The latest Microsoft AI-900 Azure AI Fundamentals certification actual real practice exam question and answer (Q&A) dumps are available free, which are helpful for you to pass the Microsoft AI-900 Azure AI Fundamentals exam and earn Microsoft AI-900 Azure AI Fundamentals certification.
Table of Contents
Question 481
Ensuring users are aware of limitations of AI-based application they are using is an example of which Responsible AI principle?
Answer
Transperency
Question 482
What are some of the key elements of AI?
Answer
Machine Learning
Question 483
You work for an insurance company. You have been asked to develop an application that places clients into High, Medium, or Low risk categories. Which machine learning model should you use?
Answer
Clustering
Question 484
You wish to use Azure Machine Learning in Dev & Test environment. ACI deployment offers the right solution.
Answer
True
Question 485
If we want to build an app that identifies celebrities in images, which Cognitive Services API would we utilize?
Answer
Computer Vision
Question 486
The ability to classify individual pixels in an image according to the object to which they belong is known as:
Answer
Semantic Segmentation
Question 487
You have the following dataset:
You plan to use the dataset to train a model that will predict the house price categories of houses.
You plan to use the dataset to train a model that will predict the house price categories of houses.
What are Household Income and House Price Category? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Household Income:
- A feature
- A label
House Price Category:
- A feature
- A label
Answer
Household Income: A feature
House Price Category: A label
Question 488
Match the types of machine learning to the appropriate scenarios.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Select and Place:
Learning Type:
- Classification
- Clustering
- Regression
Answer Ares:
- Predict how many minutes late a flight will arrive based on the amount of snowfall at an airport.
- Segment customers into different groups to support a marketing department.
- Predict whether a student will complete a university course.
Answer
Regression: Predict how many minutes late a flight will arrive based on the amount of snowfall at an airport.
Classification: Segment customers into different groups to support a marketing department.
Clustering: Predict whether a student will complete a university course.
Explanation
- Regression: In the most basic sense, regression refers to prediction of a numeric target. Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable. You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
- Classification: Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.
- Clustering: Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation. Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.
Question 489
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.
Select and Place:
Learning Type:
- Feature engineering
- Feature selection
- Model deployment
- Model evaluation
- Model training
Answer Ares:
- Examining the values of a confusion matrix
- Splitting a date into month, day, and year fields
- Picking temperature and pressure to train a weather model
Answer
- Model evaluation: Examining the values of a confusion matrix
- Feature engineering: Splitting a date into month, day, and year fields
- Feature selection: Picking temperature and pressure to train a weather model
Explanation
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.
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.
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.
Question 490
Data values that influence the prediction of a model are called ____.
A. Dependant variables.
B. Features.
C. Identifiers.
D. Labels.
Answer
D. Labels.
Explanation
In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict.
In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.