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

Question 1

A developer is using Azure OpenAI Service to generate product descriptions. They want the output to be highly focused and deterministic, with less randomness. Which parameter should they decrease?

Accepted Answer

Temperature. Temperature controls randomness in Azure OpenAI. Lowering the temperature parameter makes the output more focused and deterministic. Max tokens controls length, while presence and frequency penalties influence repetition rather than core randomness.

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Max tokens

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Presence penalty

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Frequency penalty

Question 2

A quality assurance team at a software company uses Azure OpenAI Service to generate compliance reports. They need the model to produce the exact same output for a given prompt every time the API is called, to ensure reproducibility during testing. Which parameter should they set to achieve this deterministic behavior?

Accepted Answer

Set temperature to 0. Setting temperature to 0 forces the model to choose the most likely token at each step, eliminating randomness and producing deterministic outputs for the same prompt. This is essential for reproducibility in testing scenarios where identical results are required across API calls.

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Set frequency penalty to 1

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Set top_p to 1

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Set max_tokens to the expected output length

Question 3

A marketing team uses Azure OpenAI Service to generate taglines for a new advertising campaign. They want the output to be more predictable and less surprising, sticking to the most common phrases and avoiding unusual combinations. Which parameter should they decrease?

Accepted Answer

Temperature. Temperature controls randomness by scaling token probabilities; decreasing it favors high-probability tokens. Top P (nucleus sampling) controls the cumulative probability threshold; decreasing it also restricts outputs to a smaller set of probable tokens. Both make output more conservative and predictable. The original explanation is misleading because reducing Top P also avoids unusual combinations.

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Frequency penalty

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Presence penalty

Question 4

A marketing team wants to use a generative AI model to produce social media posts that match their brand's specific tone and style. They have a small set of example posts written by their copywriters. Which approach should they use to customize the model's outputs without retraining the entire model?

Accepted Answer

Prompt engineering with carefully designed instructions. Prompt engineering, especially few-shot prompting, allows the model to match a desired tone and style by providing example posts in the instruction. It does not involve any training or weight updates, satisfying the 'without retraining' requirement. Fine-tuning (B) requires a large dataset and involves further training, which conflicts with the constraint. Grounding (C) adds context but does not effectively customize style, and content filtering (D) is for safety, not style.

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Fine-tuning the model on the example posts

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Grounding the model with a knowledge base of brand guidelines

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Implementing a content filter to enforce brand rules

Question 5

A developer is using Azure OpenAI to generate code snippets for a banking application. The developer wants to minimize the risk that the generated code contains security vulnerabilities or malicious instructions, even if the prompt is ambiguous. Which Azure OpenAI feature should the developer configure to address this concern?

Accepted Answer

Use a specific system message that requests secure code. A system message guides the model to follow secure coding practices, directly addressing security vulnerabilities. Content filters are a safety layer for harmful content but are not designed to identify code-level vulnerabilities.

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Set the temperature parameter to 0

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Enable content filters

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Set max_tokens to a low value

Question 6

What is grounding in the context of generative AI and Retrieval Augmented Generation (RAG)?

Accepted Answer

Connecting AI responses to verified information from a knowledge base to improve accuracy. Grounding in generative AI and RAG refers to the process of constraining a language model's output to information retrieved from a trusted, external knowledge base (e.g., Azure Cognitive Search, vector databases). This prevents the model from generating hallucinated or outdated content by anchoring its responses to verified facts, which is essential for enterprise applications requiring high accuracy.

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Training an AI model from scratch on domain-specific data

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Removing biases from a trained language model

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Converting text responses into speech output

Question 7

A marketing team wants to use Azure AI to automatically generate unique product descriptions for thousands of items in an e-commerce catalog based on a few keywords provided by the inventory team. Which Azure service should they use?

Accepted Answer

A. Azure OpenAI Service. Azure OpenAI Service provides access to large language models (LLMs) like GPT-4, which are specifically designed for generative tasks such as creating unique, human-like text from a few input keywords. This makes it the ideal choice for automatically generating product descriptions at scale, as it can produce varied and contextually relevant content without requiring pre-labeled training data.

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B. Azure Computer Vision

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C. Language Understanding (LUIS)

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D. Azure Machine Learning

Question 8

A company is developing a chatbot that can both answer customer questions in natural language and create images on demand (e.g., 'Generate a picture of a product prototype'). Which combination of Azure generative AI models should they integrate?

Accepted Answer

A. GPT-4 for text and DALL-E for images. GPT-4 is a generative AI model optimized for natural language understanding and generation, making it ideal for answering customer questions in a conversational manner. DALL-E is a generative AI model specifically designed to create images from textual descriptions, enabling the chatbot to generate product prototypes on demand. Together, they cover both text and image generation requirements.

