AI-900 Microsoft Azure AI Fundamentals Exam Questions Total Questions: 501 – 9 Mock Exams & 1 Master Cheat Sheet Â
Practice Set 1
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Question 1 of 64
1. Question
While developing an AI system you encountered a situation where the AI system should be ingested with unusual and missing values. Which Microsoft guiding principle for responsible AI you should consider?
________________ is the average of absolute differences between prediction and actual observation where all individual differences have equal weight.
Select the appropriate regression performance metrics from below to complete the sentence.
You have the predicted versus true chart shown in the following exhibit:
Which type of model is the chart used to evaluate?
Correct
Predicted vs. True chart
What is a Predicted vs. True chart?
Predicted vs. True shows the relationship between a predicted value and its correlating true value for a regression problem. This graph can be used to measure performance of a model as the closer to the y=x line the predicted values are, the better the accuracy of a predictive model.
What does automated ML do with the Predicted vs. True chart?
After each run, you can see a predicted vs. true graph for each regression model. To protect data privacy, values are binned together and the size of each bin is shown as a bar graph on the bottom portion of the chart area. You can compare the predictive model, with the lighter shade area showing error margins, against the ideal value of where the model should be. https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml
Incorrect
Predicted vs. True chart
What is a Predicted vs. True chart?
Predicted vs. True shows the relationship between a predicted value and its correlating true value for a regression problem. This graph can be used to measure performance of a model as the closer to the y=x line the predicted values are, the better the accuracy of a predictive model.
What does automated ML do with the Predicted vs. True chart?
After each run, you can see a predicted vs. true graph for each regression model. To protect data privacy, values are binned together and the size of each bin is shown as a bar graph on the bottom portion of the chart area. You can compare the predictive model, with the lighter shade area showing error margins, against the ideal value of where the model should be. https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml
Unattempted
Predicted vs. True chart
What is a Predicted vs. True chart?
Predicted vs. True shows the relationship between a predicted value and its correlating true value for a regression problem. This graph can be used to measure performance of a model as the closer to the y=x line the predicted values are, the better the accuracy of a predictive model.
What does automated ML do with the Predicted vs. True chart?
After each run, you can see a predicted vs. true graph for each regression model. To protect data privacy, values are binned together and the size of each bin is shown as a bar graph on the bottom portion of the chart area. You can compare the predictive model, with the lighter shade area showing error margins, against the ideal value of where the model should be. https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml
Question 6 of 64
6. Question
Which of the following types of machine learning is NOT an example of supervised machine learning?
Correct
Classification and regression are supervised machine learning, clustering is unsupervised.
Incorrect
Classification and regression are supervised machine learning, clustering is unsupervised.
Unattempted
Classification and regression are supervised machine learning, clustering is unsupervised.
Question 7 of 64
7. Question
Which module in Azure Machine Learning designer will help in exporting intermediate data and working data from pipelines into cloud storage destinations?
Which of the following is/are example(s) of conversational AI workload? Choose 2 scenarios from below options.
Correct
-Interactively responding to a userÂ’s question through a website that uses a knowledge base.-conversational AI.
-An AI enabled smart device in the car that responds to questions such as “What will the distance between Goldcoast and Brisbane?” conversational AI + NLP
Incorrect
-Interactively responding to a userÂ’s question through a website that uses a knowledge base.-conversational AI.
-An AI enabled smart device in the car that responds to questions such as “What will the distance between Goldcoast and Brisbane?” conversational AI + NLP
Unattempted
-Interactively responding to a userÂ’s question through a website that uses a knowledge base.-conversational AI.
-An AI enabled smart device in the car that responds to questions such as “What will the distance between Goldcoast and Brisbane?” conversational AI + NLP
Question 9 of 64
9. Question
You are working in a digital marketing company and you have been tasked to build a customer support system. The system should meet the following requirement:
a) It should be a web-based AI solution.
b) User must be able to interact with a web app that will advise them the best solution.
As a developer which service in azure should you use?
The National Highway Traffic Safety department has asked you to build a vehicle monitoring system which will detect heavy vehicles such as trucks and buses. The AI system will be build based on the shape of the moving object and should able to classify vehicles as heavy or light.
Which type of AI workload should you use?
You plan to use the data set to train a model that will predict the battery requirement for mobile phones.
What are “screen size” and “battery requirement”?
Select 2 correct answers from below options.
Correct
For both classification and regression problems, simply consider the feature as input and label as output.
