AI-900 Describe features of generative AI workloads on Azure Practice Test 12 — 15 Questions

Question 1

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 2

What is 'prompt flow' in Azure AI Foundry?

Accepted Answer

A visual development tool for building, testing, and deploying LLM application pipelines. Prompt flow in Azure AI Foundry is a visual development tool that enables developers to design, test, and deploy end-to-end pipelines for large language model (LLM) applications. It provides a graph-based interface to orchestrate LLM calls, data processing, and custom logic, making it easier to build complex generative AI workflows without writing extensive code.

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A tool for managing the queue of prompt requests sent to Azure OpenAI during peak usage

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An automated system that suggests improvements to prompts based on output quality metrics

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A monitoring dashboard showing the flow of prompts through an AI application in production

Question 3

What is 'evaluation' of generative AI models in Azure AI Foundry?

Accepted Answer

Systematically measuring a generative AI application's quality (groundedness, relevance) and safety metrics. In Azure AI Foundry, evaluation refers to the systematic measurement of a generative AI application's quality and safety using predefined metrics such as groundedness (factual alignment with source data), relevance, and safety (e.g., content filtering). This process is distinct from ad-hoc user feedback or training diagnostics, as it provides structured, repeatable assessments to validate model behavior before deployment.

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The process of assessing job candidates using AI-powered assessments

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Having users rate the AI's responses with thumbs up or thumbs down during beta testing

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Running model training and measuring loss curves to determine when to stop training

Question 4

What is 'zero-shot prompting' and how does it work?

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Asking the model to perform a task without any examples, relying on pre-trained knowledge. Zero-shot prompting refers to instructing a generative AI model to perform a task without providing any examples in the prompt. The model relies entirely on its pre-trained knowledge—gained from vast datasets during training—to interpret the instruction and generate a relevant response. This is a core capability of large language models (LLMs) like GPT-4, enabling them to generalize to unseen tasks without task-specific fine-tuning.

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Running the model for zero seconds to test if the API connection works

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Prompting the model to generate a response with zero errors or hallucinations

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A technique that removes all instructions from the prompt to test raw model behaviour

Question 5

What is 'retrieval-augmented generation' (RAG) and what problem does it solve?

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Retrieving relevant documents from a knowledge base to provide accurate context for LLM responses. Retrieval-augmented generation (RAG) combines a retrieval step with a generative language model. It first retrieves relevant documents or passages from an external knowledge base (e.g., Azure Cognitive Search) and then feeds that context into the LLM to ground its response. This solves the problem of LLMs producing outdated, hallucinated, or factually incorrect answers by ensuring the model has access to current, authoritative information.

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Storing model responses in a cache to retrieve them faster for repeated questions

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Generating random responses and selecting the most relevant using a ranker model

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A technique for making LLM responses shorter by removing irrelevant sections

Question 6

What is 'text generation' as a generative AI capability and what are common use cases?

Accepted Answer

Creating new coherent text from prompts for writing, code, summaries, and conversational AI. Text generation in generative AI refers to the capability of models (like GPT-4 or GPT-3.5) to produce new, coherent text based on a given prompt. This includes tasks such as writing articles, generating code, creating summaries, and powering conversational AI agents. The key distinction is that the output is novel content, not a direct extraction or transformation of existing text.

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Extracting and copying text from scanned images using OCR

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Converting speech audio into a written transcript

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Formatting existing text by adding headings, bullets, and correct punctuation

Question 7

What is 'structured output' (JSON mode) in Azure OpenAI?

Accepted Answer

Constraining model responses to valid JSON conforming to a specified schema for application integration. Structured output (JSON mode) in Azure OpenAI constrains the model to generate responses that are valid JSON objects conforming to a user-defined schema. This is achieved by setting the `response_format` parameter to `{ "type": "json_object" }` and optionally providing a JSON schema via the `json_schema` parameter, ensuring the output can be directly parsed and integrated into applications without additional formatting logic.

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Formatting the model's text response with numbered sections and bullet points

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Saving model responses to a structured database table automatically

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Generating output in multiple languages simultaneously in a structured format

Question 8

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 9

What is 'agentic AI' and how does it differ from a simple chatbot?

