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Die AI-900-Zertifizierungsprüfung richtet sich hauptsächlich um Unternehmensstakeholder und technische Fachkräfte, die ein grundlegendes Verständnis der KI-Technologien und ihrer potenziellen Anwendungen in Microsoft Azure-Diensten erlangen möchten. Die Zertifizierung ist eine Einstiegsprüfung, die das Wissen des Kandidaten über KI-Konzepte und Azure-KI-Dienste, einschließlich kognitiver Dienste, maschinelles Lernen und Bot-Diensten, bestätigt. Die Prüfung deckt auch den ethischen und verantwortungsvollen Gebrauch von KI ab, einschließlich Überlegungen zur Privatsphäre, Sicherheit und Compliance.
Während andere Leute noch überall die Prüfungsunterlagen für Microsoft AI-900 suchen, üben Sie schon verschiedene Prüfungsaufgaben. Sie können im Vorbereitungsphase schon ganz vorne liegen. Wir ExamFragen bieten Ihnen Microsoft AI-900 Prüfungsunterlagen mit reichliche Ressourcen. Sie dürfen auch die ganz realistische Prüfungsumwelt der Microsoft AI-900 Prüfung damit erfahren.
Microsoft AI-900, auch bekannt als Microsoft Azure AI Fundamentals, ist eine Einstiegszertifizierungsprüfung für Personen, die sich für die Arbeit mit künstlicher Intelligenz (KI) und maschinellem Lernen (ML) interessieren. Diese Zertifizierung soll ein grundlegendes Verständnis von KI und ihren Fähigkeiten vermitteln und wie sie zur Lösung von Geschäftsproblemen eingesetzt werden kann. Die Prüfung umfasst eine Reihe von Themen, darunter KI-Workloads, grundlegende Prinzipien des maschinellen Lernens und Computer Vision.
321. Frage
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
This question tests understanding of Microsoft's six guiding principles for Responsible AI, which are:
fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. These principles, as described in the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn Responsible AI module, help ensure that AI systems are developed and used ethically and responsibly.
* Transparency - Yes:Transparency means users should understand how and why an AI system makes certain decisions. Providing an explanation of the outcome of a credit loan application clearly supports transparency because it helps customers know the reasoning behind approval or rejection. According to Microsoft Learn, transparency ensures that "AI systems are understandable by users and stakeholders," especially in sensitive applications such as finance and credit scoring. Thus, the first statement is Yes.
* Reliability and Safety - Yes:The reliability and safety principle ensures AI systems perform consistently, safely, and as intended, even in complex or high-risk environments. A triage bot that prioritizes insurance claims based on injury type aligns with this principle-it must be accurate, dependable, and safe to ensure claims are processed correctly and not influenced by errors or faulty algorithms. Microsoft teaches that AI should be "reliable under expected and unexpected conditions" to prevent harm or misjudgment. Therefore, this statement is Yes.
* Inclusiveness - No:Inclusiveness focuses on ensuring AI systems empower and benefit all users, especially those with different abilities or backgrounds. Offering an AI solution at different prices across sales territories is a business decision, not an ethical or inclusiveness principle issue. It does not relate to accessibility or equal participation of diverse users. Therefore, this final statement is No.
322. Frage
Match the tasks to the appropriate machine learning models.
To answer, drag the appropriate model from the column on the left to its scenario on the right. Each model may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) study guide, the three main types of supervised and unsupervised machine learning models-classification, clustering, and regression-are used for distinct problem types depending on the structure of the data and the prediction goal.
* Clustering is an unsupervised learning technique used when the goal is to group items with similar characteristics without predefined labels. In this scenario, "Assign categories to passengers based on demographic data" implies automatically grouping passengers based on patterns such as age, income, or travel frequency, without any prior labeling. This directly maps to clustering, which discovers hidden groupings (for example, segmenting customers into categories like business travelers or vacationers).
* Regression is a supervised learning method used to predict continuous numerical values. The scenario
"Predict the amount of consumed fuel based on flight distance" is a classic regression problem because the output (fuel consumption) is a continuous variable dependent on another continuous variable (distance). Regression models, such as linear regression, are trained to estimate numeric outputs.
* Classification is also a supervised learning approach, but it predicts discrete categories or outcomes.
The scenario "Predict whether a passenger will miss their flight based on demographic data" involves a binary decision (missed or not missed), which is typical of classification tasks. These models learn from labeled examples to assign new instances to specific categories.
In summary, Clustering groups similar passengers, Regression predicts continuous numerical outcomes, and Classification determines categorical outcomes. This alignment precisely matches the definitions in Microsoft' s AI-900 learning objectives under "Describe common machine learning types and scenarios."
323. Frage
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, named entity recognition, and language detection.
Box 1: Yes
You can detect which language the input text is written in and report a single language code for every document submitted on the request in a wide range of languages, variants, dialects, and some regional/cultural languages. The language code is paired with a score indicating the strength of the score.
Box 2: No
Box 3: Yes
Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview
324. Frage
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation
Graphical user interface, text, application, email Description automatically generated
Box 1: No
The validation dataset is different from the test dataset that is held back from the training of the model.
Box 2: Yes
A validation dataset is a sample of data that is used to give an estimate of model skill while tuning model's hyperparameters.
Box 3: No
The Test Dataset, not the validation set, used for this. The Test Dataset is a sample of data used to provide an unbiased evaluation of a final model fit on the training dataset.
Reference:
https://machinelearningmastery.com/difference-test-validation-datasets/
325. Frage
You have an Azure subscription that uses Azure OpenAI.
You need to create an original image of a rural scene to use on a website.
What should you do?
Antwort: D
Begründung:
The Azure OpenAI DALL-E model is specifically designed for generating original images from natural language prompts. If you want to create an image of a "rural scene," you can use Azure AI Foundry (formerly Azure OpenAI Studio) to deploy the DALL-E model and provide descriptive instructions such as "create an image of a peaceful rural village with trees and a sunset."
* A. GPT-3.5 Turbo # Handles text generation, not image creation.
* B. Bing search # Finds existing images, not generate original ones.
* C. GitHub Copilot # Assists with writing code, not generating images.
326. Frage
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