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 771
Which of the following is a clustering algorithm?
A. Two-Class Logistic Regression
B. Two-Class Neural Network
C. K-Means
Answer
C. K-Means
Explanation
K-Means is a clustering algorithm.
Question 772
What is the purpose of a clustering model?
A. Answers simple two-choice questions
B. Separates similar data points into intuitive groups
C. Makes forecasts by estimating the relationship between values
Answer
B. Separates similar data points into intuitive groups
Explanation
Clustering models have the purpose of separating similar data points into intuitive groups.
Question 773
Which of the following scenarios can be resolved by applying clustering modules/algorithms?
Select all that apply.
A. A bike rental company that wants to predict the number of customers for the next day so that it will assure the necessary staff and cycles.
B. A radio company that wants to apply tags (like rock, pop, R&B etc) to songs or artists.
C. A social media company that wants to group similar users based on their posts.
Answer
B. A radio company that wants to apply tags (like rock, pop, R&B etc) to songs or artists.
C. A social media company that wants to group similar users based on their posts.
Explanation
Clustering models have the purpose of separating similar data points into intuitive groups.
Question 774
When evaluating a clustering model, what metrics can you visualize in the Evaluate results section?
Select all that apply.
A. Maximal distance to cluster center
B. Average distance to cluster center
C. Number of points
Answer
A. Maximal distance to cluster center
B. Average distance to cluster center
C. Number of points
Explanation
The metrics that can be visualized in the Evaluate results section of a clustering module are: Average distance to other center, Average distance to cluster center, Number of points, Maximal distance to cluster center.
Question 775
You are building an Azure Machine learning pipeline that involves a clustering module. You need to prepare the data and change some of the numeric values from the dataset to use a common scale, without distorting differences in the ranges of values or losing information.
Which module should you apply?
A. Edit metadata
B. Normalize Data
C. Split data
Answer
B. Normalize Data
Explanation
The goal of normalization is to change the values of numeric columns in the dataset to use a common scale, without distorting differences in the ranges of values or losing information.
Question 776
Clustering is an example of supervised machine learning, in which you train a model to separate items into clusters based purely on their characteristics or features. True or False?
A. True
B. False
Answer
B. False
Explanation
Clustering is an example of unsupervised machine learning, in which you train a model to separate items into clusters based purely on their characteristics or features.
Question 777
A Hospital Care chain wants to open a series of Emergency-Care wards within a region. The chain knows the location of all the maximum accident-prone areas in the region. They have to decide the number of the Emergency Units to be opened and the location of these Emergency Units, so that all the accident-prone areas are covered in the vicinity of these Emergency Units.
Which type of machine learning model is best to be applied in this scenario?
A. Clustering
B. Regression
C. Classification
Answer
A. Clustering
Question 778
Which metric presents the ratio of correct predictions (true positives + true negatives) to the total number of predictions?
A. Recall
B. F1 Score
C. Precision
D. Accuracy
Answer
D. Accuracy
Explanation
Accuracy presents the ratio of correct predictions (true positives + true negatives) to the total number of predictions.
Question 779
You use an Azure Machine Learning designer pipeline to train and test a binary classification model. You review the model’s performance metrics in an Evaluate Model module, and note that it has an AUC score of 0.6. What can you conclude about the model?
A. The model performs better than random guessing
B. The model predicts accurately for 40% of cases
C. The model can explain 60% of the variance between true and predicted labels.
Answer
A. The model performs better than random guessing
Explanation
The higher the score of AUC, the better the performance of the model.
Question 780
Which metric presents the fraction of positives cases correctly identified?
A. F1 Score
B. Recall
C. Accuracy
D. Precision
Answer
D. Precision
Explanation
Precision presents the fraction of positive cases correctly identified (the number of true positives divided by the number of true positives plus false positives)