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AI-900 Microsoft Azure AI Fundamentals Exam Questions and Answers – Page 7 Part 1

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.

Question 601

You created a Custom Vision model. You want your model to detect trained objects on the photos. What information will you get about each object if you are using an object detection model?

You created a Custom Vision model. You want your model to detect trained objects on the photos.What information will you get about each object if you are using an object detection model?

Answer: Probability score, Bounding box, Class name

Explanation

Object detection is the form of ML that helps to recognize objects on the images. Each recognizable object will be put in the bounding box with the class name and probability score. Here is the Microsoft information about the object detection model:

Question 602

What are the four types of Compute resources you can use in Azure Machine Learning Studio?

Answer: Attached compute, Compute Instances, Compute Clusters, Inference Clusters

Explanation

When you open Compute blade in Microsoft Azure Machine Learning Studio, you can see all four compute resources:

When you open Compute blade in Microsoft Azure Machine Learning Studio, you can see all four compute resources.

Question 603

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:

You collected technical and sales data for the phones on the market. Now you are ready to train your model.

What will be the label for this model?

Answer: Price Range

Explanation

The label is a generic name for the model output value or class. In our case, we are predicting the price range category that a new phone belongs to. The “Price range” column is our label.

All other options are incorrect because these columns are inputs or features for the model.

Question 604

You are using Text Analytics Sentiment API to analyze the following sentence: “Peter was surprised and very happy to meet Sara in Paris.”

What sentiment value should you expect in the API response?

Answer: Positive

Explanation

Sentiment Analysis is a Text Analytics service that helps analyze text and returns sentiment scores (between 0 and 1) for each sentence.

A score close to 0 means a negative sentiment, and a score close to 1 means positive. In cases with a neutral or undefined sentiment, the score is 0.5.

In our case, the sentiment of the sentence is positive, with a confidence score of 1.0.

Question 605

You need to train and test your ML model. You prepare data for model training. Several of your numeric features have different scales. The first feature has a minimum value of 0.253 and a max of 0.987, the second one – from 12 to 124, and the last one – from 13545 to 56798. You need to bring them to a common scale. You decide to use feature engineering to address this problem.

Does this decision help you to achieve your goal?

Answer: No

Explanation

You need to normalize your numeric features. The process of normalization brings numeric features to a common scale.

Feature engineering is the method of creating new features based on the existing ones.

Question 606

You implement an aerial image processing application to identify the flooded areas.
What common Computer Vision task is this application using?

Answer: Semantic Segmentation

Explanation

When the application processes images, it uses Semantic segmentation to classify pixels that belong to the particular object (in our case, flooded areas) and highlights them.

Object detection is incorrect because Object detection helps to identify objects and their boundaries within the image.

Image classification is incorrect because Image classification helps to classify images based on their content.

Face detection is incorrect because Face detection is a Computer vision technique that helps detect and recognize people’s faces.

Image Analysis is incorrect because Image Analysis helps extract information from the images, tag them, and create a descriptive image summary.

Question 607

The customer service of your company spends a lot of time answering the same questions. They asked you to help them to automate this process. They provided you with a Microsoft Excel (*.xlsx) document with frequently asked questions and typical answers. What service will you use to create a knowledge base from this document?

Answer: QnA Maker

Explanation

You need to use the QnA Maker service. First, you need to provision the QnA Maker resource in your Azure subscription. After that, you can populate the newly created knowledge base using a frequently asked questions (FAQ) document.

Azure Bot Service facilitates access to the knowledge base, but this service doesn’t create a knowledge base.

Custom vision service helps create your computer vision model, but this service doesn’t create a knowledge base.

Text Analytics helps analyze text documents, detect documents’ language, extract key phrases, determine entities, and provide sentiment analysis. This service doesn’t create a knowledge base.

Language Understanding Intelligent Service (LUIS) helps understand voice or text commands. This service doesn’t create a knowledge base.

Question 608

You are working for a car dealership. Your boss asks you to provide him forecast information: will the new car model be successful or not. The new model has a variety of engine improvements, more comfortable seats, and a sunroof. You compiled the list of data about previous successful models with their characteristics and sales numbers.

What should you do in the pre-processing data stage that would help you to predict the success of the new model?

Answer: Feature Selection

Explanation

During pre-processing, you need to work with data to select features that influence the label prediction. In this problem, features are the engine characteristics (power or volume), seat comforts, etc. They could help the ML model to predict the success of the new car model. Maybe the sunroof is not essential for predicting the label, and we need to discard this feature from the final set of features that we will use for model training.

In short, Feature selection helps us to narrow down the features that are important for our label prediction and discard all features that don’t play or play a minimal role in a label prediction. As a result, our trained model and prediction will be more efficient.

All other options are incorrect because they are parts of the different data processing events that are irrelevant to the pre-processing (Training set selection or Data for model evaluation selection) or too generic (Data selection or Data Classification).

Question 609

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:

You collected technical and sales data for the phones on the market. Now you are ready to train your model.

What column will you discard from the final dataset during feature selection?

Answer: Color

Explanation

Data pre-processing involves various techniques, like feature selection, normalization or feature engineering, etc.

During feature selection, we identify features that would help us with label prediction. And we discard the rest. In our dataset, the Color feature wouldn’t correlate with the label due to the constant value of “black.” We can safely remove this feature from the final dataset.

All other options should be included in the training dataset.

Question 610

What tasks does Computer Vision Cognitive service include?
Select all that apply.

Answer: Detects Objects, Read the text in the image, Categorize image, Identifies Landmarks

Explanation

Computer Vision service is one of the main areas of Artificial Intelligence. It belongs to the group of Azure Computer vision solutions such as Computer Vision service, Custom Vision Service, Face service, and Form Recognizer.

Computer Vision service works with images. This service brings sense to the image pixels by using them as features for ML models. These predefined models help categorize and classify images, detect and recognize objects, tag, and identify them. Computer Vision can “read” a text in images in 25 languages and recognize landmarks.

Natural Language Processing is incorrect because the Translator Text is a Natural Language Processing service and is not a Computer Vision service.

Process Forms is incorrect because the Process Forms service is part of Azure Computer vision solutions and is not a Computer Vision service.

Find Similar Faces because the Find Similar Faces is the technique that is part of Face service and is not a Computer Vision service.

Alex Lim is a certified IT Technical Support Architect with over 15 years of experience in designing, implementing, and troubleshooting complex IT systems and networks. He has worked for leading IT companies, such as Microsoft, IBM, and Cisco, providing technical support and solutions to clients across various industries and sectors. Alex has a bachelor’s degree in computer science from the National University of Singapore and a master’s degree in information security from the Massachusetts Institute of Technology. He is also the author of several best-selling books on IT technical support, such as The IT Technical Support Handbook and Troubleshooting IT Systems and Networks. Alex lives in Bandar, Johore, Malaysia with his wife and two chilrdren. You can reach him at [email protected] or follow him on Website | Twitter | Facebook

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