AI-900 Describe features of generative AI workloads on Azure Practice Test 4 — 15 Questions

Question 1

A developer uses Azure OpenAI Service to generate conversation scripts for a chatbot. The developer wants to encourage the model to introduce new topics and avoid repeatedly discussing the same subject matter. Which parameter should the developer increase?

Accepted Answer

Presence penalty. The Presence penalty parameter penalizes tokens that have already appeared in the conversation, encouraging the model to introduce new topics and avoid repetition. By increasing this value, the developer reduces the likelihood of the model reusing the same subject matter, which is exactly the requirement described.

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Temperature

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Top_p (nucleus sampling)

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Frequency penalty

Question 2

A marketing team wants to use AI to automatically create new product descriptions that are original and varied, simulating human-like writing. Which type of AI model is best suited for this task?

Accepted Answer

Generative model. Option B is correct because generative AI models, such as GPT (Generative Pre-trained Transformer), are specifically designed to create new, original content by learning the underlying patterns and distributions of training data. For the task of generating varied and human-like product descriptions, a generative model can produce novel text that mimics the style and structure of the training examples, unlike discriminative models which only classify or predict labels.

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Discriminative model

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Regression model

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Clustering model

Question 3

A fashion retailer wants to automatically generate new, unique images of clothing items based on textual descriptions (e.g., 'a blue silk dress with floral patterns'). Which Azure service would be most appropriate to accomplish this?

Accepted Answer

B) Azure OpenAI Service. Azure OpenAI Service provides access to powerful generative AI models like GPT-4 and DALL-E, which can create new images from textual descriptions. This service is specifically designed for generative tasks, such as producing unique clothing images based on prompts like 'a blue silk dress with floral patterns', making it the most appropriate choice.

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A) Azure Machine Learning

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C) Azure Cognitive Search

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D) Custom Vision

Question 4

A company uses a generative AI model to answer customer questions about their products. They observe that the model sometimes produces factually incorrect or fabricated information. To reduce these inaccuracies, they want to provide the model with relevant, up-to-date product documentation as context before generating a response. Which technique is being applied?

Accepted Answer

Grounding. B is correct because grounding is the technique of providing a generative AI model with specific, authoritative source data (such as product documentation) as context before generating a response. This anchors the model's output to verified facts, directly reducing hallucinations and fabricated information by constraining the generation to the provided context.

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Prompt Engineering

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Fine-tuning

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Reinforcement Learning from Human Feedback (RLHF)

Question 5

A company uses a GPT-based model to generate marketing copy. They notice the model occasionally produces text that includes harmful stereotypes. They want to reduce these harmful outputs without retraining the model. Which approach is most appropriate?

Accepted Answer

Prompt engineering with specific instructions to avoid stereotypes. Option B is correct because prompt engineering allows you to guide the model's behavior at inference time without modifying its weights. By including explicit instructions in the prompt (e.g., 'Avoid harmful stereotypes'), you can steer the output toward safer content. This is the most appropriate approach when retraining is not an option, as it directly addresses the undesired outputs through input design.

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Fine-tuning the model on a curated dataset

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Reducing the temperature parameter to zero

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Increasing the maximum output length

Question 6

A company uses a large language model to generate answers to employee questions about internal HR policies. However, the model sometimes produces answers that are factually incorrect or not based on the official policies. To reduce these inaccuracies, the company wants to provide the model with relevant, up-to-date policy documents as extra context before generating a response. Which technique is being applied?

Accepted Answer

Grounding with relevant data (RAG). The technique described is Retrieval-Augmented Generation (RAG), which retrieves relevant, up-to-date policy documents from an external knowledge base and provides them as context to the large language model before generating a response. This grounds the model's output in verified data, reducing factual inaccuracies without modifying the model itself. Option C is correct because RAG directly addresses the need to supply extra context from authoritative sources.

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Prompt engineering only

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Fine-tuning the model on policy documents

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Using a content filter

Question 7

A company uses a generative AI model to create blog posts. They want to ensure that the model's output never contains offensive or harmful language before the content is published. They implement a system that checks the generated text against a list of prohibited terms and blocks or edits the content if necessary. Which type of safety measure is this?