In the above problem set, based on screen size, RAM and HDD, we have to develop a model to predict the battery requirement. So, the battery requirement is the label and the screen size is the feature.
Incorrect
For both classification and regression problems, simply consider the feature as input and label as output.
In the above problem set, based on screen size, RAM and HDD, we have to develop a model to predict the battery requirement. So, the battery requirement is the label and the screen size is the feature.
Unattempted
For both classification and regression problems, simply consider the feature as input and label as output.
In the above problem set, based on screen size, RAM and HDD, we have to develop a model to predict the battery requirement. So, the battery requirement is the label and the screen size is the feature.
Question 14 of 64
14. Question
Out of the below ROC, which model is the best performing.
Correct
Higher the AUC, better the model is at distinguishing between outcomes.
Incorrect
Higher the AUC, better the model is at distinguishing between outcomes.
Unattempted
Higher the AUC, better the model is at distinguishing between outcomes.
Question 15 of 64
15. Question
Which of the following statement(s) is/are CORRECT?
Correct
The text analytics service can detect language of your text. – TRUE
Language detection
Use the language detection capability of the Text Analytics service to identify the language in which text is written. You can submit multiple documents at a time for analysis. For each document submitted to it, the service will detect:
The language name (for example “English”).
The ISO 6391 language code (for example, “en”).
A score indicating a level of confidence in the language detection. https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure
Text analytics service can classify a broad range of entities in text, such as people, places, organisations etc. TRUE
Entity recognition
You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. The service can also provide links to more information about that entity on the web. An entity is essentially an item of a particular type or a category; and in some cases, subtype, such as those as shown in the following table.
Entity recognition
Type SubType Example
Person “Bill Gates”, “John”
Location “Paris”, “New York”
Organization “Microsoft”
Text analytics service can read text in images, scanned documents. FALSE
Optical Character Recognition (OCR)
Microsoft’s Computer Vision API includes Optical Character Recognition (OCR) capabilities that extract printed or handwritten text from images and PDF documents. The OCR APIs extract text from both analogue documents (images, scanned documents) and digitized documents. You can extract text from images in the wild, such as photos of license plates or containers with serial numbers, as well as from documents – invoices, bills, financial reports, articles, and more. https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-recognizing-text https://devblogs.microsoft.com/cse/2018/05/07/handwriting-detection-and-recognition-in-scanned-documents-using-azure-ml-package-computer-vision-azure-cognitive-services-ocr/
Incorrect
The text analytics service can detect language of your text. – TRUE
Language detection
Use the language detection capability of the Text Analytics service to identify the language in which text is written. You can submit multiple documents at a time for analysis. For each document submitted to it, the service will detect:
The language name (for example “English”).
The ISO 6391 language code (for example, “en”).
A score indicating a level of confidence in the language detection. https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure
Text analytics service can classify a broad range of entities in text, such as people, places, organisations etc. TRUE
Entity recognition
You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. The service can also provide links to more information about that entity on the web. An entity is essentially an item of a particular type or a category; and in some cases, subtype, such as those as shown in the following table.
Entity recognition
Type SubType Example
Person “Bill Gates”, “John”
Location “Paris”, “New York”
Organization “Microsoft”
Text analytics service can read text in images, scanned documents. FALSE
Optical Character Recognition (OCR)
Microsoft’s Computer Vision API includes Optical Character Recognition (OCR) capabilities that extract printed or handwritten text from images and PDF documents. The OCR APIs extract text from both analogue documents (images, scanned documents) and digitized documents. You can extract text from images in the wild, such as photos of license plates or containers with serial numbers, as well as from documents – invoices, bills, financial reports, articles, and more. https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-recognizing-text https://devblogs.microsoft.com/cse/2018/05/07/handwriting-detection-and-recognition-in-scanned-documents-using-azure-ml-package-computer-vision-azure-cognitive-services-ocr/
Unattempted
The text analytics service can detect language of your text. – TRUE
Language detection
Use the language detection capability of the Text Analytics service to identify the language in which text is written. You can submit multiple documents at a time for analysis. For each document submitted to it, the service will detect:
The language name (for example “English”).
The ISO 6391 language code (for example, “en”).
A score indicating a level of confidence in the language detection. https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure
Text analytics service can classify a broad range of entities in text, such as people, places, organisations etc. TRUE
Entity recognition
You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. The service can also provide links to more information about that entity on the web. An entity is essentially an item of a particular type or a category; and in some cases, subtype, such as those as shown in the following table.
