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

“The Azure OpenAI GPT-3.5 Turbo model can transcribe speech to text.” — NOThis statement is false. The GPT-3.5 Turbo model is a text-based large language model (LLM) designed for natural language understanding and generation, such as answering questions, summarizing text, or writing content. It does not process or transcribe audio input. Speech-to-text capabilities belong to Azure AI Speech Services, specifically the Speech-to-Text API, not Azure OpenAI.

“The Azure OpenAI DALL-E model generates images based on text prompts.” — YESThis statement is true. The DALL-E model, available within Azure OpenAI Service, is a generative AI model that creates original images from natural language descriptions (text prompts). For example, given a prompt like “a futuristic city at sunset,” DALL-E generates a unique, high-quality image representing that concept. This aligns with generative AI workloads in the AI-900 study guide, where DALL-E is specifically mentioned as an image-generation model.

“The Azure OpenAI embeddings model can convert text into numerical vectors based on text similarities.” — YESThis statement is also true. The embeddings model in Azure OpenAI converts text into multi-dimensional numeric vectors that represent semantic meaning. These embeddings enable tasks such as semantic search, recommendations, and text clustering by comparing similarity scores between vectors. Words or phrases with similar meanings have vectors close together in the embedding space.

In summary:

GPT-3.5 Turbo → Text generation (not speech-to-text)

DALL-E → Image generation from text prompts

Embeddings → Convert text into numerical semantic representations

Correct selections: No, Yes, Yes.

facial analysis.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Describe features of computer vision workloads on Azure,” facial analysis is a computer vision capability that detects faces and extracts attributes such as facial expressions, emotions, pose, occlusion, and image quality factors like exposure and noise. It does not identify or verify individual identities; rather, it interprets facial features and image characteristics to analyze conditions in an image.

In this question, the AI solution helps photographers take better portrait photos by providing feedback on exposure, noise, and occlusion — tasks directly linked to facial analysis. The model analyzes the detected face to determine if the image is well-lit, clear, and unobstructed, thereby improving photo quality. These capabilities are part of the Azure Face service in Azure Cognitive Services, which includes both facial detection and facial analysis functionalities.

Here’s how the other options differ:

Facial detection only identifies that a face exists in an image and provides its location using bounding boxes, without further interpretation.

Facial recognition goes a step further — it attempts to identify or verify a person’s identity by comparing the detected face with stored images. This is not what the scenario describes.

Thus, when an AI solution evaluates image quality aspects like exposure, noise, and occlusion, it’s performing facial analysis, which focuses on understanding image and facial characteristics rather than identification.

In summary, based on Microsoft’s AI-900 study material, this scenario demonstrates facial analysis, a subcategory of computer vision tasks within Azure Cognitive Services.

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials, a webchat bot is defined as a conversational AI application that interacts with users through a web-based chat interface. It simulates human conversation using text (and sometimes voice) to answer questions, assist with transactions, or provide information automatically. Microsoft Learn’s “Describe features of common AI workloads” module highlights conversational AI as a major AI workload, where bots and virtual agents are used to provide automated, intelligent responses in real time through web, mobile, or messaging platforms.

In this scenario, the chatbot on the festival website provides immediate answers about scheduled events and ticket purchases. This aligns exactly with how a webchat bot operates — interacting with users through a website, handling repetitive inquiries, and providing consistent information without human intervention. This type of solution is commonly built using Azure Bot Service integrated with Azure Cognitive Services for Language, which allows the bot to understand user intent and respond naturally.

Let’s examine the other options to reinforce why D is correct:

A describes a text analytics or sentiment analysis scenario, not a conversational bot, because it classifies text sentiment but doesn’t “chat” with a user.

B is an example of machine translation using the Translator service, not a chatbot.

C is an email classification or natural language processing task, not a webchat interaction.

The AI-900 exam objectives clearly distinguish conversational AI from other cognitive services such as translation or sentiment analysis. Conversational AI focuses on dialogue and interaction through natural language conversation channels like websites or messaging apps.

Therefore, the verified and officially aligned answer is D. From a website interface, answer common questions about scheduled events and ticket purchases for a music festival.

Microsoft’s Responsible AI Principles, as outlined in the AI-900 certification materials and official Microsoft documentation, emphasize six guiding principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. The principle of transparency means that AI systems should be designed so their decisions and processes are understandable and explainable to users and stakeholders.

In this scenario, the AI system is being developed to decide whether a loan should be approved. Such a decision directly affects people’s lives and finances, so it is essential that the system can explain which factors influenced its decision—for example, credit score, income, or payment history. Microsoft’s Responsible AI framework stresses that transparency helps ensure trust between humans and AI systems. When decisions are explainable, users can understand and contest the reasoning if necessary.

The other options do not align precisely with this scenario:

B. Inclusiveness focuses on making AI accessible to all people, regardless of ability or background.

C. Fairness ensures that AI systems treat all individuals equally and do not discriminate. While fairness is important for loan assessment, the question specifically highlights the need for explainability, not equality.

