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

According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Azure Cognitive Services documentation, the Custom Vision service is a specialized computer vision tool that allows users to build, train, and deploy custom image classification and object detection models. It is part of the Azure Cognitive Services suite, designed for scenarios where pre-built Computer Vision models do not meet specific business requirements.

“The Custom Vision service can be used to detect objects in an image.” → YesThis statement is true. The Custom Vision service supports object detection, enabling the model to identify and locate multiple objects within a single image using bounding boxes. For example, it can locate cars, products, or animals in photos.

“The Custom Vision service requires that you provide your own data to train the model.” → YesThis statement is true. Unlike pre-trained models such as the standard Computer Vision API, the Custom Vision service requires users to upload and label their own images. The system uses this labeled dataset to train a model specific to the user’s scenario, improving accuracy for custom use cases.

“The Custom Vision service can be used to analyze video files.” → NoThis statement is false. The Custom Vision service works only with static images, not videos. To analyze video files, Azure provides Video Indexer and Azure Media Services, which are designed for extracting insights from moving visual content.

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore fundamental principles of machine learning,” anomaly detection is a machine learning workload used to identify data points or patterns that deviate significantly from expected behavior.

In this scenario, the IoT device monitors engine temperature and generates alerts when the readings deviate from normal operating ranges. This directly matches the definition of anomaly detection, where the AI system learns what “normal” looks like and identifies outliers or abnormal conditions that may indicate potential issues.

Common real-world uses of anomaly detection include:

Detecting equipment malfunctions or overheating in IoT systems.

Identifying fraudulent transactions in finance.

Detecting unusual spikes or drops in system metrics (e.g., temperature, traffic, or pressure).

Other options are incorrect:

A. NLP (Natural Language Processing): Focuses on understanding and interpreting human language, not sensor data.

B. Computer Vision: Involves analyzing images or videos, which is unrelated to temperature data.

D. Knowledge Mining: Refers to extracting information from large document stores, not identifying abnormal readings.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn Cognitive Services documentation, developing a voice-controlled personal assistant app involves integrating multiple Azure AI services that specialize in different aspects of language and speech processing. The three services in focus—Azure AI Speech, Azure AI Language Service, and Azure AI Translator Text—perform unique but complementary roles in conversational AI systems.

Convert a user’s speech to text → Azure AI SpeechThe Azure AI Speech service provides speech-to-text (STT) capabilities. It enables applications to recognize spoken language and convert it into written text in real time. This is often the first step in voice-enabled applications, transforming audio input into a machine-readable format that can be analyzed further.

Identify a user’s intent → Azure AI Language serviceOnce speech has been transcribed, the Azure AI Language service (which includes capabilities like Conversational Language Understanding and Text Analytics) interprets the meaning of the text. It detects the user’s intent (what the user wants to accomplish) and extracts entities (key data points) from the input. This service helps the assistant understand commands like “Book a flight” or “Set a reminder.”

Provide a spoken response to the user → Azure AI SpeechAfter determining an appropriate response, the system uses the text-to-speech (TTS) feature of Azure AI Speech to convert the assistant’s text-based reply back into natural-sounding spoken language, allowing the user to hear the response.

Together, these services form the backbone of a conversational AI system: Speech-to-Text → Language Understanding → Text-to-Speech, aligning precisely with the AI-900 curriculum’s explanation of how Azure Cognitive Services enable intelligent voice-based interactions.

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.

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

This question evaluates understanding of clustering—an unsupervised learning technique explained in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Explore fundamental principles of machine learning.” Clustering involves finding natural groupings within data without prior knowledge of output labels. The algorithm identifies similarities among data points and groups them accordingly, with each group (or cluster) containing items that are more similar to each other than to those in other groups.

Organizing documents into groups based on similarities of the text contained in the documents → YesThis is a classic clustering application. In text analytics or natural language processing (NLP), clustering algorithms such as K-means or hierarchical clustering are used to group documents with similar content or topics. According to Microsoft Learn, “clustering identifies relationships in data and groups items that share common characteristics.” Therefore, organizing text documents based on content similarity is a textbook example of clustering.

