AI-900 Exam Dumps - Microsoft Azure AI Fundamentals

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Describe features of conversational AI workloads,” the correct answer is Power Virtual Agents. This service is part of the Microsoft Power Platform and is specifically designed to enable users to create intelligent chatbots without writing any code.

Power Virtual Agents (PVA) provides a no-code/low-code environment where business users and developers can collaboratively design conversational experiences. It integrates built-in natural language processing (NLP) models to understand user intent and respond intelligently to text or speech inputs. The platform’s interface allows chatbot creators to design dialogues visually, connect to back-end data via Power Automate, and publish bots on websites, Teams, or other communication channels.

This approach is highlighted in Microsoft Learn as an ideal solution for organizations that want to deploy conversational bots quickly without requiring specialized AI or programming expertise. PVA automatically leverages Microsoft’s language understanding models, allowing it to interpret user input and map it to predefined topics or actions.

Let’s analyze the other options:

Azure Health Bot: A specialized solution for the healthcare industry that provides prebuilt medical compliance and healthcare content. It is not a general-purpose, no-code chatbot builder.

Microsoft Bot Framework: A developer-focused SDK for building highly customized bots through code, offering maximum flexibility but not no-code functionality.

Therefore, the most appropriate choice that “can be used to build no-code apps that use built-in natural language processing models” is Power Virtual Agents — the official Microsoft no-code chatbot solution for conversational AI workloads.

Full Detailed Explanation (250–300 words):

“You can fine-tune some Azure OpenAI models by using your own data.” – YESThis statement is true. Azure OpenAI allows customers to fine-tune certain models like GPT-3, GPT-3.5, and some embedding models with their own data. Fine-tuning customizes a model to perform better on specific tasks or match a company’s domain terminology, tone, or context. According to Microsoft Learn’s AI-900 and Azure OpenAI documentation, fine-tuning is supported for approved use cases while maintaining Microsoft’s Responsible AI oversight and compliance process.

“Pretrained generative AI models are a component of Azure OpenAI.” – YESThis statement is also true. Azure OpenAI provides access to pretrained large language and generative AI models such as GPT-3.5, GPT-4, Codex, and DALL·E. These models are pretrained on vast datasets and made available via APIs, allowing developers to generate text, code, and images without needing to train their own models. This is a core feature of Azure OpenAI’s service offering.

“To build a solution that complies with Microsoft responsible AI principles, you must build and train your own model.” – NOThis statement is false. Compliance with Microsoft Responsible AI principles (Fairness, Reliability & Safety, Privacy & Security, Inclusiveness, Transparency, Accountability) does not require building custom models. Prebuilt Azure AI and OpenAI services already align with Responsible AI standards. Developers simply need to use these services responsibly, applying governance and ethical design practices.

In Azure Machine Learning designer, a low-code drag-and-drop interface, users can visually build machine learning workflows. According to the AI-900 study guide and Microsoft Learn module “Create and publish models with Azure Machine Learning designer”, two key components that can be dragged onto the designer canvas are datasets and modules.

Datasets (A): These are collections of data that serve as the input for training or evaluating models. They can be registered in the workspace and then dragged onto the canvas for use in transformations or model training.

Modules (D): These are prebuilt processing and modeling components that perform operations such as data cleaning, feature engineering, model training, and evaluation. Examples include “Split Data,” “Train Model,” and “Evaluate Model.”

Compute (B) and Pipeline (C) are not drag-and-drop items within the designer. Compute targets are infrastructure resources used to run the pipeline, while a pipeline represents the overall workflow, not a component that can be added like a dataset or module.

Hence, the correct answers are A. Dataset and D. Module.

Conversational AI workloads involve human-like dialogue with AI systems.

A: A smart assistant (e.g., smart speaker) uses voice-based conversational AI.

B: A knowledge-based chatbot interacts with users via natural language.Options C and D describe automation/IoT workloads, not conversational AI.

✅ Final Answer (Q110): A and B

 You can communicate with a bot by using email → No

 You can communicate with a bot by using Microsoft Teams → Yes

 You can communicate with a bot by using a webchat interface → Yes

These answers are based on the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore conversational AI in Microsoft Azure.”

