AI-900 Describe features of generative AI workloads on Azure Practice Test 5 — 15 Questions

Question 1

A company wants to use Azure OpenAI to generate personalized marketing emails. They have a large dataset of customer purchase histories. They want the model to generate emails that recommend products based on individual customer preferences without retraining the entire model. Which technique should they use?

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

Accepted Answer

Prompt engineering with few-shot learning. Prompt engineering with few-shot learning is correct because it allows the model to generate personalized marketing emails by providing a few examples of customer-product pairs in the prompt, without modifying the underlying model weights. This technique leverages the pre-trained knowledge of Azure OpenAI to recommend products based on individual customer purchase histories, avoiding the need for costly retraining.

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Fine-tuning

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Reinforcement learning from human feedback

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Creating a custom neural network

Question 2

A marketing agency wants to use Azure OpenAI Service to generate product descriptions. They need the descriptions to be factually accurate and based on their specific product catalog, which is stored in a vector database. Which technique should they use to ground the model's outputs in their own data?

Accepted Answer

Prompt engineering with retrieval augmented generation (RAG). Retrieval augmented generation (RAG) is the correct technique because it allows the model to retrieve relevant, up-to-date product information from the vector database at inference time and use that data as context to generate factually accurate descriptions. This grounds the model's outputs in the specific product catalog without modifying the underlying model weights, ensuring responses are based on the agency's own data.

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Fine-tuning the model on the product catalog

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Zero-shot prompting without additional data

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Reinforcement learning from human feedback (RLHF)

Question 3

A company uses Azure OpenAI Service to generate summaries of long technical documents. They notice that the model sometimes produces summaries that sound plausible but contain factual errors contradicting the source document. Which concept describes this type of error in large language models?

Accepted Answer

Hallucination. Hallucination in large language models refers to the generation of content that is factually incorrect or nonsensical but presented with confidence. In this scenario, the model produces summaries that sound plausible yet contain factual errors contradicting the source document, which is the hallmark of hallucination. This occurs because the model generates text based on probabilistic patterns rather than verifying facts against the input.

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Overfitting

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Tokenization

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Bias

Question 4

A developer wants to use Azure OpenAI to build a customer service chatbot that can answer questions about a company's return policy. They create a set of example question-answer pairs in the prompt without retraining the model. Which technique is being used?

Accepted Answer

Few-shot learning. Few-shot learning is the correct technique because the developer provides a small set of example question-answer pairs directly in the prompt to guide the model's responses, without retraining or updating the model's weights. This leverages the model's pre-existing knowledge to generalize from the examples, which is a hallmark of few-shot prompting in Azure OpenAI.

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Fine-tuning

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Reinforcement learning

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Transfer learning

Question 5

A marketing team uses Azure OpenAI to generate product descriptions. They want the output to reflect their latest catalog and current pricing, not the model's general knowledge. Which technique should they use?

Accepted Answer

Retrieval Augmented Generation (RAG). Retrieval Augmented Generation (RAG) is the correct technique because it allows the model to retrieve up-to-date information from an external knowledge base—such as the latest catalog and current pricing—and incorporate that data into the generated output. Unlike the model's static training data, RAG dynamically injects fresh, domain-specific content at inference time, ensuring accuracy and relevance without modifying the model itself.

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Few-shot learning

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Fine-tuning

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Prompt engineering

Question 6

A company uses Azure OpenAI Service to generate marketing copy for a new product. They have a strict brand voice that requires formal, technical language and explicitly prohibits any humorous or informal phrases. They want to enforce these constraints without retraining the model. Which technique should they use?

Accepted Answer

B) Prompt engineering. Prompt engineering is correct because it allows the user to craft system messages or user prompts that explicitly instruct the model to use formal, technical language and avoid humor, all without modifying the underlying model weights. This technique leverages the model's instruction-following capability to enforce constraints at inference time, making it ideal for brand voice enforcement without retraining.

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A) Fine-tuning

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C) Reinforcement learning

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D) Transfer learning

Question 7

A marketing team wants to generate unique product images by providing detailed textual descriptions. Which Azure OpenAI model should they use?

Accepted Answer

DALL-E. DALL-E is the correct Azure OpenAI model because it is specifically designed to generate images from textual descriptions. It uses a diffusion-based architecture to create high-quality, unique images based on detailed prompts, making it ideal for the marketing team's requirement.

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GPT-4

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Codex

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Whisper

Question 8

A marketing team uses Azure OpenAI Service to generate product descriptions. They have a base description and want the model to produce multiple variations with different tones, such as formal, playful, and technical, while still being factually accurate. Which parameter should they adjust to control the randomness and diversity of the output?

