Free AI-900 Exam Dumps Preview

https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/split-data

References:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier

References:
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.0

Box 1: Regression
In the most basic sense, regression refers to prediction of a numeric target.

Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.

You use this module to define a linear regression method, and then train a model using a labeled dataset.
The trained model can then be used to make predictions.

Box 2: Classification
Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.

Box 3: Clustering
Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.

Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.

References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression

The correct answer is B. Azure AI Document Intelligence (formerly Form Recognizer). This Azure service uses AI and OCR technologies to analyze and extract structured data from documents such as invoices, receipts, and purchase orders. It identifies key fields like billing address, invoice number, total amount due, and line items. The service supports prebuilt models for common document types and custom models for
specialized layouts. Option review:

A. Azure AI Search: Used for knowledge mining and semantic search, not document data extraction.
B. Azure AI Document Intelligence - # Correct. Designed for form and invoice extraction.
C. Azure AI Custom Vision: Used for image classification and object detection, not text extraction.
D. Azure OpenAI: Generates or processes language but not structured document data.

Therefore, Azure AI Document Intelligence is the right service to extract data from scanned invoices.

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.

References:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-m

Incorrect:
Skill actions include
* Use skills for complex, multi-turn operations. For example, schedule a meeting or book a flight. (Not A,
Not B)
* Use skills to emit any supported bot response. For example, show an adaptive card or send random
responses. (not C)

References:
https://learn.microsoft.com/en-us/power-virtual-agents/configuration-add-skills

Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. 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. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.

The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like "indoor", which can't be localized with bounding boxes.

References:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection

Regression is used to predict numeric values, such as prices. It models the relationship between input features and a continuous output variable.

Box 1: Facial recognition
Face detection that perceives faces and attributes in an image; person identification that matches an individual in your private repository of up to 1 million people; perceived emotion recognition that detects a
range of facial expressions like happiness, contempt, neutrality, and fear; and recognition and grouping of similar faces in images.

Box 2: OCR

Box 3: Objection detection
Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. 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. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.

The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like "indoor", which can't be localized with bounding boxes.

References:
https://azure.microsoft.com/en-us/services/cognitive-services/face/
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection

You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. The service can also provide links to more information about that entity on the web. An entity is essentially an item of a particular type or a category; and in some cases,
subtype, such as those as shown in the following table.
https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview

D: Content types of documents you can add to a knowledge base: Content types include many standard structured documents such as PDF, DOC, and TXT.

Note: The tool supports the following file formats for ingestion: .tsv: QnA contained in the format Question(tab)Answer.
.txt, .docx, .pdf: QnA contained as regular FAQ content--that is, a sequence of questions and answers.

Incorrect Answers:
A: PPTX is the default presentation file format for new PowerPoint presentations.
B: It is not possible to ingest xml file directly.

References:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/data-sources-and-content

Regression is a supervised machine learning technique used to predict numeric values.

References:
https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/1-introduction

This question is drawn from the Microsoft Azure AI Fundamentals (AI-900) syllabus section "Describe features of natural language processing (NLP) workloads on Azure." According to the Microsoft Learn materials, Natural Language Processing (NLP) is a branch of artificial intelligence that allows computers to analyze, understand, and generate human language. NLP enables machines to work with text or speech data in a way that extracts meaning, sentiment, and intent. Microsoft defines NLP as enabling scenarios such as language detection, text classification, key phrase extraction, sentiment analysis, and named entity recognition. The example given-classifying emails as "work-related" or "personal"-is a text classification task, which falls under NLP capabilities. The AI model processes the textual content of emails, identifies linguistic patterns, and categorizes them based on the detected topic or context. Let's analyze the other options: Predict the number of future car rentals # This is a forecasting task, handled by machine learning regression models, not NLP. Predict which website visitors will make a transaction # This is a classification or prediction problem in machine learning, not NLP, since it deals with behavioral or numerical data rather than language. Stop a process in a factory when extremely high temperatures are registered # This is an IoT or anomaly detection scenario, focusing on sensor data, not language
understanding. Therefore, only classifying email messages as work-related or personal correctly represents an NLP use case. It illustrates how NLP can analyze written text and make intelligent categorizations-a key capability covered in AI-900's natural language workloads section.

