AI-900 Describe features of generative AI workloads on Azure Practice Test 6 — 15 Questions

Question 1

A developer uses Azure OpenAI Service to generate code. They provide a few examples of function definitions and their corresponding descriptions, then ask the model to write a new function based on a new description. Which technique is the developer using?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Prompt engineering with few-shot learning. The developer is using prompt engineering with few-shot learning, a technique where a small set of input-output examples (here, function definitions and descriptions) is included in the prompt to guide the model's behavior without modifying its weights. This leverages the model's in-context learning ability to generalize from the provided examples and generate a new function for a new description.

Answer

Fine-tuning the model with the examples

Answer

Training a custom model from scratch

Answer

Using reinforcement learning from human feedback

Question 2

A marketing team wants to use Azure OpenAI to generate blog post outlines. They have a single example of an outline that follows their preferred structure: introduction, three key points, conclusion. They want the model to generate new outlines that follow the same structure without retraining the model. Which technique should they use?

Accepted Answer

Providing the example outline in the prompt (few-shot learning). Few-shot learning involves providing a small number of examples (in this case, one example outline) directly in the prompt to guide the model's output format and structure without any retraining. This technique leverages the model's in-context learning ability to mimic the given pattern, making it ideal for generating new outlines that follow the same structure.

Answer

Fine-tuning the model on a large dataset of blog outlines

Answer

Setting the temperature parameter to a high value

Answer

Using the Azure OpenAI embeddings API

Question 3

A customer service company uses Azure OpenAI Service to generate automated replies to customer inquiries. They want each reply to adopt a polite and empathetic tone. Which configuration should they use to guide the model's behavior without retraining?

Accepted Answer

Define a system message that instructs the model to be polite and empathetic.. A system message in Azure OpenAI Service allows you to set the context and tone for the model's responses without retraining. By defining a system message that instructs the model to be polite and empathetic, you guide the model's behavior at inference time, ensuring replies adopt the desired tone.

Answer

Set the temperature parameter to a high value (e.g., 1.0).

Answer

Set the top_p parameter to a low value (e.g., 0.1).

Answer

Set the max_tokens parameter to a specific value (e.g., 150).

Question 4

A developer is using Azure OpenAI Service to generate structured data in JSON format. They want to ensure that every response is valid JSON without adding instructions in every prompt. Which Azure OpenAI feature should they configure?

Accepted Answer

Set the response_format parameter to 'json_object'.. Azure OpenAI Service provides a `response_format` parameter that can be set to `json_object`, which instructs the model to always return valid JSON output. This ensures structured data without requiring the developer to include formatting instructions in every prompt, as the service enforces JSON schema compliance at the API level.

Answer

Set the temperature parameter to a low value (e.g., 0).

Answer

Set the top_p parameter to a high value (e.g., 1).

Answer

Set the max_tokens parameter to a high value (e.g., 2000).

Question 5

A content creator uses Azure OpenAI to generate unique story ideas for a fantasy novel. They want the output to be highly creative and unpredictable, avoiding common clichés. Which parameter should they primarily increase to achieve this?

Accepted Answer

Temperature. Increasing the Temperature parameter makes the model's output more random and less deterministic, which is ideal for generating highly creative and unpredictable story ideas. A higher temperature (e.g., 0.9–1.0) increases the probability of sampling less likely tokens, reducing repetition and clichés.

Answer

Top p

Answer

Frequency penalty

Answer

Presence penalty

Question 6

An advertising agency wants to generate product images from text prompts. They need the ability to specify the visual style (e.g., photorealistic, oil painting) and also ensure that the generated images are safe for work by blocking inappropriate content. Which Azure OpenAI model and feature should they use?

Accepted Answer

DALL-E with built-in content filtering. B is correct because DALL-E is the Azure OpenAI model specifically designed for generating images from text prompts, and it includes built-in content filtering to block inappropriate or unsafe content. This combination directly meets the agency's need to specify visual styles (e.g., photorealistic, oil painting) via prompt engineering while ensuring safety compliance without additional configuration.

Answer

GPT-4 with standard content filtering

Answer

GPT-3.5 with custom moderation

Answer

Codex with output validation

Question 7

A company uses Azure OpenAI Service to generate marketing copy. They want to ensure that the generated text does not contain offensive language or harmful stereotypes, even if the prompt inadvertently leads the model in that direction. Which Azure OpenAI feature should they configure to help prevent such outputs?

Accepted Answer

Content filtering. Content filtering in Azure OpenAI Service uses a set of pre-built, configurable filters to detect and block harmful content categories such as hate, violence, sexual, and self-harm. This feature operates at the service level, intercepting both prompts and completions to prevent offensive language or harmful stereotypes from being generated, regardless of how the prompt is phrased.

Answer

Prompt engineering

Answer

Fine-tuning

Answer

Few-shot learning

Question 8

A developer uses Azure OpenAI to generate product descriptions. They provide five examples of product descriptions that follow a specific format (name, features, price, call to action). They then ask the model to write a new description for a given product, expecting the same format. Which technique is the developer using?