Accepted Answer

temperature. Temperature controls the randomness of the model's output by scaling the logits before applying the softmax function. A higher temperature (e.g., 0.8) increases diversity and creativity, while a lower temperature (e.g., 0.2) makes the output more deterministic and focused. For generating product descriptions with different tones while maintaining factual accuracy, adjusting temperature is the correct approach.

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max_tokens

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top_p

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frequency_penalty

Question 9

A company uses Azure OpenAI Service to automatically generate customer support email responses. They want to ensure that the model does not produce responses containing offensive language, hate speech, or biased content. Which Microsoft responsible AI principle is most directly addressed by implementing content filters that screen the model's output before it is sent?

Accepted Answer

D. Fairness. Implementing content filters to screen model outputs for offensive language, hate speech, or biased content directly addresses the Fairness principle, which requires AI systems to treat all people equitably and avoid reinforcing societal biases. By filtering out harmful or biased content, the organization ensures that the generated responses do not discriminate against or marginalize any group, aligning with Microsoft's commitment to fairness in AI.

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A. Transparency

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B. Reliability and Safety

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C. Inclusiveness

Question 10

A writer uses Azure OpenAI Service to generate multiple story ideas. They find that the model often repeats the same concepts across different outputs. Which parameter should they increase to reduce repetition and encourage more novel content?

Accepted Answer

Frequency penalty. The frequency penalty parameter in Azure OpenAI Service reduces the likelihood of repeating the same tokens or phrases by applying a penalty proportional to the frequency of tokens already generated. Increasing this value discourages the model from reusing common concepts, thereby promoting more novel and diverse story ideas.

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Temperature

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Top_p (nucleus sampling)

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Max tokens

Question 11

A company uses Azure OpenAI to generate marketing copy. They want to ensure that the generated text does not contain inappropriate or harmful content before it is published. Which Azure OpenAI feature is specifically designed for this purpose?

Accepted Answer

Content filters. Content filters are the Azure OpenAI feature specifically designed to detect and block inappropriate or harmful content in generated text. They apply configurable severity levels across categories like hate, violence, self-harm, and sexual content, ensuring outputs meet safety policies before publication.

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Temperature

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Top-p (nucleus sampling)

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System message

Question 12

A company wants to build a chatbot that answers customer questions using only their internal knowledge base, which consists of several PDFs and Word documents. They do not want the chatbot to use any information from the model's pre-trained knowledge. Which Azure OpenAI feature should they use to achieve this?

Accepted Answer

Azure OpenAI on your data. Azure OpenAI on your data allows you to connect Azure OpenAI models to your own data sources (such as PDFs and Word documents) and restrict the model to generate responses solely from that data, without using the model's pre-trained knowledge. This is achieved by indexing the documents into an Azure Cognitive Search index and using retrieval-augmented generation (RAG) to ground the model's responses in your specific content.

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Content filtering

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Prompt flow

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Temperature parameter

Question 13

A company wants to use Azure OpenAI to generate personalized marketing emails. They have a large dataset of customer purchase histories. They want the model to generate emails that recommend products based on individual customer preferences without retraining the entire model. Which technique should they use?

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

Accepted Answer

Use prompt engineering with few-shot examples in the prompt. Prompt engineering with few-shot examples allows the model to mimic the style and structure of provided conversation snippets without retraining. By including a few example conversations in the prompt, the model learns the desired pattern through in-context learning, leveraging its pre-trained capabilities to generate realistic customer conversations.

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Fine-tune the model on the conversation dataset

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Use DALL-E to generate the conversations

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Apply a content filter to restrict the output style

Question 14

A marketing team uses Azure OpenAI to generate social media posts. They want to ensure the generated text maintains a consistent, predictable brand voice without being overly creative or random. Which parameter should they primarily adjust to control the randomness of the output?

Accepted Answer

Temperature. Temperature controls the randomness of token selection by scaling the logits before applying the softmax function. A lower temperature (e.g., 0.2) makes the model more deterministic and conservative, producing outputs that stick closely to the most likely tokens—ideal for maintaining a consistent, predictable brand voice. Higher temperatures increase randomness, which the team wants to avoid.

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Max tokens

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Frequency penalty

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Top P

Question 15

A company wants to use Azure OpenAI to generate personalized marketing emails. They have a large dataset of customer purchase histories. They want the model to generate emails that recommend products based on individual customer preferences without retraining the entire model. Which technique should they use?

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

Accepted Answer

Few-shot prompting with the examples in the prompt. Few-shot prompting provides the model with a small number of example inputs and outputs directly in the prompt, allowing it to infer the desired style and structure without any training. This approach is ideal when you have a few high-quality examples and want to generate new content that matches them, without the cost and complexity of fine-tuning.

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Fine-tune the model on the example descriptions

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Embeddings and similarity search

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Content filtering configurations