For an app that helps employees write emails and reports based on user prompts, you need a text generation model capable of understanding natural language instructions and producing coherent, contextually appropriate output. Azure OpenAI GPT models-available through Azure AI Foundry (formerly Azure OpenAI Studio)-are specifically designed for such generative tasks. By integrating GPT-3.5 or GPT- 4, the app can analyze prompts like "Write a professional email to a client about project updates" and automatically generate polished text in seconds. The other options do not fit:

A. Azure AI Speech: Converts spoken language to text or text to speech; not suitable for generating written
content.
C. Azure AI Vision: Processes and analyzes images or video content.
D. Azure Machine Learning Studio: Used for training, testing, and deploying custom ML models, not
directly for content generation.

Therefore, to create a writing-assistance app for emails and reports, the correct solution is B. Azure OpenAI in Foundry Models using GPT-based language generation.

Sentiment analysis models often output scores where negative values indicate negative sentiment, positive values indicate positive sentiment, and values close to zero represent neutral sentiment.

References:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-designer-python
https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml

In Azure, the Language service unifies several natural language capabilities, including LUIS, QnA Maker, and Text Analytics, into one comprehensive service. To determine a user's intent in a chatbot, you use the Conversational Language Understanding (CLU) feature of the Language service, which is the evolution of LUIS. CLU helps chatbots and applications comprehend natural language input by identifying the intent (the purpose of the user's statement) and extracting entities (important details). For example, when a user types "Book a meeting for tomorrow," the model recognizes the intent (BookMeeting) and the entity (tomorrow). The other options do not determine intent: Translator (A) is used for language translation.
Azure Cognitive Search (B) retrieves documents based on search queries. Speech (C) converts audio to text but doesn't analyze meaning.

References:
Microsoft Learn - "Build conversational language understanding models with Azure Language service."

References:
https://docs.microsoft.com/en-us/azure/applied-ai-services/form-recognizer/overview?tabs=v2-1

Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent classes) to images, according to their visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify the labels to apply.

Note: The Custom Vision service uses a machine learning algorithm to apply labels to images. You, the developer, must submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then the algorithm trains to this data and calculates its own accuracy by testing itself on those same images. Once the algorithm is trained, you can test, retrain, and eventually use it to classify new images according to the needs of your app. You can also export the model itself for offline use.

Incorrect Answers:
Computer Vision:
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.

References:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/home

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft's
Responsible AI Framework, the Reliability and Safety principle ensures that AI systems operate consistently, accurately, and as intended, even when confronted with unexpected data or edge cases. It emphasizes that AI systems must be tested, validated, and monitored to ensure stable performance and to prevent harm caused by inaccurate or unreliable outputs. In the given scenario, the AI system is designed not to provide predictions when key fields contain unusual or missing values. This approach demonstrates that the system is built to avoid unreliable or unsafe outputs that could result from incomplete or corrupted data. Microsoft explicitly outlines that reliable AI systems must handle data anomalies and input validation properly to prevent incorrect predictions.

Here's how the other options differ: Inclusiveness ensures accessibility for all users, including those with disabilities or from different backgrounds. It's unrelated to prediction control or data reliability. Privacy and Security protects sensitive data and ensures proper handling of personal information, not system prediction logic. Transparency ensures that users understand how an AI system makes its decisions but doesn't address prediction reliability. Thus, stopping a prediction when data is incomplete or abnormal directly supports the Reliability and Safety principle - it ensures that the AI model functions correctly under valid conditions and avoids unintended or harmful outcomes. This principle aligns with Microsoft's
Responsible AI guidance, which highlights that AI solutions must "operate reliably and safely, even under unexpected conditions, to protect users and maintain trust."

The correct answer is Document Intelligence. According to the Microsoft Azure AI Fundamentals (AI-900) study materials and Microsoft Learn documentation, the Azure AI Document Intelligence service (formerly known as Form Recognizer) is specifically designed to extract structured data from documents, including scanned invoices, receipts, forms, and business cards. This service combines optical character recognition (OCR) with machine learning to analyze both the layout and semantic meaning of document content.
When processing scanned invoices, Document Intelligence identifies and extracts fields such as invoice numbers, dates, totals, taxes, vendor names, and line-item details. The extracted information can then be automatically imported into business systems like accounting software or databases, eliminating manual data entry and improving operational efficiency. Here's why the other options are incorrect: Generative AI: Focuses on creating new content such as text, images, or code (for example, using GPT-4 or DALL- E). It is not used for structured data extraction. Natural Language Processing (NLP): Deals with understanding and generating human language from text-based input, not document scanning or layout interpretation.
The Document Intelligence workload excels at handling semi-structured documents where the location and format of data vary between samples. Microsoft's prebuilt models-like Invoice, Receipt, Identity Document, and Contract-simplify extraction without requiring custom training. In summary, if the task involves extracting data from scanned invoices, the appropriate Azure AI service is Azure AI Document Intelligence, which uses AI-powered document understanding to convert unstructured document images into structured, usable data.

