AI-900 Describe features of generative AI workloads on Azure — 30 Questions

Question 1

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 2

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. Option A is correct because 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 3

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 4

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 5

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 6

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 7

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 8

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 9

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. Option C (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

Question 10

A game development studio uses Azure OpenAI Service to generate unique backstories for non-player characters (NPCs). They want the generated stories to be coherent and relevant to a given character class (e.g., warrior, mage) but also creative and varied. Which parameter should the studio adjust primarily to increase the creativity and variety of the generated text?

Accepted Answer

Increase the temperature parameter. Increasing the temperature parameter makes the model's output more random by scaling the probability distribution over tokens, which encourages less likely word choices and thus increases creativity and variety in generated text. For the game studio, a higher temperature (e.g., 0.8–1.0) will produce more diverse and imaginative backstories for different character classes, while still maintaining coherence if not set too high.

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Increase the top_p parameter

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Increase the frequency_penalty parameter

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Decrease the max_tokens parameter

Question 11

A marketing team wants to use Azure OpenAI Service to generate product descriptions that consistently match a specific brand voice. They have a small set of example descriptions that demonstrate the desired tone. They want to adapt the model without retraining it from scratch. Which approach should they take?

Accepted Answer

Use prompt engineering with few-shot learning by including the example descriptions in the prompt. Option A is correct because prompt engineering with few-shot learning allows the model to infer the desired brand voice from the example descriptions included directly in the prompt, without requiring retraining. This approach leverages the model's in-context learning capability, where it adapts its output based on the provided examples while keeping the base model unchanged.

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Fine-tune the base model on the example descriptions

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Increase the temperature parameter to the maximum value

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Train a new model using Azure Machine Learning

Question 12

A company uses Azure OpenAI Service to generate long technical reports. To manage costs, the development team needs to accurately estimate the number of tokens that a given prompt will consume before making any API call. Which Azure OpenAI Service feature should they use to obtain this estimate?

Accepted Answer

The Token Counter tool in Azure OpenAI Studio. The Token Counter tool in Azure OpenAI Studio is specifically designed to estimate the number of tokens a prompt will consume before making an API call. This allows developers to predict costs accurately by calculating token usage for both input and expected output, without incurring actual API charges.

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The Chat Completions API

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The Embeddings API

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The Content Filter configuration

Question 13

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 the randomness of the model's output. Lowering the temperature (e.g., from 1.0 to 0.2) makes the model more deterministic by reducing the probability of sampling less likely tokens, resulting in more focused and predictable responses.

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Max tokens

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Top-p

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Frequency penalty

Question 14

A developer is using Azure OpenAI Service to generate Python code snippets. They notice that the generated code often contains repetitive function definitions and loops. Which parameter should be increased to reduce this repetition?

Accepted Answer

Frequency penalty. The frequency penalty parameter reduces repetition by penalizing tokens that have already appeared in the generated text, making the model less likely to reuse the same functions or loops. Increasing this value directly discourages the model from generating repetitive patterns, which is exactly the issue described.

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Temperature

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Max tokens

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Top P

Question 15

A marketing team uses Azure OpenAI Service to generate marketing copy. They notice the generated text is often repetitive, using the same phrases and words multiple times. Which parameter should they increase to directly reduce this repetition?

Accepted Answer

Frequency penalty. Frequency penalty directly reduces repetition by penalizing tokens that have already appeared in the generated text. A higher frequency penalty value (e.g., 0.5 to 1.0) decreases the likelihood of the model reusing the same phrases or words, making the output more diverse and less repetitive.

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Temperature

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Top-p

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Max tokens

Question 16

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 17

A developer uses Azure OpenAI Service to generate creative marketing copy. The API costs are based on the total number of tokens processed (input + output). To minimize costs, the developer wants to ensure that the generated text is as brief as possible while still being effective. Which parameter should the developer adjust in the API request?

Accepted Answer

max_tokens. Option C (max_tokens) is correct because this parameter directly controls the maximum number of tokens the model can generate in a single response. By setting a lower max_tokens value, the developer caps the length of the output, which reduces the total tokens processed (input + output) and thus lowers API costs. Other parameters influence the style or diversity of the output but do not directly limit the length of the generated text.

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temperature

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top_p

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frequency_penalty

Question 18

A developer uses Azure OpenAI Service to generate product descriptions. Each description must be concise and not exceed 50 words. Which parameter should the developer set in the API request to control the output length?

Accepted Answer

max_tokens. The `max_tokens` parameter in the Azure OpenAI API directly controls the maximum number of tokens (words or subwords) in the generated output. By setting `max_tokens` to a value that corresponds to 50 words, the developer ensures the model stops generating once the limit is reached, producing concise descriptions.

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Temperature

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top_p

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frequency_penalty

Question 19

A marketing team uses Azure OpenAI Service to generate social media posts. They want the generated text to be more creative and diverse, with unexpected word choices. Which parameter should they increase?

Accepted Answer

temperature. Increasing the temperature parameter makes the model more creative and diverse by raising the probability of sampling lower-probability tokens, leading to unexpected word choices. Temperature controls the randomness of token selection, with higher values (e.g., 0.9) producing more varied outputs, which aligns with the team's goal of generating creative social media posts.

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frequency_penalty

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presence_penalty

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top_p

Question 20

A marketing team uses Azure OpenAI Service to generate tagline options for a new product. They notice that the model often generates very similar taglines for the same prompt, lacking creativity. To increase the diversity and variety of the output, which parameter should they increase?

Accepted Answer

Temperature. Increasing the temperature parameter makes the model's output more random and diverse by scaling the probability distribution over possible next tokens. A higher temperature (e.g., 0.9) flattens the distribution, giving lower-probability tokens a better chance to be selected, which directly addresses the lack of creativity and variety in the generated taglines.

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Top P

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Frequency penalty

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Max tokens