AI-900 Describe features of generative AI workloads on Azure Practice Test 10 — 15 Questions

Question 1

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 2

What is Microsoft 365 Copilot and how does it use generative AI?

Accepted Answer

LLM-powered AI assistance embedded in Word, Excel, PowerPoint, and Teams for productivity tasks. Microsoft 365 Copilot is an AI assistant that integrates large language models (LLMs) with Microsoft Graph data and Microsoft 365 apps. It uses generative AI to create, summarize, and analyze content directly within Word, Excel, PowerPoint, and Teams, enhancing productivity without replacing the existing Office interface.

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An AI assistant that replaces Microsoft Office with a conversational interface

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An automated backup system for Microsoft 365 documents

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A virtual employee that works independently in Microsoft Teams

Question 3

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

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Small language models from Microsoft Research that achieve strong reasoning performance at compact size. The 'phi' family of models are small language models (SLMs) developed by Microsoft Research that achieve strong reasoning and language understanding performance despite their compact size. They are designed to run efficiently on resource-constrained devices, making them distinctive for edge and offline scenarios where large models are impractical.

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Large multimodal models that process images, audio, and text simultaneously

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Models specialized exclusively for mathematical calculations

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A family of image generation models for creative AI tasks

Question 4

What is the 'frequency penalty' parameter in Azure OpenAI API calls?

Accepted Answer

A parameter that reduces repetition of words already present in the response. The 'frequency penalty' parameter in Azure OpenAI API calls is designed to reduce the likelihood of the model repeating words or phrases that have already appeared in the generated response. It works by applying a penalty proportional to the frequency of tokens already used, encouraging more diverse and less repetitive text output. This is distinct from the 'presence penalty', which penalizes tokens based on whether they have appeared at all, regardless of frequency.

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A cost multiplier based on how often you call the API

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A rate limiting parameter controlling maximum API calls per minute

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A filter that removes profanity based on how frequently it appears

Question 5

What are 'plugins' or 'tools' in the context of AI agents and Microsoft Copilot?

Accepted Answer

Extensions that give AI models the ability to call external APIs and take actions beyond text generation. Plugins (or tools) in AI agents and Microsoft Copilot are extensions that enable the AI to call external APIs, retrieve real-time data, or perform actions beyond text generation. This allows the AI to interact with services like databases, calendars, or custom business logic, making it an agent capable of executing tasks rather than just generating static responses.

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Browser extensions that block AI-generated content on websites

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Audio plugins for improving AI speech synthesis quality

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Software updates for Azure OpenAI service deployments

Question 6

What is the Azure AI Evaluation SDK used for in generative AI development?

Accepted Answer

Systematically measuring quality (groundedness, relevance, coherence) and safety of generative AI responses. The Azure AI Evaluation SDK is specifically designed to systematically measure the quality and safety of generative AI responses. It evaluates key metrics such as groundedness (how well the response aligns with source data), relevance, and coherence, as well as safety aspects like content filtering and harm detection. This makes it essential for validating and improving generative AI applications before deployment.

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Evaluating the environmental impact of AI model training

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Evaluating Azure subscription costs for AI workloads

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A peer review system for human evaluation of AI responses

Question 7

What is 'tool calling' (function calling) in Azure OpenAI?

Accepted Answer

A feature allowing models to specify structured calls to external functions for real-world actions. Tool calling (function calling) in Azure OpenAI is a feature that allows the model to output structured JSON requests to invoke external functions or APIs, enabling it to perform real-world actions like querying databases or sending emails. This bridges the gap between the model's static knowledge and dynamic, up-to-date data or services.

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The Azure OpenAI API endpoint URL used to call the model

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Calling Azure support when the AI model returns incorrect results

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A billing mechanism for counting API function calls per minute

Question 8

What is the GPT-4o model in Azure OpenAI?

Accepted Answer

A multimodal model that natively processes text, images, and audio inputs and outputs. GPT-4o is a multimodal model in Azure OpenAI that natively processes and generates text, images, and audio inputs and outputs. Unlike earlier GPT-4 versions that required separate models or pipelines for different modalities, GPT-4o integrates these capabilities into a single unified model, enabling richer interactions such as analyzing an image and responding with spoken audio.

