Ditch the Recycled Junk: Master the AI-900 Exam with Confidence

Azure Kubernetes Service (AKS).

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn documentation on Azure Machine Learning, the Azure Kubernetes Service is commonly used to host and deploy machine learning models, including Automated ML models, into production environments. Once a model is trained using Azure Machine Learning (Azure ML), it must be deployed as a web service endpoint so it can receive data and return predictions.

Azure ML offers two primary options for hosting and deploying models:

Azure Kubernetes Service (AKS) – for high-scale, production-grade deployments that require fast response times, high availability, and scalability.

Azure Container Instances (ACI) – for testing or low-scale workloads where cost and simplicity are more important than performance.

AKS provides a managed Kubernetes cluster that allows for automated container orchestration, load balancing, scaling, and monitoring of deployed machine learning models. When you use Automated ML in Azure ML Studio, the generated model can be containerized and deployed directly to AKS, making it accessible as a REST API endpoint. This enables applications, systems, or users to send data and receive predictions in real time.

The other options serve different purposes:

Azure Data Factory is used for data integration and pipeline orchestration, not model hosting.

Azure Automation focuses on automating administrative tasks and runbooks, not ML deployment.

Azure Logic Apps is used to automate workflows and integrate services, not to serve ML models.

Therefore, the correct service to host automated machine learning (AutoML) models in production is Azure Kubernetes Service (AKS), as it provides a reliable, scalable, and secure environment for real-time inference and enterprise AI workloads.

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.

“Optical Character Recognition (OCR) extracts text from handwritten documents.”

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Identify features of computer vision workloads,” Optical Character Recognition (OCR) is a computer vision capability that enables AI systems to detect and extract printed or handwritten text from images, scanned documents, and photographs.

Microsoft Learn explains that OCR uses machine learning algorithms to analyze visual data, locate regions containing text, and then convert that text into machine-readable digital format. This capability is essential for automating processes such as document digitization, form processing, and data extraction.

OCR technology is provided through services such as the Azure Cognitive Services Computer Vision API and Azure Form Recognizer. The Computer Vision API’s OCR feature can extract text from both typed and handwritten sources, including receipts, invoices, letters, and forms. Once extracted, this text can be processed, searched, or stored electronically, enabling automation and efficiency in document management systems.

Let’s review the incorrect options:

Object detection identifies and locates objects in an image by drawing bounding boxes (e.g., detecting vehicles or people).

Facial recognition identifies or verifies individuals by comparing facial features.

Image classification assigns an image to one or more predefined categories (e.g., “dog,” “car,” “tree”).

None of these perform the task of extracting textual content from images — that is uniquely handled by Optical Character Recognition (OCR).

Therefore, based on the AI-900 official study content, the verified and correct answer is Optical Character Recognition (OCR), as it specifically extracts text (printed or handwritten) from image-based documents.

According to the Microsoft Azure AI Fundamentals (AI-900) learning path and Azure Text Analytics service documentation, key phrase extraction is a natural language processing (NLP) technique used to automatically identify the main topics or talking points within a text document or a collection of documents. This feature is designed to summarize textual data by detecting the most relevant words or short phrases that capture the essence of the content.

For example, if a document discusses “renewable energy sources such as solar and wind power,” the key phrases extracted might include “renewable energy,” “solar power,” and “wind power.” This helps users quickly understand the primary focus areas of large volumes of text without manual review.

In Azure, the Text Analytics service provides several core NLP capabilities, including:

Key phrase extraction – identifies main concepts or topics.

Entity recognition – detects and categorizes proper names like people, locations, or organizations.

Sentiment analysis – determines the emotional tone (positive, neutral, or negative).

Language detection – identifies the language used in the text.

Since the question specifies identifying main talking points, the correct feature is key phrase extraction, as it focuses on summarizing themes rather than identifying entities or emotions.

Therefore, the verified answer is B. key phrase extraction.

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.

