Try Microsoft : AI-900日本語 valid & accurate questions and answers

Correct Answer:

Explanation:

In Azure Machine Learning Designer, the Dataset output visualization feature is specifically used to explore and understand the distribution of values in potential feature columns before model training begins. This capability is critical for data exploration and preprocessing, two essential stages of the machine learning pipeline described in the Microsoft Azure AI Fundamentals (AI-900) and Azure Machine Learning learning paths.
When a dataset is imported into Azure Machine Learning Designer, users can right-click on the dataset output port and select "Visualize". This launches the dataset visualization pane, which provides detailed statistical summaries for each column, including:
* Data type (numeric, categorical, string, Boolean)
* Minimum, maximum, mean, and standard deviation values for numeric columns
* Frequency counts and distinct values for categorical columns
* Missing value counts
This visual inspection helps determine which columns should be used as features, which might need normalization or encoding, and which contain missing or irrelevant data. It is a vital step in ensuring the dataset is clean and ready for model training.
Let's examine why other options are incorrect:
* Normalize Data module is used to scale numeric data, not to visualize distributions.
* Select Columns in Dataset module is used to include or exclude columns, not to analyze them.
* Evaluation results visualization feature is used after model training to interpret performance metrics like accuracy or recall, not data distributions.
Therefore, based on official Microsoft documentation and AI-900 study materials, to explore the distribution of values in potential feature columns, you use the Dataset output visualization feature in Azure Machine Learning Designer.

Correct Answer:

Explanation:
Statement
Yes / No
Providing an explanation of the outcome of a credit loan application is an example of the Microsoft transparency principle for responsible AI.
Yes
A triage bot that prioritizes insurance claims based on injuries is an example of the Microsoft reliability and safety principle for responsible AI.
Yes
An AI solution that is offered at different prices for different sales territories is an example of the Microsoft inclusiveness principle for responsible AI.
No
This question is based on the Responsible AI principles defined by Microsoft, a major topic in the AI-900:
Microsoft Azure AI Fundamentals certification. The goal of Responsible AI is to ensure that artificial intelligence is developed and used ethically, safely, and transparently to benefit people and society. Microsoft' s framework defines six core principles: Fairness, Reliability and Safety, Privacy and Security, Inclusiveness, Transparency, and Accountability.
* Transparency Principle - YesProviding an explanation for a loan application decision clearly reflects transparency. According to Microsoft's Responsible AI guidelines, transparency involves ensuring that users and stakeholders understand how AI systems make decisions. When a financial AI model explains why a loan was approved or denied, it promotes user trust and confidence in automated decision- making. Transparency helps individuals understand influencing factors (like income or credit score), thereby fostering ethical AI deployment.
* Reliability and Safety Principle - YesA triage bot that prioritizes insurance claims based on injury severity demonstrates reliability and safety. This principle ensures that AI systems consistently operate as intended, handle data accurately, and do not cause unintended harm. For a triage bot, safety means it must correctly interpret medical or claim information and consistently provide appropriate prioritization. Microsoft emphasizes that reliable AI systems must be tested rigorously, function correctly in various scenarios, and maintain user safety at all times.
* Inclusiveness Principle - NoAn AI solution priced differently for various sales territories is unrelated to inclusiveness. Inclusiveness focuses on designing AI systems that are accessible and fair to all users, including those with disabilities or from different demographic backgrounds. Price variation across territories is a business strategy, not an ethical AI inclusion concern. Hence, this statement does not align with any Responsible AI principle.

Correct Answer: A,D  🗳️

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Correct Answer: B  🗳️

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Correct Answer:

Explanation:
Features
The correct completion of the sentence is:
"In a machine learning model, the data that is used as inputs are called features." According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Identify features of common machine learning types," the term feature refers to an individual measurable property or characteristic of the data that is used by a machine learning model to make predictions or decisions.
In supervised and unsupervised learning, features serve as the inputs to the model. They are the variables that represent the information the algorithm learns from. For example, if a model predicts the price of a house, the features might include square footage, number of bedrooms, location, and age of the property. These features feed into the model so that it can learn the relationship between inputs and outputs.
Microsoft Learn further defines these key concepts:
* Features: Input variables (independent variables) used by the model to learn patterns.
* Labels: The desired output or target variable that the model is trained to predict (e.g., price, category).
* Instances: Individual rows or data records within the dataset (each instance has multiple features).
* Functions: Algorithms or mathematical operations used during training and prediction - not data inputs.
Therefore, among the provided options - features, functions, labels, instances - only features accurately describe the data elements used as inputs for training or inference in a machine learning model.
In summary, within the AI-900 learning context:
* Features = inputs to the model.
* Labels = outputs for supervised learning.
* Instances = examples (rows) of data.