Pass the Microsoft Certified: Azure AI Fundamentals AI-900 Questions and answers with ExamsMirror

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore computer vision in Microsoft Azure,” there are multiple types of computer vision tasks, each designed for different goals such as recognizing, categorizing, or locating objects within an image.

In this question, the application performs two distinct tasks: locating birds within an image and identifying their species. Each of these corresponds to a different type of computer vision capability.

Locate the birds → Object detection

Object detection is used when an AI system needs to identify and locate multiple objects within a single image.

It not only recognizes what the object is but also provides bounding boxes that indicate the exact position of each object.

In this scenario, locating the birds (drawing rectangles around each bird) is achieved through object detection models, such as those available in the Azure Custom Vision Object Detection domain.

Identify the species of the birds → Image classification

Image classification focuses on identifying what is in the image rather than where it is.

It assigns a single label (or multiple labels in multilabel classification) to an entire image based on its contents.

In this case, determining the species of a bird (e.g., robin, eagle, parrot) is achieved through image classification, where the model compares visual features against learned patterns from training data.

Incorrect options:

Automated captioning generates descriptive sentences about an image, not object locations or classifications.

Optical character recognition (OCR) extracts text from images, irrelevant in this case.

When using generative AI models such as those in Azure OpenAI, it’s critical to monitor and control the model’s outputs to prevent the generation of harmful, hateful, or unsafe content. According to Microsoft’s Responsible AI guidelines and Azure OpenAI safety systems, this is achieved through content filtering mechanisms.

Content filtering automatically analyzes generated text and blocks or flags responses containing hate speech, harassment, sexual content, or violent language. Azure OpenAI applies these filters by default through its built-in content moderation system, which categorizes responses into safety levels (safe, review, block).

Option review:

A. Prompt engineering: Helps guide responses but does not detect hate content automatically.

B. Abuse monitoring: Refers to manual or post-processing review, not real-time automated filtering.

C. Content filtering: ✅ Correct — actively detects and prevents hateful or unsafe responses.

D. Fine-tuning: Adjusts model behavior using additional training data but doesn’t ensure safety monitoring.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Identify features of common machine learning types”, there are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Within supervised learning, two common approaches are regression and classification, while clustering is a primary example of unsupervised learning.

“You train a regression model by using unlabeled data.” – No.Regression models are trained with labeled data, meaning the input data includes both features (independent variables) and target labels (dependent variables) representing continuous numerical values. Examples include predicting house prices or sales forecasts. Unlabeled data (data without target output values) cannot be used to train regression models; such data is used in unsupervised learning tasks like clustering.

“The classification technique is used to predict sequential numerical data over time.” – No.Classification is used for categorical predictions, where outputs belong to discrete classes, such as spam/not spam or disease present/absent. Predicting sequential numerical data over time refers to time series forecasting, which is typically a regression or forecasting problem, not classification. The AI-900 syllabus clearly separates classification (categorical prediction) from regression (continuous value prediction) and time series (temporal pattern analysis).

“Grouping items by their common characteristics is an example of clustering.” – Yes.This statement is correct. Clustering is an unsupervised learning technique used to group similar data points based on their features. The AI-900 study materials describe clustering as the process of “discovering natural groupings in data without predefined labels.” Common examples include customer segmentation or document grouping.

Therefore, based on Microsoft’s AI-900 training objectives and definitions:

Regression → supervised learning using labeled continuous data (No)

Classification → categorical prediction, not sequential numeric forecasting (No)

Clustering → grouping by similarity (Yes)

According to the Microsoft Azure AI Fundamentals study guide and Microsoft Learn module “Identify features of computer vision workloads”, computer vision is an AI workload that allows systems to interpret and understand visual information from the world, such as images and videos.

Computer vision tasks typically include:

Object detection and image classification (e.g., detecting brands, logos, or items in images)

Image analysis (e.g., identifying colors, patterns, or visual features)

Face detection and recognition

Optical Character Recognition (OCR) for reading text in images

Therefore, both detecting brands and detecting color schemes in an image are clear examples of computer vision tasks because they involve analyzing visual content.

In contrast:

A. Predict stock prices → Regression task, not vision-based.

D. Translate text between languages → Natural language processing (NLP).

E. Extract key phrases → NLP as well.

Thus, the correct computer vision tasks are B and C.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Identify features of common machine learning types”, the three main machine learning model types differ by their purpose and the kind of data they use — whether supervised (using labeled data) or unsupervised (using unlabeled data).

Regression → A supervised machine learning model used to predict numeric values.Regression is a type of supervised learning that predicts continuous numerical outcomes. It learns the relationship between input features (independent variables) and a continuous target variable (dependent variable). Examples include predicting house prices, sales revenue, or temperature. The AI-900 curriculum emphasizes regression for “predicting numeric values based on known data,” using algorithms such as linear regression or decision tree regression.

Classification → A supervised machine learning model used to predict categories.Classification is also a supervised learning technique, but it predicts discrete outcomes (classes) instead of continuous values. It assigns input data to one or more categories based on learned patterns. Typical examples include spam detection (spam vs. not spam), sentiment analysis (positive, neutral, negative), or predicting loan approval (approved/denied). The AI-900 study materials describe classification as “predicting a category or label for new observations.”

