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Microsoft AI-900 certification exam, also known as the Microsoft Azure AI Fundamentals exam, is a fundamental-level exam that measures a candidate's knowledge and understanding of essential concepts related to artificial intelligence (AI) and machine learning (ML) on the Azure platform. Microsoft Azure AI Fundamentals certification exam is intended for individuals who wish to demonstrate their knowledge of AI and ML concepts and their practical applications using the Azure platform. AI-900 Exam is designed to validate that the candidate has the foundational knowledge required to use Azure AI services to build intelligent solutions.
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Here, the following skills will be measured:
NEW QUESTION # 291
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
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.
NEW QUESTION # 292
Match the facial recognition tasks to the appropriate questions.
To answer, drag the appropriate task from the column on the left to its question on the right. Each task may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
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.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/face/#features
NEW QUESTION # 293
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Azure Machine Learning documentation, Automated Machine Learning (AutoML) is a feature designed to help users build, train, and tune machine learning models automatically without requiring deep knowledge of programming or data science.
* First Statement: "Automated machine learning provides you with the ability to include custom Python scripts in a training pipeline."This is False (No). AutoML automates the model selection and tuning process but does not allow the inclusion of custom Python scripts within its workflow. Custom Python integration is supported in Azure Machine Learning designer pipelines or SDK-based training, not in AutoML.
* Second Statement: "Automated machine learning implements machine learning solutions without the need for programming experience."This is True (Yes). One of AutoML's core benefits is that it enables non-programmers to train and evaluate models by simply selecting data, choosing a target column, and letting Azure automatically test algorithms and hyperparameters. This aligns with Microsoft's AI-900 objective to democratize AI development.
* Third Statement: "Automated machine learning provides you with the ability to visually connect datasets and modules on an interactive canvas."This is False (No). That feature belongs to Azure Machine Learning Designer, not AutoML. The designer offers a drag-and-drop visual interface for connecting datasets and modules, whereas AutoML provides a wizard-driven approach focused on automation.
NEW QUESTION # 294
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
NEW QUESTION # 295
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
In Microsoft's Responsible AI framework, the Reliability and Safety principle ensures that AI systems perform consistently, safely, and as intended across diverse conditions - even when faced with incomplete, unusual, or unexpected data. Correctly handling unusual or missing values in a dataset directly demonstrates this principle, as it helps prevent faulty predictions, biased results, or unsafe system behaviors.
According to the Microsoft Learn Responsible AI module (from the AI-900 and AI-102 study paths), a reliable AI model should maintain its performance when encountering data anomalies. This includes validating inputs, managing missing or extreme values, and testing models to ensure they behave as expected in real-world scenarios. Such practices make AI systems robust and trustworthy, which aligns exactly with the Reliability and Safety principle.
The other Responsible AI principles address different concerns:
* Inclusiveness: Ensures AI empowers and serves all users equitably.
* Privacy and Security: Focuses on safeguarding personal data and preventing unauthorized access.
* Transparency: Ensures that AI decisions are understandable and explainable to users.
While all principles are essential, managing data integrity and system stability-including how a model responds to missing or anomalous values-is primarily a matter of reliability and safety. It ensures the AI behaves predictably and minimizes risks of errors or unintended harm.
Therefore, the correct completion of the sentence is:
"Correctly handling unusual or missing values is an example of the application of the Reliability and Safety principle for Responsible AI."
NEW QUESTION # 296
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