AI-900 Describe features of generative AI workloads on Azure Practice Test 11 — 15 Questions

Question 1

What is 'content moderation' in the context of Azure OpenAI?

Accepted Answer

Automatically filtering and classifying inputs/outputs for harmful content categories. Content moderation in Azure OpenAI uses AI models to automatically scan both user prompts (inputs) and generated responses (outputs) for harmful content such as hate, violence, sexual material, and self-harm. It applies configurable severity filters (e.g., low, medium, high) to block or flag content that violates Microsoft's Responsible AI policies, ensuring safe deployment of generative AI workloads.

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Controlling how much content a user is allowed to generate per day

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Editing generated text to improve grammar and style

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Optimising prompt length to reduce token costs

Question 2

What is 'hallucination' in large language models and what techniques help reduce it?

Accepted Answer

When a model generates confident but factually incorrect or fabricated information. Hallucination in large language models (LLMs) refers to the generation of text that is confident, coherent, and plausible-sounding but factually incorrect or entirely fabricated. This occurs because LLMs are probabilistic next-token predictors trained on vast datasets, not databases of verified facts; they lack a built-in mechanism to distinguish truth from fiction. Techniques to reduce hallucination include grounding outputs with retrieval-augmented generation (RAG) using Azure AI Search, prompt engineering with system messages that constrain responses to verified sources, and fine-tuning with human feedback (RLHF) to penalize factual errors.

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When a model generates images instead of text in response to a text prompt

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When users imagine the AI is sentient due to very convincing responses

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When a model's training data contains copyrighted material it memorises

Question 3

What is 'semantic search' in Azure AI Search (cognitive search)?

Accepted Answer

Search that understands the meaning and intent of queries to return conceptually relevant results. Semantic search in Azure AI Search uses advanced AI models to understand the meaning and intent behind a user's query, rather than relying solely on keyword matching. It re-ranks search results based on conceptual relevance to the query, enabling the system to return results that are semantically related even if they don't contain the exact keywords. This is powered by Azure's deep learning models, including transformer-based language models, to capture the context and semantics of the search terms.

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A search that finds all documents containing the exact keywords typed by the user

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Searching for programming code by its semantic meaning in a code repository

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Restricting search results to documents tagged with specific metadata labels

Question 4

What is the 'Azure OpenAI Playground' and what is it used for?

Accepted Answer

A web-based interface for interactively testing Azure OpenAI models and prompts without coding. The Azure OpenAI Playground is a web-based interface that allows users to interactively test and experiment with Azure OpenAI models (like GPT-4, GPT-3.5, and DALL-E) by entering prompts and adjusting parameters (e.g., temperature, max tokens) without writing any code. It is used for rapid prototyping, prompt engineering, and evaluating model behavior before integrating into applications via the API.

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A children's educational game powered by Azure OpenAI for learning to code

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A sandboxed environment for running untrusted AI models safely

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A feature for generating synthetic training data for custom model fine-tuning

Question 5

What is 'prompt injection' and why is it a security concern for AI applications?

Accepted Answer

Malicious input that overrides an AI system's instructions to hijack its behaviour. Prompt injection is a security vulnerability where an attacker crafts input that overrides or bypasses the system-level instructions (system prompt) of an AI model, causing it to behave in unintended ways. This is a critical concern because generative AI models, especially large language models (LLMs), are designed to follow instructions in the prompt, and a malicious user can inject commands that hijack the model's behavior, potentially exposing sensitive data, generating harmful content, or performing unauthorized actions. In Azure AI services, this risk is mitigated through content filtering, input validation, and the use of metaprompt protections.

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When developers inject test prompts to evaluate model performance

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The process of adding new prompts to expand a model's capability

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Accidentally sending the wrong prompt to the model due to a software bug

Question 6

What is 'DALL-E' in Azure OpenAI and what does it do?

Accepted Answer

An image generation model that creates images from natural language text prompts. DALL-E is an image generation model within Azure OpenAI that creates original images from natural language text prompts. It uses a transformer-based architecture trained on image-text pairs to generate visuals that match the semantic content of the input description, making it a core generative AI workload for visual content creation.

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A text summarisation model that condenses long documents

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A data analysis language for querying Azure databases

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A code generation tool optimised for Python development

Question 7

What is 'few-shot prompting' and how does it improve model outputs?

Accepted Answer

Including a small number of input-output examples in the prompt to demonstrate the desired task format. Few-shot prompting improves model outputs by providing a small number of input-output examples directly in the prompt, which helps the model understand the desired task format, style, or reasoning pattern without requiring any fine-tuning or retraining. This technique leverages the model's in-context learning ability to generalize from the given examples and produce more accurate, consistent responses.

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Training a model with very few labelled examples using transfer learning

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Generating a short (few-shot) response rather than a detailed answer

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Running the model for only a few seconds to save compute costs

Question 8

What is 'chain-of-thought prompting' and when is it most effective?

Accepted Answer

Prompting the model to show explicit reasoning steps before giving a final answer. Chain-of-thought prompting instructs the model to break down a complex problem into intermediate reasoning steps before producing the final answer. This technique improves accuracy on tasks requiring multi-step logic, such as arithmetic, commonsense reasoning, or symbolic manipulation, by making the model's internal reasoning explicit and reducing errors from shortcut answers.