D. Privacy and Security deals with safeguarding user data, which is separate from explaining decisions.

Therefore, the principle demonstrated here is transparency, as it ensures decision-making processes are clear, explainable, and traceable—directly aligning with Microsoft’s responsible AI guidance.

This question tests your understanding of machine learning workflow tasks as described in the Microsoft Azure AI Fundamentals (AI-900) study guide and the Microsoft Learn module “Explore the machine learning process.” The AI-900 curriculum divides the machine learning lifecycle into key phases: data preparation, feature engineering and selection, model training, model evaluation, and model deployment. Each phase has specific tasks designed to prepare, build, and assess predictive models before deployment.

Examining the values of a confusion matrix → Model evaluationIn Azure Machine Learning, evaluating a model involves checking its performance using metrics such as accuracy, precision, recall, and F1-score. The confusion matrix is one of the most common tools for this purpose. According to Microsoft Learn, “model evaluation is the process of assessing a trained model’s performance against test data to ensure reliability before deployment.” Analyzing the confusion matrix helps determine whether predictions align with actual outcomes, making this task part of model evaluation.

Splitting a date into month, day, and year fields → Feature engineeringFeature engineering refers to transforming raw data into features that better represent the underlying patterns to improve model performance. The study guide describes it as “the process of creating new input features from existing data.” Splitting a date field into separate numeric fields (month, day, year) is a classic example of feature engineering because it enables the model to learn from temporal patterns that might otherwise remain hidden.

Picking temperature and pressure to train a weather model → Feature selectionFeature selection involves identifying the most relevant variables that have predictive power for the model. As defined in Microsoft Learn, “feature selection is the process of choosing the most useful subset of input features for training.” In this scenario, selecting temperature and pressure variables as inputs for a weather prediction model fits perfectly within the feature selection stage.

Therefore, the correct matches are:

✅ Examining confusion matrix → Model evaluation

✅ Splitting date field → Feature engineering

✅ Picking temperature & pressure → Feature selection

The Azure AI Vision service (formerly Computer Vision) is designed to analyze visual content in images and videos. According to Microsoft Learn’s “Describe features of computer vision workloads,” Azure AI Vision can identify objects, people, text, and scenes, and even classify images or detect objects in real time.

Identifying breeds of animals in live video streams is an example of image classification or object detection—core capabilities of Azure AI Vision. The Vision service can analyze each frame in a video, recognize animals, and classify them according to known categories, making this the correct answer.

The other options are incorrect:

B. Extracting key phrases from documents → Done by Azure AI Language (Text Analytics).

C. Extracting data from handwritten letters → Done by Azure AI Document Intelligence (Form Recognizer) using OCR.

D. Creating thumbnails for training videos → While possible in Azure Media Services, it’s not a primary Azure AI Vision function.

Thus, the best answer is A. Identifying breeds of animals in live video streams.

According to Microsoft’s Responsible AI principles, one of the six core principles is fairness, which ensures that AI systems treat all individuals equitably and that their outcomes are not influenced by biases present in the training data or algorithms. The official Microsoft Learn module “Identify the guiding principles for responsible AI” clearly defines fairness as the requirement that AI systems should not amplify or perpetuate existing societal biases.

In this scenario, the statement emphasizes that AI systems should NOT reflect biases from the datasets used to train them, which directly aligns with the fairness principle. Bias in AI models can arise when the data used for training is unbalanced or not representative of the real-world population. For instance, if a facial recognition model is trained mostly on images of one demographic group, it may perform poorly on others—an example of unfair bias. Microsoft advocates building and testing AI systems with diverse, high-quality datasets to ensure fair performance across all groups.

The other principles listed—accountability, inclusiveness, and transparency—are also important but do not directly address bias mitigation:

Accountability ensures that people remain responsible for AI systems and their decisions.

Inclusiveness promotes accessibility and usability for all people, including those with disabilities.

Transparency focuses on explaining how AI systems make decisions.

However, Fairness explicitly deals with avoiding discrimination and bias in AI outcomes and training data.

Thus, in Microsoft’s Responsible AI framework, ensuring that systems do not reflect biases from datasets is part of the Fairness principle, which promotes equitable and unbiased treatment for all individuals in AI-driven decisions.

A: R-squared (R2), or Coefficient of determination represents the predictive power of the model as a value between -inf and 1.00. 1.00 means there is a perfect fit, and the fit can be arbitrarily poor so the scores can be negative.

C: RMS-loss or Root Mean Squared Error (RMSE) (also called Root Mean Square Deviation, RMSD), measures the difference between values predicted by a model and the values observed from the environment that is being modeled.

B: Azure ' s Computer Vision service provides developers with access to advanced algorithms that process images and return information based on the visual features you ' re interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.

C: Computer Vision includes Optical Character Recognition (OCR) capabilities. You can use the new Read API to extract printed and handwritten text from images and documents.