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B. GPT-3 for text and Custom Vision for images

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C. BERT for text and OCR for images

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D. Language Understanding (LUIS) and Face API

Question 9

A game development company uses Azure OpenAI Service to automatically generate in-game dialog for non-player characters (NPCs) based on character profiles. They need to ensure the generated text does not contain offensive language or harmful suggestions. Which Azure OpenAI Service feature should they configure to prevent this?

Accepted Answer

Content filters. Content filters in Azure OpenAI Service allow you to define categories of harmful content (e.g., hate, violence, self-harm) and set severity thresholds. When generating NPC dialog, the service automatically evaluates each output against these filters and blocks or flags any text that violates the configured policies, ensuring offensive language or harmful suggestions are prevented.

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Model deployment

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Token limit

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Prompt engineering

Question 10

A company uses Azure OpenAI Service to generate marketing copy for social media posts. They want to prevent the model from producing content that contains offensive language, harmful stereotypes, or violent themes that go against their brand guidelines. Which feature should the company configure within Azure OpenAI Service?

Accepted Answer

Configuring the content filtering (responsible AI filters). B is correct because Azure OpenAI Service includes built-in content filtering (responsible AI filters) that automatically detects and blocks offensive language, harmful stereotypes, and violent themes in both input prompts and generated outputs. This feature enforces brand guidelines without requiring custom model modifications or manual oversight.

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Fine-tuning the model with a custom dataset

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Increasing the token limit per response

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Using prompt engineering techniques

Question 11

A company uses Azure OpenAI Service to power a chat-based support assistant. They have extensive knowledge base documents that contain the correct information. The company wants the assistant to answer questions solely based on the provided documents and avoid generating plausible-sounding but incorrect information. Which approach should they implement to minimize the risk of such fabrications?

Accepted Answer

Retrieval Augmented Generation (RAG) — provide relevant document excerpts as context in the prompt. Retrieval Augmented Generation (RAG) is the correct approach because it grounds the model's responses in actual, retrieved document excerpts provided as context in the prompt. This ensures the assistant answers based solely on the supplied knowledge base, directly minimizing the risk of hallucination (plausible-sounding but incorrect information) by constraining the model to the retrieved facts.

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Increase the temperature parameter to 1.0 to force more creative responses

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Fine-tune the model on the knowledge base documents using supervised learning

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Use prompt engineering with a system message that tells the model to never make up facts

Question 12

A marketing team uses Azure OpenAI Service to generate multiple variations of a product description from a single prompt. They want the generated descriptions to be more creative and diverse, rather than repetitive. Which parameter should they increase to achieve this?

Accepted Answer

Temperature. Increasing the Temperature parameter makes the model more creative and diverse by raising the randomness of token selection. At higher temperatures (e.g., 0.8–1.0), the model assigns more weight to less probable tokens, producing varied and unexpected outputs. This directly addresses the need for diverse product descriptions rather than repetitive ones.

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Max tokens

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Top probability

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Frequency penalty

Question 13

A company uses Azure OpenAI Service to power an AI assistant that helps customers with product troubleshooting. The assistant must maintain the conversation history to provide contextually relevant answers across multiple turns. Which API endpoint should be used for this purpose?

Accepted Answer

Chat Completions API. The Chat Completions API is designed for multi-turn conversational scenarios because it accepts a list of messages with roles (system, user, assistant) that represent the conversation history. This allows the model to maintain context across multiple interactions, making it the correct choice for an AI assistant that needs to provide contextually relevant answers over several turns.

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Completions API

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Embeddings API

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Fine-tuning

Question 14

A marketing agency wants to use Azure OpenAI Service to generate product descriptions that consistently match a client's distinctive brand voice. They have a collection of 50 sample descriptions written in the desired tone and style. Which Azure OpenAI Service capability should they use to specialize the model to produce text that closely matches this style?

Accepted Answer

Fine-tuning. Fine-tuning (C) is the correct choice because it allows the marketing agency to train the Azure OpenAI model on their 50 sample descriptions, adjusting the model's weights to specialize its output to match the client's distinctive brand voice. Unlike prompt engineering or parameter adjustments, fine-tuning creates a custom model that internalizes the style and tone from the provided examples, enabling consistent generation without needing lengthy instructions in every prompt.

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Temperature parameter adjustment

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Prompt engineering with detailed instructions

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Content filtering

Question 15

A marketing team uses Azure OpenAI Service to generate headline ideas for a campaign. They find the generated headlines are often too similar and lack creativity. Which parameter should they increase to introduce more randomness in the generated text?

Accepted Answer

Temperature. (Temperature) is correct because temperature controls the randomness of token selection in the model's probability distribution. Increasing temperature (e.g., from 0.7 to 1.0) flattens the probability curve, making lower-probability tokens more likely to be chosen, which introduces more diversity and creativity in the generated headlines.

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Frequency penalty

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Top_p (nucleus sampling)

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Presence penalty