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A text-only model optimized for faster response speeds than GPT-4

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A model specialized for generating only programming code

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An older, less capable version of GPT-4

Question 9

What is 'Azure AI Foundry' and what is its primary purpose?

Accepted Answer

A unified enterprise platform for building, evaluating, and deploying AI applications with the full development lifecycle. Azure AI Foundry is a unified enterprise platform that provides an integrated environment for building, evaluating, and deploying AI applications across the full development lifecycle. It combines tools for data preparation, model training, evaluation, and deployment, enabling teams to manage AI projects from ideation to production within a single interface.

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A physical Microsoft facility for AI hardware manufacturing

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A subscription tier that includes all Azure AI services at a fixed monthly price

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A training platform specifically for AI engineers at Microsoft

Question 10

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 11

What is GitHub Copilot and how does it use AI?

Accepted Answer

An AI-powered code assistant that generates code completions and suggestions in IDEs using LLMs. GitHub Copilot is an AI-powered code assistant developed by GitHub and OpenAI. It uses large language models (LLMs), specifically a version of OpenAI's Codex model, to analyze the context of the code a developer is writing in an IDE (like VS Code) and generate real-time code completions, suggestions, and even entire functions. This directly aligns with generative AI workloads on Azure, as Copilot leverages generative AI to produce new code content based on natural language prompts or existing code patterns.

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An automated GitHub Actions workflow for running CI/CD pipelines

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A bot that automatically reviews and merges GitHub pull requests

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A GitHub feature for visualizing code repository history

Question 12

What is the 'presence penalty' parameter in Azure OpenAI API calls?

Accepted Answer

A flat penalty discouraging repetition of any token already present in the response. The 'presence penalty' parameter in Azure OpenAI API calls applies a flat penalty to any token that has already appeared in the response so far, reducing the model's likelihood of repeating that token. This helps generate more diverse and less repetitive text by discouraging the reuse of tokens already present in the output sequence.

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A parameter requiring AI systems to acknowledge their presence as AI to users

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A parameter indicating whether the AI is present online or offline

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The minimum number of characters that must be present in a response

Question 13

What is 'retrieval augmented generation' (RAG) and which Azure services typically implement it?

Accepted Answer

Combining Azure AI Search (retrieval) with Azure OpenAI (generation) to ground LLM responses in a knowledge base. Retrieval Augmented Generation (RAG) is a pattern that combines a retrieval step with a generative step. In Azure, this is typically implemented by using Azure AI Search to retrieve relevant documents or chunks from a knowledge base, then passing those results as context to an Azure OpenAI model (e.g., GPT-4) to generate a grounded, fact-based response. This approach reduces hallucinations and ensures the output is based on authoritative data rather than the model's training data alone.

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Using Azure Storage to retrieve training data for model fine-tuning

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Using Azure CDN to deliver AI-generated content faster globally

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A method of compressing large datasets before training language models

Question 14

What is 'grounding' in the context of Azure OpenAI and Retrieval-Augmented Generation?

Accepted Answer

Anchoring model responses to specific, retrieved source documents to improve factual accuracy. Grounding in Azure OpenAI and Retrieval-Augmented Generation (RAG) refers to the practice of anchoring the model's responses to specific, retrieved source documents. This ensures that the generated output is factually accurate and verifiable, reducing the risk of hallucination by constraining the model to use only the provided context.

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Connecting the model to electrical ground to prevent static during training

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The process of converting floating-point weights to integer values for deployment

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Setting the baseline performance metrics before model fine-tuning begins

Question 15

What is a 'system message' (system prompt) in Azure OpenAI chat models?

Accepted Answer

A developer-set instruction that defines the model's role, persona, and behavioural constraints. A system message (system prompt) in Azure OpenAI chat models is a developer-defined instruction that sets the model's role, persona, and behavioral constraints. This prompt is sent as part of the conversation context to guide the model's responses, ensuring it adheres to specific guidelines, tone, or safety rules. It is not an error notification, automated confirmation, or user input.

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An error notification sent by Azure when the OpenAI service is unavailable

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Automated messages the model sends to confirm it received the user's input

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The first message a user sends to start a new conversation session