Brand detection is a specialized mode of object detection that uses a database of thousands of global logos to identify commercial brands in images or video. You can use this feature, for example, to discover which brands are most popular on social media or most prevalent in media product placement.

The Azure AI Vision service detects whether there are brand logos in a given image; if there are, it returns the brand name, a confidence score, and the coordinates of a bounding box around the logo.

The built-in logo database covers popular brands in consumer electronics, clothing, and more. If you find that the brand you're looking for is not detected by the Azure AI Vision service, you could also try creating and training your own logo detector using the Custom Vision service.

References:
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-brand-detection

Computer vision services can analyze images from many sources, including user uploads. Once an image is submitted, the service can perform tasks such as object detection, tagging, or text recognition.

Box 1: verification
Face verification: Check the likelihood that two faces belong to the same person and receive a confidence score.

Box 2: similarity

Box 3: Grouping

Box 4: identification
Face detection: Detect one or more human faces along with attributes such as: age, emotion, pose, smile, and facial hair, including 27 landmarks for each face in the image.

References:
https://azure.microsoft.com/en-us/services/cognitive-services/face/#features

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.

References:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/home
Detect faces in an image - Face API Microsoft Azure provides multiple cognitive services that you can use to detect and analyze faces, including: Computer Vision, which offers face detection and some basic face analysis, such as determining age. Video Indexer, which you can use to detect and identify faces in a video. Face, which offers pre-built algorithms that can detect, recognize, and analyze faces. Recognize hand written text - Read API The Read API is a better option for scanned documents that have a lot of text. The Read API also has the ability to automatically determine the proper recognition model

The fairness principle ensures that AI systems treat all individuals equally and do not disadvantage certain groups. In this scenario, the system must fairly represent feedback regardless of whether it comes from spoken or written input.

DALL-E is an image generation model available through Azure OpenAI that creates images from text prompts. Parameters such as image size and prompt text are commonly used when generating images.
Codex is designed for code generation and GPT-4 primarily focuses on text-based tasks.

You can drag-and-drop datasets and modules onto the canvas.

References:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer

An utterance is a phrase or sentence that a user might input, used to train the model to recognize intents.

According to the Microsoft Azure AI Fundamentals (AI-900) curriculum and Microsoft Learn's modules on Computer Vision, object detection is the AI technique used to identify and locate multiple objects within an image. Unlike simple image classification, which only labels an entire image with a single category (for example, "This is a product"), object detection not only identifies the type of object but also pinpoints its exact position by providing bounding boxes and coordinates within the image. In the scenario described - identifying the location of products on a conveyor belt - the system must be able to detect multiple items simultaneously and determine their spatial positions. Object detection algorithms (such as YOLO, Faster R-CNN, or SSD) are specifically designed for this purpose. This allows automation systems, like robotic arms or quality inspection systems, to track product locations in real time for sorting, packaging, or defect detection. Let's evaluate the other options: Image classification only determines what is in the image, not where it is located. It cannot handle multiple objects or their positions.

Image processing involves operations like resizing, filtering, or adjusting contrast, not understanding object placement. Optical character recognition (OCR) extracts text from images and documents, unrelated to locating physical items. Thus, per Microsoft Learn's AI-900 guidance, object detection is the correct computer vision capability when a task requires both identification and spatial localization of items in an image or video stream. # Final Answer: Object detection

Privacy and security. According to Microsoft's Responsible AI Principles, implementing filters to block harmful or inappropriate content in a Generative AI chat solution demonstrates a commitment to the Privacy and Security principle. This principle ensures that AI systems are designed and operated in a way that protects users, their data, and society from harm. When a chat system uses Generative AI models (like Azure OpenAI's GPT-based services), there is a risk that the model might produce unsafe, offensive, or sensitive content. Microsoft addresses this through content filters and safety systems, which automatically detect and block violent, hate-based, or sexually explicit outputs. This is part of responsible deployment practices to ensure that user interactions remain safe, private, and compliant with ethical standards.

