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Microsoft AI-900 exam is designed to evaluate an individual's understanding of AI and ML concepts, such as machine learning types, data labeling, and the creation of ML models using Azure services. AI-900 exam also covers the Azure AI services and their capabilities, including Cognitive Services, Azure Machine Learning, and Bot Services. The AI-900 Exam is suitable for individuals who have a basic understanding of cloud computing and programming concepts.
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Microsoft AI-900, also known as Microsoft Azure AI Fundamentals, is a certification exam that aims to test an individual's knowledge of artificial intelligence (AI) concepts and how they can be applied to Microsoft Azure. AI-900 Exam covers a range of topics, including machine learning, natural language processing, computer vision, and cognitive services. Microsoft Azure AI Fundamentals certification is designed for professionals who are looking to understand the basics of AI and how it can be used to solve business problems.
NEW QUESTION # 28
Match the types of computer vision to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
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.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/face/
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
NEW QUESTION # 29
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features and uses of speech capabilities", speech recognition refers to the process of converting spoken words into written text. When a speaker's voice is transcribed into subtitles during a presentation, the system listens to the audio input, identifies the spoken words, and generates corresponding text in real time. This is precisely what speech recognition technology accomplishes.
Azure provides this functionality through the Azure Speech Service, which supports multiple speech-related features:
* Speech-to-Text (Speech Recognition) - Converts spoken audio into text.
* Text-to-Speech (Speech Synthesis) - Converts written text into spoken audio.
* Speech Translation - Translates spoken words into another language.
In this case, the session is transcribed into subtitles in the same language, not translated or spoken aloud, so the correct feature is Speech Recognition.
Let's review the other options:
* Sentiment Analysis: This belongs to the Text Analytics service under natural language processing (NLP) and is used to determine the emotional tone of text, not to convert speech to text.
* Speech Synthesis: Converts text into audible speech (Text-to-Speech), the reverse of what is happening in this scenario.
* Translation: Converts spoken or written words from one language to another. Here, no translation is mentioned-only transcription.
Therefore, the described process-turning live spoken language into readable subtitles-is an example of Speech Recognition, a speech-to-text AI capability provided by Azure Cognitive Services.
Final answer: Speech recognition
Reference:Microsoft Learn - Identify speech capabilities of Azure AI services (AI-900 Learning Path)
NEW QUESTION # 30
Which format should you use to send requests to a REST API endpoint for Azure OpenAI?
Answer: B
Explanation:
When interacting with the Azure OpenAI REST API, requests and responses are structured using the JSON format. According to the Microsoft Learn documentation on the Azure OpenAI REST API, JSON is the required format for sending prompts, configuration parameters (such as temperature, max_tokens, or top_p), and metadata to the API endpoint.
A typical request to an Azure OpenAI endpoint (for example, /openai/deployments/{model-name}/chat
/completions?api-version=2024-02-01) includes a JSON body similar to the following:
{
"messages": [
{"role": "system", "content": "You are an assistant."},
{"role": "user", "content": "Write a JavaScript function to add two numbers."}
],
"max_tokens": 200,
"temperature": 0.7
}
This JSON format allows for structured data exchange between the client and the server, ensuring that key- value pairs are properly parsed by the API.
* Option A (CSV): Used for storing tabular data, not suitable for API communication.
* Option C (XML): Though historically used in web services, Microsoft's modern APIs, including Azure OpenAI, rely on JSON.
* Option D (YAML): Common in configuration files but not for REST API payloads.
Therefore, per official Microsoft documentation and the AI-900 study guide, the correct and verified answer is B. JSON, as it is the standard format required when sending requests to the Azure OpenAI REST API endpoint.
NEW QUESTION # 31
You need to predict the sea level in meters for the next 10 years.
Which type of machine learning should you use?
Answer: A
Explanation:
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.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression Regression is a form of machine learning that is used to predict a numeric label based on an item's features.
https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/introduction
NEW QUESTION # 32
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The correct completion of the sentence is:
"The interactive answering of questions entered by a user as part of an application is an example of natural language processing." According to the Microsoft Azure AI Fundamentals (AI-900) official study materials, Natural Language Processing (NLP) is a branch of Artificial Intelligence that focuses on enabling computers to understand, interpret, and respond to human language in a way that is both meaningful and useful. It is one of the key AI workloads described in the "Describe features of common AI workloads" module on Microsoft Learn.
When a user types a question into an application and the system responds interactively - such as in a chatbot, Q & A system, or virtual assistant - this process requires language understanding. NLP allows the system to process the input text, determine user intent, extract relevant entities, and generate an appropriate response. This is the foundational capability behind services such as Azure Cognitive Service for Language, Language Understanding (LUIS), and QnA Maker (now integrated as Question Answering in the Language service).
Microsoft's study guide explains that NLP workloads include the following key scenarios:
* Language understanding: Determining intent and context from text or speech.
* Text analytics: Extracting meaning, key phrases, sentiment, or named entities.
* Conversational AI: Powering bots and virtual agents to interact using natural language.These systems rely on NLP models to analyze user inputs and respond accordingly.
In contrast:
* Anomaly detection identifies data irregularities.
* Computer vision analyzes images or video.
* Forecasting predicts future values based on historical data.
Therefore, based on the AI-900 official materials, the interactive answering of user questions through an application clearly falls under Natural Language Processing (NLP).
NEW QUESTION # 33
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