AI-900 Practice Exam 1 — 20 Questions

Question 1

What is the purpose of the Azure AI Document Intelligence's prebuilt models?

Accepted Answer

Extracting structured data from common document types (invoices, receipts, IDs) without custom training. Azure AI Document Intelligence's prebuilt models are designed to extract structured data from common document types such as invoices, receipts, and IDs without requiring any custom training. They leverage pre-trained neural networks that recognize fields like invoice totals, receipt line items, and ID numbers, enabling rapid data extraction for standard forms. This aligns with the purpose of reducing manual data entry and accelerating document processing workflows.

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Training custom document extraction models for unique business forms

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Translating documents from one language to another

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Converting documents to PDF format for archiving

Question 2

A creative agency wants to use Azure OpenAI to generate marketing images from text descriptions. They need to ensure that the generated images are appropriate for all audiences by automatically blocking sexually explicit or violent content. Which Azure OpenAI feature should they configure to meet this requirement?

Accepted Answer

Enable content filtering on the DALL-E deployment. Azure OpenAI's DALL-E deployment includes built-in content filtering that automatically blocks sexually explicit, violent, or otherwise inappropriate images from being generated. This feature is configured at the deployment level and requires no custom model training, making it the simplest and most effective way to meet the requirement for all-audience appropriateness.

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Use the GPT-4 model with safety prompts

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Train a custom image classification model to filter outputs

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Use the Embeddings model to detect inappropriate content

Question 3

A robotics team is training a robot to navigate a maze. The robot receives a positive reward (+10) when it reaches the exit and a negative reward (-1) every time it bumps into a wall. The robot learns to maximize its cumulative reward over multiple trials. Which type of machine learning is being used?

Accepted Answer

Reinforcement learning. The robot learns by interacting with its environment, receiving rewards (positive for reaching the exit, negative for bumping into walls), and adjusting its behavior to maximize cumulative reward over time. This trial-and-error learning process, where an agent learns a policy through feedback from its actions, is the defining characteristic of reinforcement learning.

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Supervised learning

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Unsupervised learning

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Semi-supervised learning

Question 4

What is 'voice cloning' in Azure AI Speech's custom neural voice and what are its ethical safeguards?

Accepted Answer

Creating a synthetic voice model from recordings with consent requirements and ethical safeguards. Voice cloning in Azure AI Speech's custom neural voice refers to creating a synthetic voice model from recorded speech samples, which requires explicit consent from the voice donor. Azure enforces strict ethical safeguards, including a code of conduct, identity verification, and usage restrictions to prevent misuse, such as deepfakes or unauthorized impersonation.

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Automatically improving the audio quality of poor recordings by removing noise

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Cloning a voice without the person's knowledge to create realistic audio deepfakes

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Copying a standard Azure voice model and deploying it in a private Azure subscription

Question 5

What is 'Azure AI Content Safety Studio' and what does it help you do?

Accepted Answer

A web portal for testing harm detection, configuring thresholds, and managing blocklists for content safety. Azure AI Content Safety Studio is a web-based portal that allows you to test and evaluate content safety models, configure severity thresholds for harm detection (e.g., hate, violence, self-harm), and manage custom blocklists. It helps you validate and fine-tune content filtering policies before deploying them in production, ensuring responsible AI practices.

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A recording studio application for creating AI-generated audio content safely

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A compliance certification studio for submitting AI applications for safety approval

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A tool for monitoring content safety violations in production across all Azure AI deployments

Question 6

What is 'Azure OpenAI's batch API' and when should you use it?

Accepted Answer

Asynchronous bulk processing of large inference request volumes at reduced cost. Azure OpenAI's Batch API is designed for asynchronous processing of large volumes of inference requests, such as chat completions or embeddings, at a reduced cost compared to real-time API calls. It is ideal for workloads where immediate responses are not required, allowing you to submit a batch of requests and retrieve results later. This makes it a cost-effective solution for high-throughput, non-latency-sensitive tasks.

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An API for training new models in batches on your custom datasets

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Grouping multiple Azure OpenAI API keys into a batch for easier management

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A tool for running multiple prompt experiments simultaneously to find the best prompt

Question 7

A market research company wants to analyze thousands of product reviews to identify the most frequently talked-about topics (such as 'battery life', 'screen quality', 'customer support') to guide product improvements. Which Azure AI Language feature is best suited for this task?

