AI-900 Exam Dumps - Microsoft Certified: Azure AI Fundamentals Questions and Answers

<  Smart home device answering questions → Yes

 Webchat using Azure Bot Service → Yes

 Auto-caption generation for videos → No

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Describe features of common AI workloads”, Conversational AI refers to systems designed to engage in human-like dialogue through natural language—either text or speech. These systems include chatbots, virtual assistants, and voice-enabled smart devices, all of which can interpret user intent and respond meaningfully.

A smart device in the home that responds to questions such as “When is my next appointment?” – Yes.This is a classic example of Conversational AI. Devices like smart speakers or personal digital assistants use speech recognition, natural language understanding (NLU), and language generation to interpret spoken input and respond conversationally. The AI-900 study materials identify these as examples of voice-based conversational AI systems, which fall under the Speech and Language AI workloads.

An interactive webchat feature on a company website can be implemented by using Azure Bot Service – Yes.This statement is true. The Azure Bot Service is the primary Microsoft tool for creating and deploying conversational bots across various channels, including websites, Microsoft Teams, and other messaging platforms. The AI-900 syllabus specifically cites this as an example of implementing conversational AI for customer support or information retrieval.

Automatically generating captions for pre-recorded videos is an example of conversational AI – No.This is not conversational AI; instead, it falls under the Speech AI workload, specifically speech-to-text transcription. Automatically generating captions involves converting audio from video into written text but does not involve dialogue or interaction between a user and a system.

Thus, based on the official AI-900 guidance, only the first two scenarios describe conversational AI use cases.

The Azure AI Language service provides several NLP features, including language detection, key phrase extraction, sentiment analysis, and named entity recognition (NER).

When you need to extract specific data points such as dates, names, organizations, or locations from unstructured text (for example, social media posts), the correct feature is Entity Recognition.

Entity Recognition identifies and classifies information in text into predefined categories like:

Person names (e.g., “John Smith”)

Organizations (e.g., “Contoso Ltd.”)

Dates and times (e.g., “October 22, 2025”)

Locations, events, and quantities

This capability helps transform unstructured textual data into structured data that can be analyzed or stored.

Option analysis:

A (Language detection): Determines the language of a text (e.g., English, French).

B (Speech recognition): Converts spoken audio to text; not applicable here.

C (Key phrase extraction): Identifies important phrases or topics but not specific entities like names or dates.

D (Entity recognition): Correctly extracts names, dates, and other specific data from text.

Hence, the accurate feature for this scenario is D. Entity Recognition.

To improve the quality and relevance of responses generated by a generative AI solution using GPT-3.5, the following three actions are emphasized in the Microsoft Learn: Azure OpenAI Service best practices and AI-900/AI-102 training materials:

(A) Add grounding data to prompts: Grounding ensures that the model’s output is based on factual, domain-specific information. By adding context or external knowledge sources, responses become more accurate and aligned with the organization’s data rather than relying on the model’s general training corpus.

(B) Provide additional examples to prompts: Also known as few-shot prompting, this method demonstrates desired response patterns by including examples in the prompt. This significantly improves output quality, consistency, and adherence to desired formats.

(E) Modify system messages: In Azure OpenAI chat completions, system messages define the model’s behavior, style, and tone. Adjusting system messages allows fine-tuning of the model’s response quality, ensuring it follows context or persona guidelines.

The remaining options are incorrect:

(C) Modify tokenization is a low-level text-processing technique not used to improve model response quality directly.

(D) Add training data to prompts is not possible at runtime since GPT-3.5 models are pre-trained; only prompt engineering can influence output behavior.

Therefore, based on Azure OpenAI and AI-900 guidance, the three best ways to enhance generative

The Azure OpenAI DALL-E model is specifically designed for generating original images from natural language prompts. If you want to create an image of a “rural scene,” you can use Azure AI Foundry (formerly Azure OpenAI Studio) to deploy the DALL-E model and provide descriptive instructions such as “create an image of a peaceful rural village with trees and a sunset.”

A. GPT-3.5 Turbo → Handles text generation, not image creation.

B. Bing search → Finds existing images, not generate original ones.

C. GitHub Copilot → Assists with writing code, not generating images.

validation.

In the Microsoft Azure AI Fundamentals (AI-900) study materials, a key concept in machine learning model development is splitting data into subsets for training, validation, and testing. A randomly extracted subset of data from a dataset is most commonly used for validation — that is, for evaluating the performance of the model during or after training.

Here’s how this process works:

Training set – This portion of the dataset is used to train the machine learning model. The model learns patterns, relationships, and parameters from this data.

Validation set – This is a randomly selected subset (separate from training data) used to fine-tune model hyperparameters and evaluate how well the model generalizes to unseen data. It helps detect overfitting — when the model performs well on training data but poorly on new data.

Test set – A final, untouched dataset used to measure the model’s real-world performance after all training and tuning are complete.

By reserving a random subset for validation, data scientists ensure that the model’s performance metrics reflect generalization, not memorization of the training data.

Let’s review the incorrect options:

Algorithms – These are the mathematical frameworks or methods used to build models (e.g., decision trees, neural networks). They are not data subsets.

