AI-900 Practice Test 1 — 10 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