Entity recognition
Type SubType Example
Person “Bill Gates”, “John”
Location “Paris”, “New York”
Organization “Microsoft”
Text analytics service can read text in images, scanned documents. FALSE
Optical Character Recognition (OCR)
Microsoft’s Computer Vision API includes Optical Character Recognition (OCR) capabilities that extract printed or handwritten text from images and PDF documents. The OCR APIs extract text from both analogue documents (images, scanned documents) and digitized documents. You can extract text from images in the wild, such as photos of license plates or containers with serial numbers, as well as from documents – invoices, bills, financial reports, articles, and more. https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-recognizing-text https://devblogs.microsoft.com/cse/2018/05/07/handwriting-detection-and-recognition-in-scanned-documents-using-azure-ml-package-computer-vision-azure-cognitive-services-ocr/
Question 16 of 64
16. Question
You need to deploy a real-time inference pipeline as a service for others to consume. Select the appropriate option to where you must deploy the model.
The only way to enter data in the Azure Machine Learning designer is to use Import data module.
Select True if the statement is correct else select False.
Correct
Azure machine learning studio supports manually entering data through the module “Enter Data Manually”. It also supports uploading data-set from local machine as well.
Incorrect
Azure machine learning studio supports manually entering data through the module “Enter Data Manually”. It also supports uploading data-set from local machine as well.
Unattempted
Azure machine learning studio supports manually entering data through the module “Enter Data Manually”. It also supports uploading data-set from local machine as well.
Question 19 of 64
19. Question
For the below statement select YES if the statement is true otherwise select NO.
Apply automated ML when you want Azure Machine Learning to train and tune a model for you using the target metric you specify.
Select two task that can be performed by using the computer vision service?
Correct
detect faces in an image
train a custom image classification model-Â https://docs.microsoft.com/en-us/learn/modules/classify-images-custom-vision/2-azure-image-classification
Incorrect
detect faces in an image
train a custom image classification model-Â https://docs.microsoft.com/en-us/learn/modules/classify-images-custom-vision/2-azure-image-classification
Unattempted
detect faces in an image
train a custom image classification model-Â https://docs.microsoft.com/en-us/learn/modules/classify-images-custom-vision/2-azure-image-classification
Question 21 of 64
21. Question
The AI solution shown in the diagram is __________________________.
Choose one from below options.
Correct
A chatbot
Incorrect
A chatbot
Unattempted
A chatbot
Question 22 of 64
22. Question
You have an input data-set where one column has values ranging from 0 to 1 while another column with values ranging from 10,000 to 100,000. You plan to combine the values of 2 columns to use it as a feature during modelling.
Which mathematical function will be applied to the data-set to rescale every feature to the [0,1] interval linearly.
Select the best answer from below options.
Correct
MinMax: The min-max normalizer linearly rescales every feature to the [0,1] interval.
Rescaling to the [0,1] interval is done by shifting the values of each feature so that the minimal value is 0, and then dividing by the new maximal value (which is the difference between the original maximal and minimal values).
MinMax: The min-max normalizer linearly rescales every feature to the [0,1] interval.
Rescaling to the [0,1] interval is done by shifting the values of each feature so that the minimal value is 0, and then dividing by the new maximal value (which is the difference between the original maximal and minimal values).
MinMax: The min-max normalizer linearly rescales every feature to the [0,1] interval.
Rescaling to the [0,1] interval is done by shifting the values of each feature so that the minimal value is 0, and then dividing by the new maximal value (which is the difference between the original maximal and minimal values).
Which of the following is commonly used to predict a categorical variable?
Correct
Incorrect
Unattempted
Question 24 of 64
24. Question
You are working as an AI developer in a large London based eCommerce company where you have developed a business chatbot. As a next step, you need to add content to the bot that will help answer simple user queries.
By using the QnA maker, what are the 3 ways to create a knowledge base that consists of question-and-answer pairs?
Each correct answer presents a complete solution.
Correct
generate question and answer from an existing web page.
chit-chat contain from a predefined data source.
manually enter questions and answers.
After provisioning a QnA Maker resource, you can use the QnA Maker portal to create a knowledge base that consists of question-and-answer pairs. These questions and answers can be:
Generated from an existing FAQ document or web page.
Imported from a pre-defined chit-chat data source.
Entered and edited manually.