Implementing these filters aligns with the Privacy and Security principle because it: Protects users from exposure to harmful or abusive content. Ensures that conversations are safeguarded against malicious or unsafe use. Upholds user trust by maintaining a safe digital environment for all participants. Let's briefly clarify why the other options are incorrect: Fairness deals with ensuring unbiased treatment and equitable outcomes in AI decisions. Transparency focuses on explaining how AI systems make decisions.
Accountability refers to human oversight and responsibility for AI actions. Thus, content filtering mechanisms are explicitly an example of Privacy and Security, as they protect users and data from harm or misuse while maintaining ethical AI behavior. Therefore, the verified correct answer is Privacy and security.

AI agents can operate across multiple interaction channels such as voice assistants, web chat applications, and collaboration platforms like Microsoft Teams. Azure Storage is used for data storage and does not provide a user interaction interface.

Anomaly Detector, an AI service that helps you foresee problems before they occur.
Easily embed time-series anomaly detection capabilities into your apps to help users identify problems quickly. Anomaly Detector ingests time-series data of all types and selects the best anomaly detection algorithm for your data to ensure high accuracy. Detect spikes, dips, deviations from cyclic patterns, and trend changes through both univariate and multivariate APIs. Customize the service to detect any level of anomaly. Deploy the anomaly detection service where you need it - in the cloud or at the intelligent edge.

References:
https://azure.microsoft.com/en-us/products/cognitive-services/anomaly-detector

Box 1: Yes
Azure Cognitive Service for Language provides features including:
* Language detection: This pre-configured feature evaluates text, and determines the language it was written in. It returns a language identifier and a score that indicates the strength of the analysis.

Box 2: No
Handwritten detection is part of OCR (Optical Character Recognition).

Box 3: Yes
Azure Cognitive Service for Language provides features including:
* Named Entity Recognition (NER): This pre-configured feature identifies entities in text across several pre-
defined categories.

Note: Named entity recognition is a natural language processing technique that can automatically scan entire articles and pull out some fundamental entities in a text and classify them into predefined categories.
Entities may be,

Organizations,
Quantities,
Monetary values,
Percentages, and more.
People's names

Company names
Geographic locations (Both physical and political)
Product names
Dates and times
Amounts of money
Names of events

References:
https://docs.microsoft.com/en-us/azure/cognitive-services/language-service/overview

Box 1: Model evaluation
The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, as well as ROC, Precision/Recall, and Lift curves.

Box 2: Feature engineering
Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization.

Note: Often, features are created from raw data through a process of feature engineering. For example, a time stamp in itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day.

Box 3: Feature selection
In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.

References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml

Azure's Computer Vision API includes Optical Character Recognition (OCR) capabilities that extract printed or handwritten text from images. You can extract text from images, such as photos of license plates or containers with serial numbers, as well as from documents - invoices, bills, financial reports, articles, and more.

References:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-recognizing-text

References:
https://www.cloudfactory.com/data-labeling-guide

The Translator service provides real-time multilingual text translation using neural machine translation technology.

References:
https://azure.microsoft.com/en-us/services/cognitive-services/text-to-speech/#features

The label is the column you want to predict. The identified Featuresare the inputs you give the model to predict the Label.

Example:
The provided data set contains the following columns:

vendor_id: The ID of the taxi vendor is a feature. rate_code: The rate type of the taxi trip is a feature. passenger_count: The number of passengers on the trip is a feature. trip_time_in_secs: The amount of time the trip took. You want to predict the fare of the trip before the trip is completed. At that moment, you don't know how long the trip would take. Thus, the trip time is not a feature and you'll exclude this column from the model. trip_distance: The distance of the trip is a feature. payment_type: The payment method (cash or credit card) is a feature. fare_amount: The total taxi fare paid is the label.

References:
https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/predict-prices

References:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/how-to/chit-chat-knowledge-base?tabs=v1

Language Understanding (LUIS) is a cloud-based API service that applies custom machine-learning intelligence to a user's conversational, natural language text to predict overall meaning, and pull out relevant, detailed information.

Design your LUIS model with categories of user intentions called intents. Each intent needs examples of user utterances. Each utterance can provide data that needs to be extracted with machine-learning entities.

References:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/what-is-luis