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 781
Which of the following scenarios can be resolved by applying classification models?
A. A bank wanting to determine if a specific set of clients are eligible for taking a loan.
B. A company who wants to predict the churn rate of their subscribers for next month.
C. A toy company wanting to determine which clients are inclined to buy a specific toy.
Answer
A. A bank wanting to determine if a specific set of clients are eligible for taking a loan.
C. A toy company wanting to determine which clients are inclined to buy a specific toy.
Explanation
Classification is a form of machine learning that is used to predict which category, or class, an item belongs to.
Question 782
Which of the following are models that help predict between two or several categories?
Select all that apply.
A. Multi-class neural network
B. Linear Regression
C. Two-class logistic regression
D. Two-class decision forest
Answer
A. Multi-class neural network
C. Two-class logistic regression
D. Two-class decision forest
Explanation
Two-class decision forests and Two-class logistic regressions help predict between two categories, while Multi-class neural networks help predict between several categories.
Question 783
Classification is an example of a supervised machine learning technique in which you train a model using data that includes both the features and known values for the label, so that the model learns to fit the feature combinations to the label. True or False?
A. True
B. False
Answer
A. True
Explanation
Classification is an example of a supervised machine learning technique in which you train a model using data that includes both the features and known values for the label, so that the model learns to fit the feature combinations to the label.
Question 784
You are using Azure Machine Learning designer to create a training pipeline for a binary classification model.At some point, you want to separate the data into training and testing sets. Which model should you add to the pipeline?
A. Join data
B. Split data
C. Select columns in dataset
Answer
B. Split data
Question 785
What features and capabilities are available in Azure Machine Learning?
Select all that apply.
A. Publish predictive services
B. Prepare data
C. Monitor usage of used services
Answer
A. Publish predictive services
B. Prepare data
C. Monitor usage of used services
Explanation
Azure Machine Learning is a cloud-based service with a wide range of features and capabilities that help data scientists to prepare data, train models, publish predictive services, and monitor their usage.
Question 786
After creating and running a pipeline to train the model, you need a second pipeline that performs the same data transformations for new data, and then uses the trained model to predict label values based on its features. True or False?
A. True
B. False
Answer
A. True
Explanation
An inference pipeline will form the basis for a predictive service that you can publish for applications to use.
Question 787
What type of compute resources can be created in Azure Machine Learning Studio?
A. Spot clusters
B. Compute clusters
C. Inference clusters
D. Compute instances
Answer
B. Compute clusters
C. Inference clusters
D. Compute instances
Explanation
The four types of compute resources available in Azure Machine Learning Studio are: Compute instances, Compute Clusters, Inference clusters and Attached Compute.
Question 788
You are creating a training pipeline for a regression model and you want to make sure that the dataset is complete, otherwise you need to perform various operations to fix the data. Which module should you add to the pipeline?
A. Select columns in a dataset
B. Clean missing data
C. Normalize data
Answer
B. Clean missing data
Explanation
Clean missing data helps to check data for missing values and then perform various operations to fix the data or insert new values. The goal of such cleaning operations is to prevent problems caused by missing data that can arise when training a model.
Question 789
You are creating a training pipeline for a regression model and your dataset contains hundreds of columns. For a particular part of your model, you want to use data only from some specific columns. Which module should you add to the pipeline?
A. Normalize data
B. Select columns in a dataset
C. Clean missing data
Answer
B. Select columns in a dataset
Explanation
This module is used to choose a subset of columns to use in downstream operations.
Question 790
Which of the following scenarios can be resolved by using a regression model?
A. Predict selling price of a car using data like engine size, mileage, number of seats etc.
B. Predict daily rental demand of bicycles by using historic data.
C. Predict yearly income of customers based on their occupation, age, education etc.
D. Determine if patients with some pre-existing conditions are more likely to suffer from diabetes.
Answer
A. Predict selling price of a car using data like engine size, mileage, number of seats etc.
B. Predict daily rental demand of bicycles by using historic data.
C. Predict yearly income of customers based on their occupation, age, education etc.
Explanation
Regression is a form of machine learning that is used to predict a numeric label based on an item’s features