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Linking multiple AI models in a pipeline where each model's output feeds the next

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Training a model on a sequence of related documents to build contextual knowledge

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A method for connecting chatbot conversation turns to maintain long-term memory

Question 9

What are 'embeddings' in Azure OpenAI and what are they used for?

Accepted Answer

Numerical vector representations of text that capture semantic meaning for search and similarity tasks. Embeddings in Azure OpenAI are numerical vector representations of text that capture semantic meaning, enabling tasks like semantic search, clustering, and similarity comparisons. They convert words, sentences, or documents into high-dimensional vectors so that similar meanings are represented by vectors close to each other in the vector space. This is correct because embeddings are fundamental to modern AI search and recommendation systems, not related to hardware or web embedding tags.

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Embedded systems software that runs AI models on IoT devices

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HTML embed tags for displaying AI model outputs in web applications

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Compressed versions of large language models that use fewer parameters

Question 10

What is 'Azure AI Studio' and what can you do with it?

Accepted Answer

A unified platform for building, testing, and deploying generative AI applications with access to multiple AI models. Azure AI Studio is a unified platform that enables developers to build, test, and deploy generative AI applications. It provides access to multiple AI models from OpenAI, Meta, and other sources, along with tools for prompt engineering, content safety, and monitoring. This makes it the correct answer because it directly matches the platform's purpose for generative AI workloads.

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A video streaming platform for AI-focused training content and tutorials

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A graphic design tool powered by AI for creating marketing assets

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An IDE plugin that adds AI code completion to Visual Studio Code

Question 11

What is 'Azure AI Content Safety' and what types of harmful content does it detect?

Accepted Answer

A service that detects hate, violence, sexual, and self-harm content in text and images at configurable severity levels. Azure AI Content Safety is a cloud service that detects harmful user-generated and AI-generated content in text and images. It identifies categories such as hate, violence, sexual, and self-harm content, and allows you to configure severity levels (safe, low, medium, high) to filter content appropriately. This makes option B correct because it accurately describes the service's purpose and the specific types of harmful content it detects.

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A firewall that blocks malicious network traffic from reaching Azure AI services

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A GDPR compliance tool that detects and redacts personal data from AI training datasets

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Copyright detection software that identifies AI-generated content derived from copyrighted material

Question 12

What is 'fine-tuning' a language model and when should you use it instead of prompt engineering?

Accepted Answer

Further training a model on domain-specific data to change its behaviour permanently for a task. Fine-tuning is the process of taking a pre-trained language model and further training it on a domain-specific dataset to adapt its behavior permanently for a particular task. This is used instead of prompt engineering when the task requires consistent, specialized outputs that cannot be reliably achieved through prompt instructions alone, such as classifying medical records or generating legal documents.

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Fine-tuning repairs errors in a model's base training data

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Adjusting the model's temperature setting to produce more consistent outputs

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Selecting which pre-trained model from the Azure model catalogue best suits your task

Question 13

What is the 'model catalogue' in Azure AI Foundry/AI Studio?

Accepted Answer

A curated collection of AI models from multiple providers available for deployment in Azure. The model catalogue in Azure AI Foundry (formerly AI Studio) is a curated collection of AI models from multiple providers, including OpenAI, Meta, Hugging Face, and Microsoft, that can be deployed and fine-tuned directly within the Azure environment. It simplifies the process of discovering, comparing, and deploying foundation models for generative AI workloads without requiring manual setup or external registries.

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A product listing of Azure AI hardware accelerators available for purchase

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A directory of all Azure AI customer support contacts organised by model type

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A registry of all models that have passed Microsoft's responsible AI certification

Question 14

What is 'constitutional AI' and how does it relate to responsible AI development?

Accepted Answer

A training approach using a set of ethical principles for the model to self-critique and revise outputs. Constitutional AI is a training approach developed by Anthropic where a language model is fine-tuned using a set of written ethical principles (a 'constitution'). The model learns to self-critique its own outputs against these principles and revise them to be more helpful, harmless, and honest. This directly supports responsible AI development by embedding ethical guardrails into the model's behavior without relying solely on human feedback at every step.

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Legal requirements in government constitutions that regulate AI development

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Ensuring AI models are built on open standards that any organisation can adopt

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A framework requiring AI models to have explicit constitutional rights and protections

Question 15

What is 'Azure OpenAI's Assistants API' and what capabilities does it add?

Accepted Answer

A stateful API enabling AI assistants with persistent threads, tool use, and file handling. The Assistants API is a stateful API that manages persistent threads, supports tool use (e.g., code interpreter, file search), and handles file attachments, enabling multi-turn, context-aware AI assistants. This goes beyond simple completions by maintaining conversation state and integrating external tools, which is a core generative AI workload capability on Azure.

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An API for hiring human assistants to review and approve AI model outputs

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An API for building traditional rule-based chatbots without language model capabilities

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A simplified interface for generating single-turn completions without conversation history