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

What is a Predicted vs. True chart?

Predicted vs. True shows the relationship between a predicted value and its correlating true value for a regression problem. This graph can be used to measure performance of a model as the closer to the y=x line the predicted values are, the better the accuracy of a predictive model.

rrect answer is C. Predicting the sale price of a house based on historical data, the size of the house, and the number of bedrooms.

In machine learning, regression is a supervised learning technique used to predict continuous numeric values. Microsoft’s AI-900 study guide defines regression models as those that estimate relationships between variables—predicting a continuous outcome variable from one or more input features.

In this case, the house sale price is a continuous numeric value, and inputs such as size, location, and number of bedrooms are the features. Common regression algorithms include linear regression, decision tree regression, and boosted regression.

Other options represent different ML workloads:

A involves segmentation by categories (classification or clustering).

B represents clustering, grouping similar items without predefined labels.

D represents computer vision, counting animals in images rather than predicting a numeric value.

Hence, the verified answer is C. Regression.

The correct answer is A. Configure the Content filters settings.

When using the Azure OpenAI Service for text or image generation, Microsoft provides built-in content filtering to help detect and block potentially harmful or unsafe outputs. These filters are part of Microsoft’s Responsible AI framework and are designed to prevent the generation of offensive, violent, sexual, or otherwise restricted content.

To ensure the highest level of protection, you can configure content filter settings within the Azure OpenAI deployment. This allows you to define stricter policies based on your organization’s safety requirements. For image generation models such as DALL·E, enabling or strengthening these filters ensures that inappropriate or unsafe images are not generated or returned.

B (Customize an LLM): Customization affects behavior but not safety filtering.

C (Configure the system prompt): Adjusts response style but doesn’t guarantee content safety.

D (Change the model): Different models have similar filter systems; protection level depends on filter configuration, not the model itself.

In Azure OpenAI Service, fine-tuning refers to the process of uploading your own labeled dataset to customize or adapt a pretrained model (such as GPT-3.5 or Curie) for a specific use case. According to the Microsoft Learn documentation and AI-900 official study guide, fine-tuning allows organizations to improve a model’s performance on domain-specific tasks or to align responses with brand tone and context.

Fine-tuning differs from simple prompting because it requires providing structured training data (usually in JSONL format) that contains pairs of input prompts and ideal completions. The model uses this data to adjust its internal weights, thereby “learning” your organization’s language patterns, terminology, or industry context.

Option review:

A. Completion: Refers to the text generated by a model in response to a prompt. It’s the output, not the customization process.

B. Grounding: Integrates external, up-to-date data sources (like search results or databases) during inference but doesn’t alter the model’s parameters.

C. Fine-tuning: ✅ Correct — this is the process of uploading and training with your own data.

D. Prompt engineering: Involves designing effective prompts but does not change the underlying model.

Thus, fine-tuning is the term used for customizing an Azure OpenAI model using your own uploaded data.

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

Features

The correct completion of the sentence is:

“In a machine learning model, the data that is used as inputs are called features.”

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module “Identify features of common machine learning types,” the term feature refers to an individual measurable property or characteristic of the data that is used by a machine learning model to make predictions or decisions.

In supervised and unsupervised learning, features serve as the inputs to the model. They are the variables that represent the information the algorithm learns from. For example, if a model predicts the price of a house, the features might include square footage, number of bedrooms, location, and age of the property. These features feed into the model so that it can learn the relationship between inputs and outputs.

Microsoft Learn further defines these key concepts:

Features: Input variables (independent variables) used by the model to learn patterns.

Labels: The desired output or target variable that the model is trained to predict (e.g., price, category).

Instances: Individual rows or data records within the dataset (each instance has multiple features).

Functions: Algorithms or mathematical operations used during training and prediction — not data inputs.

Therefore, among the provided options — features, functions, labels, instances — only features accurately describe the data elements used as inputs for training or inference in a machine learning model.

In summary, within the AI-900 learning context:

Features = inputs to the model.

Labels = outputs for supervised learning.

Instances = examples (rows) of data.

The correct matches are Custom Vision, Form Recognizer, and Face — each corresponding to a distinct capability under Azure Cognitive Services as described in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn modules on Computer Vision workloads.

Custom Vision → Identify objects in an imageThe Custom Vision service is part of the Azure Cognitive Services suite that enables developers to train custom image classification and object detection models. Unlike the prebuilt Computer Vision API, Custom Vision allows users to upload their own labeled images and teach the model to recognize specific objects relevant to their business context. The AI-900 syllabus explains that Custom Vision is ideal for tasks such as identifying products on a shelf, categorizing images, or detecting defects in manufacturing.