The Azure Bot Service allows developers to build, test, deploy, and manage intelligent chatbots that can interact with users through various channels. Channels are communication platforms or interfaces that connect users to bots. Once a bot is built and published through the Azure Bot Service, it can be connected to multiple channels such as Microsoft Teams, webchat, Skype, Facebook Messenger, Direct Line, Slack, and others.

Let’s evaluate each statement:

You can communicate with a bot by using email → NoAzure Bot Service does not support direct interaction via email as a channel. Bots are designed for real-time or conversational interactions through messaging or voice-based platforms, not asynchronous email communication.

You can communicate with a bot by using Microsoft Teams → YesMicrosoft Teams is one of the primary channels supported by Azure Bot Service. Bots can be integrated directly into Teams to handle chat-based conversations, provide information, automate workflows, or assist users interactively within Teams.

You can communicate with a bot by using a webchat interface → YesThe Web Chat channel is another core feature of Azure Bot Service. It allows embedding the bot into a website or web application using the Web Chat control or the Direct Line API, enabling users to chat directly from a browser interface.

In summary, Azure Bot Service supports real-time conversational interfaces like Teams and webchat, but not email.

The correct answer is B. Optical Character Recognition (OCR).

Optical Character Recognition (OCR) is a feature of Azure AI Vision that converts printed or handwritten text within images into machine-readable text. In this scenario, the goal is to read runner numbers on shirts from race photos. OCR can identify and extract these numbers, allowing them to be associated with specific participants.

Option analysis:

A. Image classification: Categorizes entire images (e.g., “runner,” “crowd”), not text.

B. Optical Character Recognition (OCR) — ✅ Correct. Extracts alphanumeric text from images.

C. Object detection: Identifies and locates objects (e.g., shoes, cars) but doesn’t read text.

D. Facial recognition: Identifies individuals by matching facial features to known identities, not by reading numbers.

Therefore, to read and extract runner numbers from photos, the correct computer vision technique is Optical Character Recognition (OCR).

According to the AI-900 study guide and Microsoft Learn module “Identify features of natural language processing workloads”, Natural Language Processing (NLP) enables computers to understand, interpret, and generate human language. Tasks such as extracting key terms, summarizing documents, identifying topics, or determining sentiment fall under NLP workloads.

In this question, you have insurance claim reports stored as text, and you need to extract key terms to generate summaries. This matches the Text Analytics service in Azure Cognitive Services, which uses NLP techniques such as key phrase extraction to identify important concepts within textual data.

The other options are incorrect because:

A. Conversational AI focuses on chatbots or dialogue systems.

B. Anomaly detection identifies unusual data patterns, not textual meaning.

D. Computer vision processes image or video content, not text.

Therefore, extracting key terms from documents is a clear example of Natural Language Processing.

In a chat solution using the Azure OpenAI GPT-3.5 model, the temperature parameter controls the creativity and variability of generated responses. According to the Microsoft Learn documentation for Azure OpenAI Service, temperature is a float value typically between 0 and 2, determining how deterministic or random the model’s output is. A lower temperature (e.g., 0–0.3) makes responses more focused and deterministic, while a higher temperature (e.g., 0.8–1.2) produces more verbose, creative, and diverse responses.

When you want the chat model to generate more detailed or expressive output, increasing the temperature encourages the model to explore a broader range of possible continuations, leading to longer and more varied text. This parameter directly affects how “verbose” or elaborate the model’s responses can be, which is why it is the correct answer.

The other options are not appropriate for this scenario:

A. Presence penalty reduces repetition by discouraging reuse of the same phrases but does not control verbosity.

C. Stop sequence defines tokens where generation should stop, limiting rather than extending response length.

D. Max response (max tokens) controls the maximum length of the response but does not inherently make answers more verbose or expressive.

Thus, to encourage more elaborate and detailed output from the Azure OpenAI GPT-3.5 model, the correct configuration parameter to adjust is Temperature (B).