Clustering → An unsupervised machine learning model used to group similar entities based on features.Clustering is an unsupervised learning approach, meaning it works on unlabeled data. It automatically identifies patterns and groups similar data points into clusters based on shared characteristics. Examples include customer segmentation (grouping customers by behavior) and grouping similar documents. The AI-900 learning module explains clustering as “discovering natural groupings in data without predefined labels.”

Thus, per Microsoft’s official AI-900 learning objectives:

Regression → Predicts numeric/continuous values.

Classification → Predicts categories/labels.

Clustering → Groups similar entities (unsupervised).

✅ Final Verified Configuration:

Regression → Predict numeric values

Classification → Predict categories

Clustering → Group similar entities based on features

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn documentation on Azure AI Vision services, different Azure AI Vision capabilities are suited for different use cases such as object detection, brand recognition, facial recognition, and spatial analysis.

App1: Uses a set of images and photos to extract brand names → Image AnalysisThe Azure AI Vision – Image Analysis service (formerly part of Computer Vision) can detect and extract brands, objects, text, and other visual features from images. It uses advanced image classification and object detection models to recognize logos and identify brand names (for example, “Microsoft” or “Coca-Cola”) in photos. The Image Analysis API can also return descriptive tags, scene descriptions, and confidence scores. Therefore, since App1 analyzes static images to extract brand names, it specifically relies on the Image Analysis feature of Azure AI Vision.

App2: Enables touchless access control for buildings → FaceThe Azure AI Face service is designed for facial detection, verification, and identification. It can recognize and match faces in real time, making it ideal for access control, identity verification, and attendance tracking systems. A “touchless access control” system uses a camera to detect a person’s face and verify identity against a stored profile, allowing or denying entry without physical interaction.

The other options are not suitable:

Optical Character Recognition (OCR) extracts text, not brand logos.

Spatial Analysis is for detecting movement or presence in video feeds.

Video Analysis is for analyzing dynamic video content rather than still images.

This question assesses knowledge of the Azure Cognitive Services Speech and Text Analytics capabilities, as described in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn modules “Explore natural language processing” and “Explore speech capabilities.” These services are part of Azure Cognitive Services, which provide prebuilt AI capabilities for speech, language, and text understanding.

You can use the Speech service to transcribe a call to text → YesThe Speech-to-Text feature in the Azure Speech service automatically converts spoken words into written text. Microsoft Learn explains: “The Speech-to-Text capability enables applications to transcribe spoken audio to text in real time or from recorded files.” This makes it ideal for call transcription, voice assistants, and meeting captioning.

You can use the Text Analytics service to extract key entities from a call transcript → YesOnce a call has been transcribed into text, the Text Analytics service (part of Azure Cognitive Services for Language) can process that text to extract key entities, key phrases, and sentiment. For example, it can identify names, organizations, locations, and product mentions. Microsoft Learn notes: “Text Analytics can extract key phrases and named entities from text to derive insights and structure from unstructured data.”

You can use the Speech service to translate the audio of a call to a different language → YesThe Azure Speech service also includes Speech Translation, which can translate spoken language in real time. It converts audio input from one language into translated text or speech output in another language. Microsoft Learn describes this as: “Speech Translation combines speech recognition and translation to translate spoken audio to another language.”

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Identify features of classification machine learning”, classification is a supervised machine learning technique used when the output variable represents discrete categories or classes. In this case, the brain scan images are already labeled into predefined haemorrhage types, such as “subarachnoid,” “epidural,” or “intraventricular.” The model’s goal is to learn patterns from labeled examples and then predict the correct class for new, unseen images.

The use of categorized brain scan images clearly indicates a supervised learning setup because both the input (image data) and output (haemorrhage type) are known during training. This aligns with Microsoft’s definition: classification problems “predict which category or class an item belongs to,” often using algorithms such as logistic regression, decision trees, neural networks, or convolutional neural networks (CNNs) for image-based data.

In contrast:

A. Clustering is an unsupervised learning approach that groups data into clusters based on similarity when no predefined labels exist.

B. Regression predicts continuous numeric values (e.g., predicting age or temperature), not categories.

Because this project aims to automatically classify medical images into known diagnostic categories, it is a textbook example of classification.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Identify guiding principles for responsible AI”, responsible AI is built upon six foundational principles: Fairness, Reliability and Safety, Privacy and Security, Inclusiveness, Transparency, and Accountability. Each principle serves to guide the ethical design, deployment, and management of artificial intelligence systems.

Fairness – This principle ensures that AI systems treat all people fairly and do not discriminate based on personal attributes such as gender, race, or age. The Microsoft Learn content emphasizes that “AI systems should treat everyone fairly” and that organizations must evaluate datasets and model outputs for bias. In this scenario, “The system must not discriminate based on gender, race” clearly aligns with Fairness because it directly addresses equitable treatment and unbiased decision-making.

Privacy and Security – Microsoft’s responsible AI framework stresses that “AI systems must be secure and respect privacy.” This means personal data should be safeguarded, processed lawfully, and visible only to authorized users. The statement “Personal data must be visible only to approved users” reflects the importance of protecting sensitive information and controlling access—precisely the intent of the Privacy and Security principle.

Transparency – Transparency refers to ensuring that users understand how AI systems operate and make decisions. Microsoft notes that “AI systems should be understandable and users should be able to know why decisions are made.” The requirement “Automated decision-making processes must be recorded so that approved users can identify why a decision was made” directly supports this principle. Transparency promotes trust and accountability by documenting the reasoning behind AI outputs.

Reliability and Safety, though another core principle, does not directly relate to any of the provided statements in this question.

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.