Incorrect
generate question and answer from an existing web page.
chit-chat contain from a predefined data source.
manually enter questions and answers.
After provisioning a QnA Maker resource, you can use the QnA Maker portal to create a knowledge base that consists of question-and-answer pairs. These questions and answers can be:
Generated from an existing FAQ document or web page.
Imported from a pre-defined chit-chat data source.
Entered and edited manually.
Unattempted
generate question and answer from an existing web page.
chit-chat contain from a predefined data source.
manually enter questions and answers.
After provisioning a QnA Maker resource, you can use the QnA Maker portal to create a knowledge base that consists of question-and-answer pairs. These questions and answers can be:
Generated from an existing FAQ document or web page.
Imported from a pre-defined chit-chat data source.
Entered and edited manually.
Question 25 of 64
25. Question
Which machine learning algorithm is mostly used for predicting the values of categorical variables.
Correct
For categorical variables, the sample space is discrete so the standard k-means algorithm is not directly applied as the euclidean function will not work.
Incorrect
For categorical variables, the sample space is discrete so the standard k-means algorithm is not directly applied as the euclidean function will not work.
Unattempted
For categorical variables, the sample space is discrete so the standard k-means algorithm is not directly applied as the euclidean function will not work.
Question 26 of 64
26. Question
Which of the following statement is CORRECT?
Correct
Forecasting the price of a car based on historical data is an example of anomaly detection: This is based on regression. Hence false.
Predicting whether a patient will develop diabetes based on the patient’s medical history is an example of anomaly detection: This is based on the classification model which classifies if he will be diabetic or not. Hence false.
Incorrect
Forecasting the price of a car based on historical data is an example of anomaly detection: This is based on regression. Hence false.
Predicting whether a patient will develop diabetes based on the patient’s medical history is an example of anomaly detection: This is based on the classification model which classifies if he will be diabetic or not. Hence false.
Unattempted
Forecasting the price of a car based on historical data is an example of anomaly detection: This is based on regression. Hence false.
Predicting whether a patient will develop diabetes based on the patient’s medical history is an example of anomaly detection: This is based on the classification model which classifies if he will be diabetic or not. Hence false.
Question 27 of 64
27. Question
___________can be integrated with Azure Bot Service to identify valuable information in a conversation and can interprets user intent.
Classification model involves assigning a class label to input examples from the problem domain.
Binary classification refers to predicting one of two classes, YES/NO, 1/0, TRUE/FALSE, SPAM/not SPAM etc.
Incorrect
Classification model involves assigning a class label to input examples from the problem domain.
Binary classification refers to predicting one of two classes, YES/NO, 1/0, TRUE/FALSE, SPAM/not SPAM etc.
Unattempted
Classification model involves assigning a class label to input examples from the problem domain.
Binary classification refers to predicting one of two classes, YES/NO, 1/0, TRUE/FALSE, SPAM/not SPAM etc.
Question 29 of 64
29. Question
You have the below pipeline in the azure machine learning designer. What is the output of the pipeline (at node 1)?
Select the most suitable option.
As per below description which Microsoft guiding principles for responsible AI is followed.
The AI system should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.
What kind of machine learning task most accurately defines the below scenario?
Picking temperature and pressure to train with the model.
Correct
Feature selection: In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.
Other examples:
Model evaluation: Examining the values of a confusion matrix
Feature engineering: Splitting a date into month, day, and year fields
Feature selection: Picking temperature and pressure to train a weather model
Incorrect
Feature selection: In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.
Other examples:
Model evaluation: Examining the values of a confusion matrix
Feature engineering: Splitting a date into month, day, and year fields
Feature selection: Picking temperature and pressure to train a weather model
Unattempted
Feature selection: In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.
Other examples:
Model evaluation: Examining the values of a confusion matrix
Feature engineering: Splitting a date into month, day, and year fields
Feature selection: Picking temperature and pressure to train a weather model
Question 32 of 64
32. Question
Which statements are true for computer vision? Select 2 options from below.
Correct
Detecting brands
This feature provides the ability to identify commercial brands. The service has an existing database of thousands of globally recognized logos from commercial brands of products.
When you call this service and pass it an image, it will perform a detection task and determine if any of the identified objects in the image are recognized brands. Recall that it compares the brands against its database of popular brands spanning clothing, consumer electronics, and many more categories. If a known brand is detected, the service will return a response that contains the brand name, a confidence score (from 0 to 1 indicating how positive the identification is), and a bounding box (coordinates) for where in the image the detected brand was found.