Features – These are input variables (attributes) used by the model, not randomly selected data subsets.

Labels – These are target values or outcomes the model predicts; again, not data subsets.

Therefore, in alignment with Azure AI-900’s machine learning fundamentals, the correct completion is:

✅ “A randomly extracted subset of data from a dataset is commonly used for validation of the model.”

This question refers to a system that monitors a user’s emotions or expressions—in this case, identifying whether a kiosk user is annoyed—through a video feed. According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Identify Azure services for computer vision,” this scenario falls under facial analysis, which is a capability of Azure AI Vision or the Face API.

Facial analysis involves detecting human faces in images or video and analyzing facial features to interpret emotions, expressions, age, gender, or facial landmarks. The AI model does not try to identify who the person is but rather interprets how they appear or feel. For example, facial analysis can detect emotions such as happiness, anger, sadness, or surprise, which allows applications to infer a user’s engagement or frustration level while interacting with a system.

Option review:

Face detection: Identifies the presence and location of a face in an image but does not interpret expressions or emotions.

Facial recognition: Matches a detected face to a known individual’s identity (for authentication or security), not for emotion detection.

Optical character recognition (OCR): Extracts text from images or scanned documents and has no relation to human emotion or facial features.

Therefore, determining whether a kiosk user is annoyed, happy, or frustrated involves emotion detection within facial analysis, making Facial analysis the correct answer.

This aligns with AI-900’s definition of computer vision workloads, where facial analysis provides insights into emotions and expressions, supporting user experience optimization and customer behavior analytics.

Confidence.

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore computer vision in Microsoft Azure,” the confidence score represents the calculated probability that a model’s prediction is correct. In image classification, when an AI model analyzes an image and assigns it to a specific category, it also produces a confidence value—a numerical probability (usually between 0 and 1) indicating how certain the model is about its prediction.

For example, if an image classification model identifies an image as a “cat” with a confidence of 0.92, it means the model is 92% certain that the image depicts a cat. The confidence value helps developers and users understand the model’s certainty level about its classification output.

Microsoft Learn emphasizes that in Azure Cognitive Services—such as the Custom Vision Service—each prediction result includes both the predicted label (class) and a confidence score. These confidence scores are essential for evaluating model performance and determining thresholds for automated decisions (e.g., accepting predictions only above a 0.8 probability).

Let’s evaluate the other options:

Accuracy: This is an overall performance metric measuring the percentage of correct predictions across the dataset, not a probability for a single prediction.

Root Mean Square Error (RMSE): This is a metric for regression models, not classification tasks. It measures average error magnitude between predicted and actual values.

Sentiment: This is a type of prediction (positive, negative, neutral) in text analysis, not a probability metric.

Therefore, based on Microsoft’s AI-900 training materials and Azure Cognitive Services documentation, the calculated probability of a correct image classification is called Confidence, which expresses how sure the model is about its prediction for a specific input.

According to the Microsoft Azure AI Fundamentals (AI-900) study material and Azure OpenAI Service documentation, large language models (LLMs) such as GPT are capable of performing multiple natural language processing (NLP) tasks depending on the intent of the prompt. These tasks generally fall into categories like classification, generation, summarization, and translation, each with a distinct purpose and output type.

Classifying – This process involves analyzing text and assigning it to a predefined category or label based on its content. The scenario “Detect the genre of a work of fiction” clearly fits this category. The model must evaluate the text and determine whether it belongs to genres like mystery, romance, or science fiction. This is a classic text classification problem, as the output is a discrete category derived from textual features.

Summarizing – This process means condensing lengthy text into a shorter version that preserves the key information. In the scenario “Create a list of bullet points based on text input,” the model extracts essential information and reformats it as concise bullet points, which is an abstraction form of summarization. Summarization models help users quickly understand the main ideas from long documents, meeting efficiency and readability goals.

Generating – This refers to the LLM’s ability to produce new, creative content based on input instructions. The task “Create advertising slogans from a product description” represents generation because it requires the model to construct original text that didn’t previously exist. Generation tasks showcase the creativity and contextual fluency of models like GPT in marketing and content creation.

Thus, these mappings align directly with the Azure OpenAI LLM capabilities taught in AI-900, linking each NLP process with its most suitable real-world task.

Object detection is similar to tagging, but the API returns the bounding box coordinates for each tag applied. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Describe features of natural language processing (NLP) workloads on Azure,” sentiment analysis is an NLP workload that determines the emotional tone or opinion expressed in a piece of text. This could be positive, negative, or neutral sentiment.

When an app analyzes social media posts to identify their tone, it is performing sentiment analysis, since it aims to understand the emotional context behind user-generated text such as tweets, reviews, or comments. Azure provides this functionality through the Azure Cognitive Services – Text Analytics API, which evaluates text and returns sentiment scores.

Other options are not suitable:

Key phrase extraction identifies main ideas in text but not tone.

Entity recognition identifies names of people, organizations, or locations.

Speech recognition converts spoken words into text, not emotional analysis.

Therefore, analyzing social media tone is an example of sentiment analysis, a key NLP workload in Microsoft’s AI-900 syllabus.