Accepted Answer

Key phrase extraction. Key phrase extraction is designed to identify the main points or topics in a body of text, making it the ideal choice for surfacing frequently mentioned themes like 'battery life' or 'screen quality' from thousands of product reviews. It returns a list of key phrases that represent the most salient concepts, directly supporting the goal of guiding product improvements based on customer feedback.

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Sentiment analysis

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Entity recognition

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Language detection

Question 8

What is 'code generation' as a generative AI capability and how is it used in development?

Accepted Answer

AI producing programming code from natural language descriptions — used in IDEs and developer tools. Code generation in generative AI refers to the model's ability to produce programming code directly from natural language prompts or partial code inputs. This capability is integrated into IDEs and developer tools (e.g., GitHub Copilot, Azure OpenAI Service) to assist developers by suggesting functions, completing lines, or generating entire code blocks, thereby accelerating development and reducing boilerplate coding.

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Automatically compiling source code into executable binaries

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Scanning existing code for security vulnerabilities and generating a fix automatically

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Auto-generating boilerplate project structure files when creating a new repository

Question 9

A city council deploys an AI system to analyze surveillance footage and automatically issue traffic violation fines. They want to ensure the system does not disproportionately target one type of vehicle (e.g., bicycles over cars) when issuing fines. Which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Fairness. The scenario describes a risk of algorithmic bias where the AI system might disproportionately issue fines to bicycles over cars. The Microsoft responsible AI principle of Fairness directly addresses this by requiring that AI systems treat all groups equitably and avoid discrimination based on protected attributes. Ensuring fairness involves auditing the model's predictions across different vehicle types and mitigating any statistical disparities.

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Inclusiveness

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Reliability and safety

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Transparency

Question 10

What is natural language processing (NLP)?

Accepted Answer

A branch of AI that enables computers to understand and generate human language. Natural language processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and human language. It enables machines to read, interpret, generate, and respond to text or speech in a way that is both meaningful and contextually relevant, using techniques such as tokenization, part-of-speech tagging, named entity recognition, and language modeling.

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The process of converting programming language code into machine code

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A networking protocol for processing data transmissions

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A type of database query language for natural language questions

Question 11

What is 'temperature' in the context of generative AI model parameters?

Accepted Answer

A parameter controlling the randomness and creativity of model outputs. Temperature is a hyperparameter in generative AI models (such as GPT) that controls the randomness of token sampling during text generation. A higher temperature (e.g., 1.0) increases creativity by making less probable tokens more likely to be chosen, while a lower temperature (e.g., 0.1) makes the output more deterministic and focused on the most probable tokens. This directly affects the diversity and novelty of the generated content.

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The operating temperature of the GPU hardware running the model

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The time required to generate a response

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The minimum confidence threshold for a response

Question 12

What is the difference between Azure OpenAI Service and the public OpenAI API?

Accepted Answer

Azure OpenAI adds enterprise security, compliance, private networking, and Azure integration. Azure OpenAI Service is a Microsoft Azure-based offering that wraps the same underlying OpenAI models (GPT-4, GPT-3.5, etc.) with enterprise-grade features such as Azure Active Directory authentication, private endpoints via Azure Virtual Network, compliance certifications (e.g., ISO 27001, SOC 2), and seamless integration with other Azure services like Cognitive Search and Logic Apps. The public OpenAI API lacks these enterprise controls, making Azure OpenAI the preferred choice for organizations that require data residency, network isolation, and managed identity access.

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Azure OpenAI has different models that perform better than OpenAI

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Azure OpenAI is only available for government customers

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Azure OpenAI does not support GPT-4 models

Question 13

What is a confusion matrix's 'false positive' in medical screening?

Accepted Answer

A patient predicted to have a disease who is actually healthy. In a confusion matrix, a false positive occurs when the model predicts a positive outcome (e.g., disease present) but the actual ground truth is negative (healthy). This is a Type I error, and in medical screening it represents a healthy patient incorrectly flagged as having the disease, leading to unnecessary follow-up tests and anxiety.

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A patient who tests positive and actually has the disease

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A patient who tests negative but actually has the disease

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A patient correctly identified as healthy by the model

Question 14

What is 'Azure Machine Learning notebooks' and who typically uses them?