According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation on Azure AI Vision, OCR is a computer vision technology that detects and extracts printed or handwritten text from images, scanned documents, or photographs. The OCR feature in Azure AI Vision can analyze images containing handwritten notes, recognize the characters, and convert them into machine-readable digital text.

This process is ideal for digitizing handwritten meeting notes, forms, or classroom materials. OCR works by identifying text regions in an image, segmenting characters or words, and then applying language models to interpret them correctly. Azure’s OCR capabilities support multiple languages and can handle varied handwriting styles.

Other options are incorrect because:

B. Object detection identifies and locates objects (like cars, animals, or furniture) within an image, not text.

C. Image classification assigns an image to a predefined category (e.g., “dog” or “cat”) rather than extracting text.

D. Facial detection detects or recognizes human faces, not written text.

Therefore, to convert handwritten notes into digital text, the correct computer vision technique is Optical Character Recognition (OCR).

Before running a pipeline in Azure Machine Learning Designer, you must have an available compute resource (such as a compute instance or compute cluster). Compute provides the processing power required to train, evaluate, and execute the pipeline’s modules.

Other options:

A. Jupyter notebook – Used for code-first development, not required for Designer pipelines.

B. Registered model – Created after running a pipeline, not before.

Reliability and safety: To build trust, it ' s critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.

The correct answer is Azure AI Custom Vision.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Explore computer vision in Azure”, the Azure AI Custom Vision service is specifically designed to allow users to train, test, and deploy custom image classification and object detection models using their own images.

The Azure AI Custom Vision service extends the capabilities of Azure’s general-purpose Computer Vision API by enabling organizations to upload their own labeled datasets, define custom tags (labels), and train models that are optimized for their specific use cases. This makes it ideal for object detection scenarios—such as detecting equipment in a manufacturing line, identifying products on store shelves, or recognizing medical images—where general-purpose models may not suffice.

By contrast, the Azure AI Computer Vision service provides pre-built models for tasks such as image description, tagging, face detection, and OCR (optical character recognition). It does not allow users to train their own models with custom data. Similarly, Azure AI Document Intelligence is used for extracting structured information from documents (forms, receipts, invoices), and Azure Video Analyzer for Media focuses on analyzing video content for insights and metadata extraction.

The AI-900 study guide emphasizes that the Custom Vision service supports two key model types:

Image Classification – categorizing entire images based on predefined tags.

Object Detection – identifying and locating multiple objects within an image by drawing bounding boxes.

Therefore, when the question specifies “train an object detection model by using your own images,” the correct Azure service is Azure AI Custom Vision, as it provides the necessary tools for training, evaluating, and deploying custom computer vision models tailored to a user’s dataset.

Hence, the verified correct answer is: Azure AI Custom Vision.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Describe core concepts of machine learning on Azure”, labeling is the process of assigning correct output values (labels) to training data before model training. In supervised learning, every input in the dataset must be paired with its corresponding output so the algorithm can learn the relationship between the two.

In this scenario, the task is to assign classes to images before training a classification model—for example, marking images as “cat,” “dog,” or “bird.” This process defines the target variable (label) that the model will later predict. During training, the classification model uses these labeled examples to learn patterns and distinguish between categories.

Microsoft’s official materials clearly define labeling as:

“The process of tagging data with the correct answer so that the model can learn to make predictions.”

Labeling is a crucial early step in the machine learning lifecycle, especially for image classification and natural language processing (NLP) tasks. Without accurate labels, the model cannot learn correctly and its predictions will be unreliable.

Let’s briefly clarify why the other options are incorrect:

Evaluation refers to testing the model after training to measure accuracy or performance using metrics like precision, recall, or F1 score.

Feature engineering involves creating or selecting the most relevant input features from raw data but does not involve tagging output labels.

Hyperparameter tuning adjusts parameters (like learning rate or depth of a tree) to optimize model performance after labeling and training have begun.

Thus, assigning classes to images prior to model training is definitively a Labeling task.