Accepted Answer

AI that autonomously plans and executes multi-step workflows using tools to accomplish complex goals. Agentic AI refers to AI systems that can autonomously plan and execute multi-step workflows by using external tools, APIs, or data sources to achieve complex goals. This differs from a simple chatbot, which typically responds to user prompts in a single turn without independent goal-setting or tool orchestration. In generative AI workloads on Azure, agentic AI might leverage Azure AI Agent Service or Semantic Kernel to chain together calls to Azure Cognitive Search, Azure Functions, or external APIs, enabling tasks like automated report generation or multi-step data analysis.

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AI that represents a company as a legal agent for contractual purposes

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Chatbots that can respond on behalf of a company's customer service team

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AI models that were trained by multiple agents working simultaneously in parallel

Question 10

What is the 'Phi' family of models in Azure AI Foundry and what makes them distinctive?

Accepted Answer

Microsoft's small language models that achieve high capability at much smaller parameter counts. The Phi family consists of small language models (SLMs) developed by Microsoft that achieve high performance on reasoning and language tasks despite having significantly fewer parameters than large models like GPT-4. Their distinctive design uses high-quality training data and novel scaling techniques to deliver competitive capability with lower computational cost, making them ideal for resource-constrained environments and real-time applications.

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Large models from OpenAI that provide the highest capability for complex tasks

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Models specifically designed for processing and analysing structured financial data

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A family of image generation models competing with DALL-E for artistic content creation

Question 11

What is 'Microsoft Copilot Studio' and what is it used for?

Accepted Answer

A low-code platform for building custom AI bots and agents integrated with Microsoft 365. Microsoft Copilot Studio is a low-code platform that allows users to build custom AI-powered bots and agents that integrate seamlessly with Microsoft 365 services. It extends the capabilities of Microsoft Copilot by enabling tailored conversational experiences, such as automating workflows, answering queries, and handling tasks within the Microsoft ecosystem without requiring extensive coding.

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A professional audio/video editing suite powered by AI for content creators

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An IDE for enterprise developers building high-performance LLM applications in C#

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A tool for generating Copilot-branded marketing content for Microsoft partners

Question 12

What is 'Whisper' in Azure OpenAI and what can it do?

Accepted Answer

A speech recognition model that transcribes audio files to text across 100+ languages. Whisper is a speech recognition model available in Azure OpenAI that transcribes audio files into text. It supports over 100 languages and is designed for high accuracy in diverse acoustic environments, making it ideal for tasks like meeting transcription, voice note conversion, and multilingual audio processing.

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A low-power mode for running Azure OpenAI at reduced compute cost

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A secure communication channel for transmitting sensitive data to Azure OpenAI

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A text-to-speech model that generates very quiet, whispered audio output

Question 13

What is 'multi-agent systems' in the context of Azure AI and agentic workflows?

Accepted Answer

Multiple specialised AI agents that collaborate — each with different roles — to accomplish complex goals. In Azure AI and agentic workflows, a multi-agent system involves multiple specialized AI agents, each with distinct roles (e.g., planner, coder, reviewer), that collaborate to decompose and solve complex tasks. This architecture leverages the Azure AI Agent Service to orchestrate agent communication and task delegation, enabling more robust and scalable solutions than a single monolithic model.

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Running multiple instances of the same model simultaneously for load balancing

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AI systems deployed across multiple Azure regions for global availability

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Security agents that monitor AI systems for prompt injection and misuse

Question 14

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 15

What is 'temperature' parameter in Azure OpenAI and how does it affect output?

Accepted Answer

A parameter controlling output randomness — low values are deterministic, high values are creative. The temperature parameter in Azure OpenAI controls the randomness of the model's output. A low temperature (e.g., 0.0) makes the model deterministic, always choosing the most likely next token, while a high temperature (e.g., 1.0 or above) increases randomness, allowing for more creative and varied responses. This parameter directly influences the probability distribution over tokens before sampling.

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The compute temperature of GPU hardware during inference, affecting speed

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The time limit before a model inference request times out

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The sensitivity of the model's content filter — higher blocks more content