

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 17

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 18

A legal firm needs to automatically extract custom entities such as 'Party Name', 'Effective Date', and 'Governing Law' from contracts. They have a set of manually labeled contracts to use as training data. Which Azure AI Language feature should they use?

Accepted Answer

Custom Named Entity Recognition (Custom NER). Custom Named Entity Recognition (Custom NER) is the correct choice because it allows the legal firm to train a model on their manually labeled contracts to extract domain-specific entities like 'Party Name', 'Effective Date', and 'Governing Law'. Unlike built-in NER, which only recognizes predefined entity types, Custom NER learns from the provided labeled data to identify custom categories tailored to the firm's needs.

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Key Phrase Extraction

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Entity Linking

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Built-in Named Entity Recognition

Question 19

A developer uses Azure OpenAI Service to generate data transformation scripts. The generated scripts sometimes contain logical errors. To make the model's output more deterministic and reduce variability, which parameter should the developer decrease?

Accepted Answer

Temperature. Temperature controls the randomness of the model's output. Lowering temperature (e.g., from 0.7 to 0.1) makes the model more deterministic and focused, reducing variability and the likelihood of logical errors in generated scripts. This is the correct parameter to adjust for more consistent, less creative responses.

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Top_p

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Frequency penalty

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Presence penalty

Question 20

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