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 611
You are building your AI solution within the framework of governance and organizational requirements that reflect defined legal and ethical standards.
What responsible AI principle are you following?
Answer
Accountability
Explanation
Microsoft recognizes six principles of responsible AI: Fairness, Reliability and safety, Privacy and security, Transparency, Inclusiveness and Accountability.
The principle of Accountability directs AI solutions to follow governance and organizational norms.
Question 612
Your company created a new mobile phone. You need to define a price range (0 – low cost to 3 – very high cost) for the phone. You collected technical and sales data for the phones on the market. Now you are ready to train your model. Here is your train dataset:
What type of model will you train?
Answer
Classification Model
Explanation
We are training the Classification model. In our case, we are using the historical data and predicting the price range category that a new phone belongs to.
The “Price range” column is our target or label, and it has four classes: 0 (low cost), 1(medium cost), 2 (high cost), and 3 (very high cost). The model output value will be one of these four classes.
The Regression model is wrong, the Regression model uses historical data for model training. But it predicts the output numeric value, not the class or classes.
The Clustering model is wrong, the Clustering model clusters unlabeled data into groups based on some common properties.
An unsupervised model uses unlabeled data.
Question 613
You want to build a personal virtual assistant. What service will you use to connect your assistant with various input channels and devices?
Answer
Azure Bot Services
Question 614
What are the four typical steps of data transformation for model training?
Answer
Impute missing values -> Normalize numeric values, Finding and removing data outliers, Feature selection
Explanation
After we ingest the data, we need to do a data preparation or transformation before supplying it for model training. There are four typical steps for data transformation such as Feature selection, Finding and removing data outliers, Impute missing values, and Normalize numeric features.
Split data is coming after data transformation.
ML algorithm selection data is coming after data transformation and Split Data steps.
Question 615
You are working for a car dealership. Your boss asks you to provide him information about how many blue cars he needs to order for the next quarter.
You decide to create an ML model and choose an unsupervised machine learning approach.
Will this help you to achieve your goal?
Answer
No
Explanation
Your task is to provide a numeric prediction. You can achieve this by creating a regression model based on the historical sales data of the blue cars from previous quarters. Regression and Classification modeling types are two parts of Supervised machine learning. Only Clustering belongs to Unsupervised machine learning. If you choose the Unsupervised machine learning approach, you will not achieve your goal.
Question 616
You need to create a new pipeline to train a regression model using Azure ML Designer. You ingest your data for the model and drop it on the canvas.
What module would you typically drag-and-drop next on the canvas?
Answer
Select columns in the dataset
Explanation
After we bring data for model training or ingest data, the next stage is the Data transformation. Data transformation or data pre-processing usually includes the following steps: feature selection, data cleaning, and data normalization.
In Azure ML Designer, we need to drag-and-drop the “Select Columns in Dataset” module from the Data Transformation section. Then on the right-side panel, we can select all the features we want to use for the model training.
Question 617
You created an AI solution that qualifies customers for a bank loan. The solution provides different results for the people living in cities and rural areas.
What responsible AI principle does your solution violate?
Answer
Fairness
Explanation
The principle of Fairness directs AI solutions to treat everybody fairly, independently from gender, race, or any bias.
Question 618
What is the name of the common AI service that provides 24/7 monitoring of the customer’s time-series data for possible data irregularities?
Answer
Anomaly Detection
Explanation
Customers are using Anomaly detection APIs for constant monitoring of their time-series data. An anomaly detection service ingests the data and automatically selects the best ML model for the identification of the possible data irregularities. The service alerts the customers as soon as such anomalies arise.
Anomaly Detection is one of the five key elements of Microsoft Artificial Intelligence. The other four are Machine Learning, Computer Vision, Natural Language Processing, and Conversational AI.
Machine Learning, Computer Vision, Natural Language Processing, and Conversational AI, along with Anomaly detection, are the key elements of Artificial Intelligence.
Automated Machine Learning is a feature of Machine Learning and is not part of Anomaly detection.
Question 619
You created an AI solution. Along with solution deployment, you provided information about the solution’s possibilities and limitations. By providing this information, with what principle for responsible AI did you comply?
Answer
Transparency
Explanation
Microsoft recognizes six principles of responsible AI: Fairness, Reliability and safety, Privacy and security, Transparency, Inclusiveness and Accountability.
The principle of Transparency helps people to understand how to use AI solutions, their behavior, possibilities, and limitations.
Question 620
What are two automated document processing models Form Recognizer supports?
Answer
A pre-build receipt model, Custom model
Explanation
Form Recognizer service is one of the Azure Computer vision solutions additional to Computer Vision service, Custom Vision Service and Face service.
For automated document processing, Form Recognizer uses two models: Custom Model and a pre-build receipt model.
With the Custom Model approach, you train the Form Recognizer model based on your own form and data. You just need only 5 samples of your form to start.
A pre-build receipt model is a Form Recognizer default model that is trained to work with receipts. It helps recognize receipts and extract data from them.