Detecting color scheme
When you pass an image to the Detect Color API, Computer Vision will analyze the image for three main attributes. The attributes are:
dominant foreground color
dominant background color
dominant colors for whole image.
The colors are limited to the following 12 colors:
Detecting brands
This feature provides the ability to identify commercial brands. The service has an existing database of thousands of globally recognized logos from commercial brands of products.
When you call this service and pass it an image, it will perform a detection task and determine if any of the identified objects in the image are recognized brands. Recall that it compares the brands against its database of popular brands spanning clothing, consumer electronics, and many more categories. If a known brand is detected, the service will return a response that contains the brand name, a confidence score (from 0 to 1 indicating how positive the identification is), and a bounding box (coordinates) for where in the image the detected brand was found.
Detecting color scheme
When you pass an image to the Detect Color API, Computer Vision will analyze the image for three main attributes. The attributes are:
dominant foreground color
dominant background color
dominant colors for whole image.
The colors are limited to the following 12 colors:
Detecting brands
This feature provides the ability to identify commercial brands. The service has an existing database of thousands of globally recognized logos from commercial brands of products.
When you call this service and pass it an image, it will perform a detection task and determine if any of the identified objects in the image are recognized brands. Recall that it compares the brands against its database of popular brands spanning clothing, consumer electronics, and many more categories. If a known brand is detected, the service will return a response that contains the brand name, a confidence score (from 0 to 1 indicating how positive the identification is), and a bounding box (coordinates) for where in the image the detected brand was found.
Detecting color scheme
When you pass an image to the Detect Color API, Computer Vision will analyze the image for three main attributes. The attributes are:
dominant foreground color
dominant background color
dominant colors for whole image.
The colors are limited to the following 12 colors:
You are building an application which will able to tell if the person looks like other people. Which facial recognition tasks will be appropriate for the above scenario?
Forecasting the Carbon Dioxide Emissions by Energy Consumption Use in Melbourne is an example of ________
Correct
Regression analysis is used when you want to predict a continuous dependent variable from several independent variables.
Regression analysis is primarily used for two conceptually distinct purposes. It is widely used for prediction and forecasting. Its also used to infer causal relationships between the independent and dependent variables.
Incorrect
Regression analysis is used when you want to predict a continuous dependent variable from several independent variables.
Regression analysis is primarily used for two conceptually distinct purposes. It is widely used for prediction and forecasting. Its also used to infer causal relationships between the independent and dependent variables.
Unattempted
Regression analysis is used when you want to predict a continuous dependent variable from several independent variables.
Regression analysis is primarily used for two conceptually distinct purposes. It is widely used for prediction and forecasting. Its also used to infer causal relationships between the independent and dependent variables.
Question 35 of 64
35. Question
You are developing a solution based on facial recognition. You have to ensure that the AI-based solution meets ethical and legal standards that advocate regulations on people civil liberties and works within a framework of governance and organizational principles.
The Microsoft guiding principle for responsible AI considered is?
Which of the following statements are CORRECT.
Select all that apply.
Correct
All of the statements are correct:
A. Azure bot service conversationally engages with customers. Azure bots are designed to interact with users in a natural, conversational way, allowing them to answer questions, complete tasks, and provide information.
B. Azure bot service can import FAQs to question and answer set. Azure Bot Service allows you to import frequently asked questions (FAQs) into your bot’s knowledge base. This can help your bot to answer common questions more efficiently and accurately.
C. It is possible to integrate intelligent and enterprise-grade azure bots with azure cognitive service. Azure Cognitive Services offer a variety of AI services that can be integrated with Azure bots to enhance their capabilities. For example, you can use Azure Cognitive Services for Language Speech to add speech recognition and natural language processing to your bot.
A. Azure bot service conversationally engages with customers. Azure bots are designed to interact with users in a natural, conversational way, allowing them to answer questions, complete tasks, and provide information.
B. Azure bot service can import FAQs to question and answer set. Azure Bot Service allows you to import frequently asked questions (FAQs) into your bot’s knowledge base. This can help your bot to answer common questions more efficiently and accurately.
C. It is possible to integrate intelligent and enterprise-grade azure bots with azure cognitive service. Azure Cognitive Services offer a variety of AI services that can be integrated with Azure bots to enhance their capabilities. For example, you can use Azure Cognitive Services for Language Speech to add speech recognition and natural language processing to your bot.