Accepted Answer

Post-processing content filtering. Option C is correct because the described system operates after the model generates text, scanning the output against a prohibited terms list and blocking or editing it. This is a classic post-processing content filtering approach, distinct from modifying the model's training data, prompts, or weights. Azure AI Content Safety is an example of such a post-processing filter that can be applied to generative AI outputs.

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Pre-training data cleaning

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Prompt engineering with safety instructions

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Model fine-tuning on safe examples

Question 8

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

Fine-tuning the model on the example posts. Fine-tuning adapts a pre-trained model to a specific task or style by training it further on a smaller, targeted dataset. In this scenario, the team has a few example posts; fine-tuning a base model (like GPT-4) on these examples will teach the model the desired tone and style. Prompt engineering (A) involves crafting input prompts but does not update the model weights and may be less effective for deep style changes. Grounding (C) provides additional context during inference but does not change the model's core behavior. Content filtering (D) is a safety measure that blocks or edits harmful outputs, not a customization method.

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Prompt engineering with carefully designed instructions

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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 9

A digital marketing agency wants to use an AI model that can create original images of products in different styles based on text prompts, such as 'a luxury watch in a futuristic setting.' Which Azure service should they choose?

Accepted Answer

Azure OpenAI Service. Azure OpenAI Service provides access to generative AI models like DALL-E, which can create original images from text prompts. This service is specifically designed for tasks such as generating product images in different styles based on descriptive text, making it the correct choice for the agency's requirement.

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Azure AI Language

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Azure Cognitive Search

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Azure Computer Vision

Question 10

A company wants to build a chatbot that can engage in free-form conversations with customers, answering questions and providing information without being limited to a fixed set of responses. Which type of AI model is most suitable?

Accepted Answer

Generative language model. A generative language model is the most suitable for building a chatbot that engages in free-form conversations because it can generate novel, contextually relevant responses based on the input it receives, rather than selecting from a fixed set of predefined answers. This capability is essential for handling the open-ended nature of customer queries, where the chatbot must produce coherent and varied responses dynamically.

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Classification model

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Regression model

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Object detection model

Question 11

A company wants to build a chatbot that answers customer questions using a large language model. The company has an extensive internal knowledge base with accurate, up-to-date product information. To ensure the chatbot's answers are based on this reliable source rather than the model's internal knowledge, which technique should they use?

Accepted Answer

Grounding with retrieval-augmented generation. Option C is correct because grounding with retrieval-augmented generation (RAG) retrieves relevant, up-to-date chunks from the internal knowledge base and provides them as context to the large language model (LLM) at inference time. This ensures the chatbot's answers are factually based on the company's reliable source rather than relying on the model's potentially outdated or incorrect parametric memory.

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Fine-tuning the model on the knowledge base

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Zero-shot learning

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Prompt engineering with few-shot examples

Question 12

A museum wants to create an interactive exhibit where visitors can type a description of a fictional creature, such as 'a fire-breathing dragon with emerald scales and golden wings,' and the system generates an image of that creature in real time. The museum must ensure that the generated images are safe and appropriate for all ages, including children. Which Azure service should they use, and which safety feature should they configure?

Accepted Answer

Azure OpenAI Service with the DALL-E 2 model and content filtering enabled. Option A is correct because Azure OpenAI Service with DALL-E 2 is specifically designed for generating images from text descriptions, and enabling content filtering ensures the output is safe for all ages, including children. This combination directly meets the museum's requirement for real-time, safe image generation from textual prompts.

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Azure Cognitive Services Computer Vision with custom vision image generation

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Azure OpenAI Service with the GPT-4 model and content filtering enabled

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Azure OpenAI Service with the DALL-E 2 model without content filtering

Question 13

A company wants to use Azure OpenAI Service to generate product descriptions. They need to ensure the model's output is based on their specific product catalog and pricing, not on generic information. Which approach should they use?

Accepted Answer

Implementing Retrieval Augmented Generation (RAG) with their catalog.. Option C is correct because Retrieval Augmented Generation (RAG) allows the model to dynamically retrieve relevant product catalog and pricing information from an external knowledge base at inference time, ensuring the generated descriptions are grounded in the company's specific data rather than relying on the model's generic training data. This approach avoids the need for costly fine-tuning and keeps the output up-to-date without retraining.

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Fine-tuning the model on their product catalog.

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Using few-shot learning with examples.

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Increasing the temperature parameter.

Question 14

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?

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 15

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)