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Microsoft AI-900 exam is a great starting point for individuals who want to pursue a career in artificial intelligence and machine learning. It provides a solid foundation for understanding the basic concepts and principles of these technologies and how they can be applied in real-world scenarios. Microsoft Azure AI Fundamentals certification also demonstrates to employers that you have the knowledge and skills necessary to work in this field.

Microsoft AI-900 or Microsoft Azure AI Fundamentals Exam is a certification exam that focuses on the fundamentals of Artificial Intelligence (AI) and its applications in Microsoft Azure. AI-900 exam is designed for individuals who want to explore the basics of AI and understand how it can be integrated into various applications. AI-900 exam covers topics such as machine learning, cognitive services, natural language processing, and computer vision.

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Target Audience

The Microsoft AI-900 Exam is designed for those individuals who have little to no experience in the world of IT. It is aimed at the students with both non-technical and technical backgrounds. They have basic programming experience and knowledge. However, they are not required to have software engineering or data science experience.

Microsoft Azure AI Fundamentals Sample Questions (Q262-Q267):

NEW QUESTION # 262
To complete the sentence, select the appropriate option in the answer area.
Using Recency, Frequency, and Monetary (RFM) values to identify segments of a customer base is an example of___________

Answer:

Explanation:

NEW QUESTION # 263
You have the Predicted vs. True chart shown in the following exhibit.

Which type of model is the chart used to evaluate?

A. classification
B. clustering
C. regression

Answer: C

Explanation:
What is a Predicted vs. True chart?
Predicted vs. True shows the relationship between a predicted value and its correlating true value for a regression problem. This graph can be used to measure performance of a model as the closer to the y=x line the predicted values are, the better the accuracy of a predictive model.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-m

NEW QUESTION # 264
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 # 265
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://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview/
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

NEW QUESTION # 266
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.