A. Azure bot service conversationally engages with customers. Azure bots are designed to interact with users in a natural, conversational way, allowing them to answer questions, complete tasks, and provide information.
B. Azure bot service can import FAQs to question and answer set. Azure Bot Service allows you to import frequently asked questions (FAQs) into your bot’s knowledge base. This can help your bot to answer common questions more efficiently and accurately.
C. It is possible to integrate intelligent and enterprise-grade azure bots with azure cognitive service. Azure Cognitive Services offer a variety of AI services that can be integrated with Azure bots to enhance their capabilities. For example, you can use Azure Cognitive Services for Language Speech to add speech recognition and natural language processing to your bot.
Which AI service you should use to determine if a customer is upset based on what the customer types in the chatbot?
Correct
Text Analytics cognitive service can help simplify application development by using pre-trained models that can:
Determine the language of a document or text (for example, French or English).
Perform sentiment analysis on text to determine a positive or negative sentiment.
Extract key phrases from text that might indicate its main talking points.
Identify and categorize entities in the text. Entities can be people, places, organizations, or even everyday items such as dates, times, quantities, and so on. https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/1-introduction
Incorrect
Text Analytics cognitive service can help simplify application development by using pre-trained models that can:
Determine the language of a document or text (for example, French or English).
Perform sentiment analysis on text to determine a positive or negative sentiment.
Extract key phrases from text that might indicate its main talking points.
Identify and categorize entities in the text. Entities can be people, places, organizations, or even everyday items such as dates, times, quantities, and so on. https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/1-introduction
Unattempted
Text Analytics cognitive service can help simplify application development by using pre-trained models that can:
Determine the language of a document or text (for example, French or English).
Perform sentiment analysis on text to determine a positive or negative sentiment.
Extract key phrases from text that might indicate its main talking points.
Identify and categorize entities in the text. Entities can be people, places, organizations, or even everyday items such as dates, times, quantities, and so on. https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/1-introduction
Question 38 of 64
38. Question
You are working as a cloud consultant for a major retail company. You are planning to create a bot from a frequently asked questions (FAQ) document. You found out that Microsoft Azure AI has many services that can help in creating a bot seamlessly. Which azure AI service should you use?
Correct
QnA Maker is a cloud-based API service that lets you create a conversational question-and-answer layer over your existing data. Use it to build a knowledge base by extracting questions and answers from your semi-structured content, including FAQs, manuals and documents. Answer users’ questions with the best answers from the QnAs in your knowledge base—automatically. Your knowledge base gets smarter, too, as it continually learns from user behavior. https://azure.microsoft.com/en-in/services/cognitive-services/qna-maker/
Incorrect
QnA Maker is a cloud-based API service that lets you create a conversational question-and-answer layer over your existing data. Use it to build a knowledge base by extracting questions and answers from your semi-structured content, including FAQs, manuals and documents. Answer users’ questions with the best answers from the QnAs in your knowledge base—automatically. Your knowledge base gets smarter, too, as it continually learns from user behavior. https://azure.microsoft.com/en-in/services/cognitive-services/qna-maker/
Unattempted
QnA Maker is a cloud-based API service that lets you create a conversational question-and-answer layer over your existing data. Use it to build a knowledge base by extracting questions and answers from your semi-structured content, including FAQs, manuals and documents. Answer users’ questions with the best answers from the QnAs in your knowledge base—automatically. Your knowledge base gets smarter, too, as it continually learns from user behavior. https://azure.microsoft.com/en-in/services/cognitive-services/qna-maker/
Question 39 of 64
39. Question
Which of the following statement(s) is/are CORRECT?
Select all that apply.
Your brand manager of the company has asked you to develop an AI solution which evaluates the text of a document(s) and then identify the main talking points of the document(s).
Which type of Natural Language Processing should you use?
Scenarios
(1) Predict the price of a pharma stock based upon various macro-economic variables including pandemic crisis like COVID19.
(2) Predict whether you have survived the sinking of the Titanic if you were a passenger on board.
(3) Segmenting customers into different categories to send advertisements and recommendations to each group.
Types of machine learning
classification
clustering
regression
Choose the option that matches the scenarios to appropriate machine learning types. Select one.
(1 or 2 or 3) represents a scenario statement in the options below.
Correct
Incorrect
Unattempted
Question 43 of 64
43. Question
K-Means algorithm is used to solve ________ problems.