Accepted Answer

Interactive Jupyter notebook environments for data exploration and model prototyping by data scientists. Azure Machine Learning notebooks are interactive Jupyter notebook environments hosted within Azure Machine Learning studio. They allow data scientists to write and execute Python code for data exploration, visualization, and model prototyping directly in the cloud, with built-in access to compute instances and datasets. Option B correctly identifies both the technology (Jupyter notebooks) and the primary user role (data scientists).

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Digital note-taking applications for recording meeting minutes during ML project planning

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Automated logging notebooks that record all model training metrics without code

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Read-only document viewers for reviewing completed ML experiment results

Question 15

What is the 'Azure AI Language SDK' and what programming languages does it support?

Accepted Answer

Client libraries for Python, .NET, Java, and JavaScript for programmatic access to Azure AI Language. The Azure AI Language SDK is a set of client libraries that allow developers to integrate Azure AI Language capabilities—such as sentiment analysis, key phrase extraction, and language understanding—directly into their applications. It supports Python, .NET, Java, and JavaScript, enabling programmatic access to the service via RESTful APIs or native SDK methods.

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A new programming language created by Microsoft for building NLP applications

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A software development kit for building physical language translation devices

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An IDE plugin that adds Azure AI Language auto-complete to code editors

Question 16

A company wants to build a chatbot that can answer customer questions about their product return policy, shipping times, and warranty information. They have a structured document with these questions and answers. Which Azure AI Language feature should they use to create this chatbot without writing custom code?

Accepted Answer

Custom Question Answering. Custom Question Answering (C) is the correct choice because it is specifically designed to create a chatbot that answers questions based on a structured document (e.g., FAQ, product manual) without writing custom code. It uses a predefined knowledge base of question-answer pairs and provides a built-in orchestration for bot integration, making it ideal for this scenario.

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Conversational Language Understanding (CLU)

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Custom Text Classification

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Language Detection

Question 17

What is 'image generation quality' evaluation — how do you measure if a generated image is good?

Accepted Answer

Metrics like FID (image distribution similarity) and CLIP score (prompt adherence), plus human evaluation. Image generation quality is evaluated using a combination of automated metrics and human judgment. FID (Fréchet Inception Distance) measures how similar the distribution of generated images is to real images, while CLIP score assesses how well the image aligns with the given text prompt. Human evaluation is also critical to capture perceptual quality that automated metrics may miss, such as aesthetic appeal and contextual coherence.

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Only image resolution and file size — higher resolution means better quality

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Simply asking the model what score it gives its own output

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Counting the number of objects correctly included vs. missing from the prompt

Question 18

What is 'Azure AI Language's text analytics for health' (TA4H) and who uses it?

Accepted Answer

A pre-built NLP service for extracting medical entities from clinical text, linked to standard terminologies. Azure AI Language's text analytics for health (TA4H) is a pre-built natural language processing (NLP) service specifically designed to extract medical entities—such as diagnoses, medications, symptoms, and procedures—from unstructured clinical text. It links these entities to standard medical terminologies like SNOMED CT, ICD-10-CM, and RxNorm, enabling structured analysis of health records without requiring custom model training.

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A health monitoring system that analyses patient wearable data for anomalies

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A service for doctors to receive AI-generated medical advice based on their queries

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A healthcare compliance tool that checks medical records for documentation errors

Question 19

What is 'coreference resolution' in natural language processing?

Accepted Answer

Identifying which words or phrases in a text refer to the same real-world entity. Coreference resolution is the NLP task of identifying when two or more expressions in a text refer to the same real-world entity. For example, in 'Alice said she would come,' the pronoun 'she' corefers to 'Alice.' This is fundamental for tasks like document summarization and question answering, where maintaining entity consistency is critical.

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Checking whether a document's references (citations) are correctly formatted

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Resolving conflicts when multiple languages are mixed in the same document

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Matching database foreign keys to their referenced primary keys

Question 20

What is the purpose of Azure AI Vision's 'product recognition' feature?

Accepted Answer

Identifying retail products in images to match them to a product catalog without barcodes. Azure AI Vision's 'product recognition' feature is designed to identify retail products in images and match them to a product catalog without relying on barcodes. It uses computer vision models trained on product images to detect and recognize items based on visual features like packaging, logos, and shape, enabling inventory management and checkout automation in retail scenarios.

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Recognizing counterfeit products in supply chain images

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Recognizing products mentioned in customer text reviews

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Detecting product defects in manufacturing quality control