How Can You Crack the Microsoft AI-900 Exam with Flying Colors?

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Microsoft AI-900 (Microsoft Azure AI Fundamentals) Exam is a certification exam that validates one's foundational knowledge of AI and its applications in Azure. AI-900 exam is designed for individuals who are interested in gaining an understanding of AI and its related services in Azure, including machine learning, cognitive services, and natural language processing. AI-900 Exam covers various topics such as AI workloads and considerations, fundamental principles of machine learning, and key features of Azure AI services.

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Microsoft AI-900, also known as the Microsoft Azure AI Fundamentals exam, is a certification exam designed to test one's knowledge and understanding of the fundamentals of artificial intelligence (AI) and its application on the Microsoft Azure platform. Microsoft Azure AI Fundamentals certification exam is ideal for individuals who are interested in pursuing a career in AI or who want to demonstrate their expertise in AI to potential employers.

Microsoft Azure AI Fundamentals Sample Questions (Q119-Q124):

NEW QUESTION # 119
Match the machine learning tasks to the appropriate scenarios.
To answer, drag the appropriate task from the column on the left to its scenario 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: 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.
Reference:
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

NEW QUESTION # 120
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:

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

NEW QUESTION # 121
What are two tasks that can be performed by using the Computer Vision service? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. Train a custom image classification model.
B. Recognize handwritten text.
C. Detect faces in an image.
D. Translate the text in an image between languages.

Answer: B,C

Explanation:
Explanation
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.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/home

NEW QUESTION # 122
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:

Explanation

Reliability and safety: To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.
Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles AI systems should perform reliably and safely. For example, consider an AI-based software system for an autonomous vehicle; or a machine learning model that diagnoses patient symptoms and recommends prescriptions. Unreliability in these kinds of system can result in substantial risk to human life.
https://docs.microsoft.com/en-us/learn/modules/get-started-ai-fundamentals/7-understand-responsible-ai