Form Recognizer → Automatically import data from an invoice to a databaseForm Recognizer is a document processing AI service that extracts structured data from forms, receipts, and invoices. It uses optical character recognition (OCR) combined with layout and key-value pair detection to automatically capture information such as invoice numbers, amounts, and vendor names. The AI-900 study materials highlight this service under the Document Intelligence category, emphasizing its ability to streamline data entry and business automation workflows by importing extracted data directly into databases or applications.

Face → Identify people in an imageThe Face service provides advanced facial detection and recognition capabilities. It can locate faces in images, compare similarities between faces, identify known individuals, and even detect facial attributes such as age or emotion. The AI-900 course classifies this under Computer Vision services for person identification and security-related use cases such as access control or identity verification.

Thus, each mapping aligns precisely with the AI-900 official learning outcomes on Cognitive Services capabilities:

Custom Vision → Object recognition

Form Recognizer → Data extraction from forms

Face → People identification

✅ Final verified configuration:

Custom Vision → Identify objects in an image

Form Recognizer → Automatically import data from an invoice to a database

Face → Identify people in an image

In Azure OpenAI Service, model behavior during text or chat generation is controlled by several parameters, such as temperature, max tokens, top_p, presence penalty, and frequency penalty. According to Microsoft Learn’s documentation for Azure OpenAI GPT models, the presence penalty influences how likely the model is to introduce new or diverse tokens in its responses.

Specifically, the presence penalty discourages the model from repeating previously used tokens, encouraging it to explore new topics or ideas instead of sticking to existing ones. Increasing the presence penalty value typically results in more diverse and creative outputs, while lowering it makes responses more repetitive or focused.

Option analysis:

A. Max response (Max tokens): Controls the maximum length of the generated response, not its diversity.

B. Past messages included: Defines how much chat history the model considers for context; it doesn’t affect diversity directly.

C. Presence penalty: Encourages novelty and introduces new tokens—this is correct for increasing response variety.

D. Stop sequence: Specifies a sequence of characters or tokens where the model should stop generating output.

This question maps each Azure AI service to its correct capability based on the Microsoft Azure AI Fundamentals (AI-900) syllabus and Microsoft Learn documentation on Azure Cognitive Services.

Classify and label images → Azure AI VisionAzure AI Vision (formerly Computer Vision) provides capabilities to analyze visual content, detect objects, classify images, and extract information from pictures. It includes object detection, image classification, and tagging, which are core vision tasks. This service enables businesses to build solutions that understand visual input, such as identifying products, reading signs, or detecting faces in images.

Generate conversational responses → Azure OpenAI ServiceAzure OpenAI Service integrates powerful large language models such as GPT-3.5 and GPT-4, capable of generating human-like text responses, summarizations, translations, and dialogues. These models are designed for natural language generation (NLG) and conversational AI, making them ideal for chatbots, virtual agents, and intelligent assistants that produce dynamic, context-aware replies.

Convert speech to text in real time → Azure AI SpeechAzure AI Speech provides speech-to-text capabilities (speech recognition) that convert spoken language into written text instantly. It is commonly used in transcription services, voice command systems, and live captioning applications. Additionally, the Speech service supports text-to-speech (speech synthesis) and speech translation, making it versatile for voice-based AI applications.

By understanding each service’s specialization—Vision for visual data, OpenAI for generative text, and Speech for audio processing—you can correctly match the capabilities.

According to Microsoft’s AI-900 study guide and the Microsoft Learn module “Identify common types of AI workloads”, computer vision is the branch of AI that enables machines to interpret and act upon visual inputs such as photos, videos, or camera feeds.

In this scenario, the recycling machine must identify bottles based on their shape and reject other objects. This requires the AI system to visually analyze an image or a video stream and make a decision based on shape recognition—a classic computer vision application.

Computer vision models are trained with labeled image data (e.g., images of correct bottle shapes vs. incorrect items). Using techniques such as object detection or image classification, the model learns to detect patterns and identify whether an image matches the desired object.

The other options are not suitable:

A. Anomaly detection identifies unusual data patterns, not visual features.

B. Conversational AI handles human-computer interactions via text or voice.

D. Natural language processing (NLP) deals with understanding and generating human language.

Therefore, because the task requires visual recognition of bottle shapes, the appropriate AI workload is Computer Vision.