FREE Microsoft Azure AI Fundamentals General Questions and Answers

Question 1

You're employed by a car dealership. Your employer requests that you give him forecast data. Will the new auto model succeed or fail? The new model sports a sunroof, improved seats, and many engine upgrades. You created a list of information about previous successful models, including details about their features and sales figures. What should you do to ensure the success of the new model during the pre-processing of the data stage?

Accepted Answer

Feature selection

Answer

To predict the success of a new car model, it's crucial to identify which characteristics (features like sunroof, seats, engine upgrades) from historical data are most influential. Feature selection is the process of choosing the most relevant features that contribute significantly to the prediction outcome. By focusing on these key features during data preprocessing, the model's accuracy and efficiency in forecasting success can be greatly improved.

Question 2

An extensive dataset of brain scan images that have been anonymised and classified into different forms of brain hemorrhages is being used in a medical research study. Before the photos are analyzed by a person, machine learning must be used to help early detection of the many forms of brain hemorrhages in the images. Which kind of machine learning does this represent?

Accepted Answer

classification

Answer

The scenario involves using a dataset of brain scan images that are already 'classified into different forms of brain hemorrhages' to help 'early detection' in new images. This means the machine learning model needs to learn from labeled examples to assign new, unseen images to one of these predefined categories (types of hemorrhages). This task of assigning data points to discrete, predefined classes is known as classification.

Question 3

Why should you randomly divide the rows into several subsets when training a model?

Accepted Answer

to test the model by using data that was not used to train the model

Answer

Randomly dividing data into separate training and testing (or validation) sets is a fundamental practice in machine learning. The training set is used to build and optimize the model, while the test set, which the model has never encountered, is used to evaluate its real-world performance and generalization ability. This process helps ensure the model can accurately predict on new data and prevents overfitting, where a model performs well only on the data it was trained on.

Question 4

For the following ten years, you must forecast the sea level in meters. Which kind of machine learning ought to be applied?

Accepted Answer

regression

Answer

Forecasting the sea level in meters involves predicting a continuous numerical value over time. Regression is the specific type of machine learning task designed for predicting continuous outcomes. Classification, in contrast, predicts discrete categories, while clustering groups similar data points without predefined labels. Therefore, regression is the appropriate machine learning approach for this prediction problem.

Question 5

What popular ML algorithm do you use when building a Clustering Model?

Accepted Answer

K-means

Answer

K-means is one of the most popular and widely used unsupervised machine learning algorithms for clustering. It works by partitioning 'n' data points into 'k' clusters, where each data point belongs to the cluster with the nearest mean (centroid). The other options, Linear Regression, Two-Class Neural Network, and Multiclass Logistic Regression, are supervised learning algorithms used for regression or classification tasks.

Microsoft Azure AI Fundamentals Practice Test

Microsoft Azure AI Fundamentals Practice Test

FREE Microsoft Azure AI Fundamentals General Questions and Answers