Accepted Answer

Few-shot learning. The developer is using few-shot learning, which involves providing a small number of examples (in this case, five product descriptions) to guide the model's output format and style without updating the model's weights. This technique leverages the model's in-context learning ability to follow the demonstrated pattern for a new input.

Answer

Fine-tuning

Answer

Zero-shot learning

Answer

Reinforcement learning

Question 9

A creative agency wants to use Azure OpenAI to generate marketing images from text descriptions. They need to ensure that the generated images are appropriate for all audiences by automatically blocking sexually explicit or violent content. Which Azure OpenAI feature should they configure to meet this requirement?

Accepted Answer

Enable content filtering on the DALL-E deployment. Azure OpenAI's DALL-E deployment includes built-in content filtering that automatically blocks sexually explicit, violent, or otherwise inappropriate images from being generated. This feature is configured at the deployment level and requires no custom model training, making it the simplest and most effective way to meet the requirement for all-audience appropriateness.

Answer

Use the GPT-4 model with safety prompts

Answer

Train a custom image classification model to filter outputs

Answer

Use the Embeddings model to detect inappropriate content

Question 10

A legal firm wants to use Azure OpenAI to generate summaries of lengthy contracts. The firm requires that the generated summaries are strictly based on the provided contract text and do not include any external knowledge or hallucinated facts. Which Azure OpenAI feature should the firm configure to meet this requirement?

Accepted Answer

Azure OpenAI on Your Data (data grounding). Azure OpenAI on Your Data (data grounding) restricts the model's responses to the content of the provided contract documents, preventing the generation of information not present in the source text. This feature uses a retrieval-augmented generation (RAG) approach, where the model only references the indexed contract data, effectively eliminating external knowledge or hallucinated facts.

Answer

Content filtering

Answer

Prompt engineering with system messages

Answer

Fine-tuning the model on legal texts

Question 11

A marketing team uses Azure OpenAI Service to generate product descriptions. They want the descriptions to follow a specific brand voice (formal, concise) and avoid generating any harmful or offensive language. Which combination of features should the team use?

Accepted Answer

A: Fine-tune the model with brand-specific data and enable content filtering.. Fine-tuning the model with brand-specific data allows the model to learn the desired brand voice (formal, concise) by adjusting its weights based on a curated dataset. Enabling content filtering ensures that any harmful or offensive language is blocked, either by Azure's built-in content moderation or by custom filters, meeting the safety requirement. This combination directly addresses both the style and safety needs.

Answer

B: Use few-shot learning with examples and disable content filtering for creativity.

Answer

C: Increase the temperature parameter and use the logprobs parameter.

Answer

D: Use the top_p parameter and set max_tokens to a low value.

Question 12

A developer wants to use Azure OpenAI to generate text that follows a specific style, such as formal business letters. They provide three examples of the desired output format in the prompt and then ask the model to generate a new letter. Which technique is the developer using?

Accepted Answer

Few-shot learning. The developer is using few-shot learning, a technique where a prompt includes several examples (in this case, three formal business letters) to guide the model's output style and format without updating the model's weights. This approach leverages the model's in-context learning ability to generalize from the provided examples, making it ideal for tasks requiring specific stylistic adherence.

Answer

Zero-shot learning

Answer

Fine-tuning

Answer

Temperature scaling

Question 13

A marketing team wants to use Azure OpenAI to generate blog posts. They require the output to avoid toxic language and adhere to their brand safety guidelines. Which Azure OpenAI feature should they configure to automatically block harmful content?

Accepted Answer

Content filters. Content filters in Azure OpenAI are designed to automatically detect and block harmful content, including toxic language, hate speech, and violence, based on configurable severity levels. This feature directly addresses the marketing team's requirement to enforce brand safety guidelines by filtering out undesirable outputs before they are returned to the user.

Answer

Grounding

Answer

Temperature

Answer

Few-shot learning

Question 14

A creative agency wants to use Azure OpenAI to generate unique images for social media campaigns based on text descriptions. Which Azure OpenAI model should they use for this purpose?

Accepted Answer

DALL-E 3. DALL-E 3 is the correct choice because it is the Azure OpenAI model specifically designed for generating images from natural language text descriptions. It uses a diffusion-based architecture to create high-quality, unique visuals that align with the provided prompts, making it ideal for creative social media campaigns.

Answer

GPT-4

Answer

Codex

Answer

Whisper

Question 15

A software company uses Azure OpenAI to generate code snippets. They want to evaluate how confident the model is in each token it generates. Which Azure OpenAI feature provides a numerical measure of confidence for each generated token?

Accepted Answer

Logprobs. Logprobs (log probabilities) is the Azure OpenAI feature that provides a numerical measure of confidence for each generated token. It outputs the log probability of each token being selected by the model, allowing developers to assess how certain the model is about its predictions at the token level.

Answer

Temperature

Answer

Top-p

Answer

Presence penalty