Select the most appropriate option to complete the statement.
You are developing a model to predict events by using classification
You have a confusion Matrix for the model score on test data as shown in the following exhibit:
The number of cases correctly predicted positives are X
The number of cases false negatives are Y .
Values for X and YÂ are?
Correct
Considering ACTUAL values as true and false and PREDICTED values as positive and negative, the confusion matrix will have the values for
TP: True Positive=136
(model predicted image is AN APPLE-positive, In ACTUAL is an APPLE-so its true)
FP: False Positive=25
(model predicted image is AN APPLE-positive, In ACTUAL is NOT an APPLE- so its false)
FN: False Negative =1289
(model predicted image is NOT an APPLE-negative, In ACTUAL is an APPLE so its false)
TN: True Negative = 10745
(model predicted image is NOT an APPLE-negative, In ACTUAL is NOT an APPLE, so its true)
Incorrect
Considering ACTUAL values as true and false and PREDICTED values as positive and negative, the confusion matrix will have the values for
TP: True Positive=136
(model predicted image is AN APPLE-positive, In ACTUAL is an APPLE-so its true)
FP: False Positive=25
(model predicted image is AN APPLE-positive, In ACTUAL is NOT an APPLE- so its false)
FN: False Negative =1289
(model predicted image is NOT an APPLE-negative, In ACTUAL is an APPLE so its false)
TN: True Negative = 10745
(model predicted image is NOT an APPLE-negative, In ACTUAL is NOT an APPLE, so its true)
Unattempted
Considering ACTUAL values as true and false and PREDICTED values as positive and negative, the confusion matrix will have the values for
TP: True Positive=136
(model predicted image is AN APPLE-positive, In ACTUAL is an APPLE-so its true)
FP: False Positive=25
(model predicted image is AN APPLE-positive, In ACTUAL is NOT an APPLE- so its false)
FN: False Negative =1289
(model predicted image is NOT an APPLE-negative, In ACTUAL is an APPLE so its false)
TN: True Negative = 10745
(model predicted image is NOT an APPLE-negative, In ACTUAL is NOT an APPLE, so its true)
Question 47 of 64
47. Question
Which of the following statement(s) is/are false?
Correct
Data labelling is the process of assigning informative tags to subsets of data. – YES
Labelling usually takes a set of unlabelled data and add each piece of that unlabelled data with meaningful tags that are informative. For example, labels might indicate whether a photo contains an orange or a pineapple.
You should always evaluate a model by using the same data used to train the model. -FALSE
Data is split and provided to train a model and other part to evaluate a model.
Data labelling is the process of assigning informative tags to subsets of data. – YES
Labelling usually takes a set of unlabelled data and add each piece of that unlabelled data with meaningful tags that are informative. For example, labels might indicate whether a photo contains an orange or a pineapple.
You should always evaluate a model by using the same data used to train the model. -FALSE
Data is split and provided to train a model and other part to evaluate a model.
Data labelling is the process of assigning informative tags to subsets of data. – YES
Labelling usually takes a set of unlabelled data and add each piece of that unlabelled data with meaningful tags that are informative. For example, labels might indicate whether a photo contains an orange or a pineapple.
You should always evaluate a model by using the same data used to train the model. -FALSE
Data is split and provided to train a model and other part to evaluate a model.
Below is the Coefficient of Determination (R2 R-Squared) of four different models. Which model is best performing as per below R2 data?
Correct
Coefficient of Determination (R2): This metric is more commonly referred to as R-Squared, and summarizes how much of the variance between predicted and true values is explained by the model. The closer to 1 this value is, the better the model is performing.
Coefficient of Determination (R2): This metric is more commonly referred to as R-Squared, and summarizes how much of the variance between predicted and true values is explained by the model. The closer to 1 this value is, the better the model is performing.
Coefficient of Determination (R2): This metric is more commonly referred to as R-Squared, and summarizes how much of the variance between predicted and true values is explained by the model. The closer to 1 this value is, the better the model is performing.
You have a lecture note which is sorted as text. For your exam, you need to extract key terms from the notes to generate summaries.
Which type of AI workload should you use?
Correct
Key phrase extraction is the concept of evaluating the text of a document, or documents, and then identifying the main talking points of the document(s). Consider the restaurant scenario discussed previously. Depending on the volume of surveys that you have collected, it can take a long time to read through the reviews. Instead, you can use the key phrase extraction capabilities of the Text Analytics service to summarize the main points.