Grouping similar patients based on symptoms and diagnostic test results → YesThis is another example of clustering. In healthcare analytics, clustering can be used to segment patients with similar health patterns or risks. The study guide emphasizes that clustering can “discover natural groupings in data such as customers with similar buying patterns or patients with similar clinical results.” Thus, this task correctly describes unsupervised clustering because it does not involve predicting a known outcome but grouping based on similarity.

Predicting whether a person will develop mild, moderate, or severe allergy symptoms based on pollen count → NoThis is a classification problem, not clustering. Classification is a supervised learning technique where the model is trained with labeled data to predict predefined categories (in this case, mild, moderate, or severe). Microsoft Learn clearly distinguishes between clustering (discovering hidden patterns) and classification (predicting predefined categories).

Yes, No, Yes.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Identify capabilities of Azure Cognitive Services for Language”, the Azure Translator service is a cloud-based machine translation service used to translate text or entire documents between languages in real time. It uses REST APIs or client libraries to translate text input, detect languages, and support multiple target languages in a single request.

“The following service call will accept English text as an input and output Italian and French text: /translate?from=en & to=it,fr – Yes.This URL format is correct because the Translator service API allows multiple target languages to be specified in a single to parameter separated by commas. In this case, from=en defines the source language (English), and to=it,fr requests translations into Italian (it) and French (fr). The API would return results in both target languages simultaneously. This syntax is officially documented in Microsoft Learn as the valid format for multi-language translation.

“The following service call will accept English text as an input and output Italian and French text: /translate?from=en & to=fr & to=it – No.This format is incorrect, as the Translator API does not support repeating the to parameter multiple times. Only one to parameter is valid, and multiple target languages must be provided as a comma-separated list within the same to parameter.

“The Translator service can be used to translate documents from English to French.” – Yes.This statement is true. The Translator service supports both text translation and document translation. The document translation capability allows the translation of whole files such as Word, PowerPoint, or PDF documents while preserving formatting and structure. This feature is included in the official Translator API under “Document Translation.”

In summary, the AI-900 study content clarifies that:

✅ /translate?from=en & to=it,fr → Valid syntax

❌ /translate?from=en & to=fr & to=it → Invalid syntax

✅ Translator can translate full documents between languages

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.

Within Azure AI Document Intelligence (formerly Form Recognizer), the Contract prebuilt model is designed to extract key information from legal and business contracts, including parties, jurisdictions, dates, and terms. According to Microsoft Learn, this prebuilt model identifies structured entities such as contracting parties, effective dates, governing jurisdictions, and termination clauses.

Layout (B) extracts text, tables, and structure but does not identify semantic information such as parties or jurisdictions.

General document (C) extracts key-value pairs and entities but lacks domain-specific contract analysis.

Read (D) performs OCR (optical character recognition) to extract raw text but not contextual metadata.

Thus, when the requirement is to extract parties and jurisdictions from a legal document, the Contract model is the correct Azure AI Document Intelligence choice.

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”, when building a predictive machine learning model, features are the input variables used by the algorithm to predict the target label. The target label is the output or value the model is trained to predict.

In this dataset, the target variable is clearly the Income Range, since the goal is to predict a customer’s income bracket. Therefore, Income Range (D) is the label, not a feature. Features must be other attributes that help the model make this prediction.

The fields Education Level (A) and Age (C) are the most relevant features because both can logically and statistically influence income level.

Education Level is a categorical variable that often correlates strongly with income. Individuals with higher education levels tend to earn more on average, making this an important predictor.

Age is a numerical variable that typically affects income level due to factors such as experience and career progression.

By contrast:

First Name (E) and Last Name (B) are irrelevant as features because they are identifiers, not meaningful predictors of income. Including them could lead to bias or model overfitting without contributing to accurate predictions.

Hence, according to AI-900 principles, the features used to train a model predicting income range would be Education Level and Age.