Explore natural language processing –>Analyze text with the Text Analytics service–> Get started with Text Analytics on Azure–>Key phrase extraction
Incorrect
Key phrase extraction is the concept of evaluating the text of a document, or documents, and then identifying the main talking points of the document(s). Consider the restaurant scenario discussed previously. Depending on the volume of surveys that you have collected, it can take a long time to read through the reviews. Instead, you can use the key phrase extraction capabilities of the Text Analytics service to summarize the main points.
Explore natural language processing –>Analyze text with the Text Analytics service–> Get started with Text Analytics on Azure–>Key phrase extraction
Unattempted
Key phrase extraction is the concept of evaluating the text of a document, or documents, and then identifying the main talking points of the document(s). Consider the restaurant scenario discussed previously. Depending on the volume of surveys that you have collected, it can take a long time to read through the reviews. Instead, you can use the key phrase extraction capabilities of the Text Analytics service to summarize the main points.
Explore natural language processing –>Analyze text with the Text Analytics service–> Get started with Text Analytics on Azure–>Key phrase extraction
Question 50 of 64
50. Question
You own a service desk company and employs a team of customer service agents to provide telephone and email support to customers. Now you are planning to develop a web chatbot to automatically answer common customer queries. What business benefit the does owner expect as a result of the web chatbot?
Correct
The Web chatbot will answer frequently asked questions which will reduce workload for the customer service agents
Incorrect
The Web chatbot will answer frequently asked questions which will reduce workload for the customer service agents
Unattempted
The Web chatbot will answer frequently asked questions which will reduce workload for the customer service agents
Question 51 of 64
51. Question
You use Natural Language Processing to process text from a historical data.
You receive the output shown in the following exhibit:
INPUT:
The huge temple complex covers an area of over 400,000 square feet (37,000 m2), and is surrounded by a high fortified wall. This 20 feet (6.1 m) high wall is known as Meghanada Pacheri. Another wall known as kurma bedha surrounds the main temple. It contains at least 120 temples and shrines. With its sculptural richness and fluidity of the Oriya style of temple architecture, it is one of the most magnificent monuments of India.
OUTPUT:
temple complex [Location]
400,000 square feet [Quantity-Dimension]
37,000 m2 [Quantity-Dimension]
20 feet [Quantity-Dimension]
6.1 m [Quantity-Dimension]
temple [Location-Structural]
120 [Quantity-Number]
fluidity [Skill]
architecture [Skill]
one [Quantity-Number]
India [Location-GPE]
Which type of Natural Language Processing was performed?
You have used various algorithms for a classification model and found below AUC values.
0.317
0.158
0.114
As none of them is close to 1, you decided to multiply all values with 3.0 to make it close to 1.
Will that change the modelÂ’s performance based on AUC?
Correct
Transforming the prediction will still maintain the relative ranking. So the AUC remains the same no matter whatever we multiply.
Incorrect
Transforming the prediction will still maintain the relative ranking. So the AUC remains the same no matter whatever we multiply.
Unattempted
Transforming the prediction will still maintain the relative ranking. So the AUC remains the same no matter whatever we multiply.
Question 53 of 64
53. Question
In Azure machine learning designer, you should use _______ module to create a training data set and validation data set from an existing data set.
You are working as a solution architect for an online retail store and have been tasked to create a service that will go through all the reviews of a product and should be able to detect if a customer is happy with the product or upset.
Which type of AI workload should you use?
Correct
What is Sentiment analysis? Is it a part of NLP?
The Text Analytics service can evaluate text and return sentiment scores and labels for each sentence. This capability is useful for detecting positive and negative sentiment in social media, customer reviews, discussion forums and more.
The Text Analytics service is a part of the Azure Cognitive Services offerings that can perform advanced natural language processing over raw text.
Why the answer is not semantic segmentation?
Semantic segmentation, or image segmentation, is the task of clustering parts of an image together which belong to the same object class. It is a form of pixel-level prediction because each pixel in an image is classified according to a category.
More info at :Â https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure
Incorrect
What is Sentiment analysis? Is it a part of NLP?
The Text Analytics service can evaluate text and return sentiment scores and labels for each sentence. This capability is useful for detecting positive and negative sentiment in social media, customer reviews, discussion forums and more.
The Text Analytics service is a part of the Azure Cognitive Services offerings that can perform advanced natural language processing over raw text.
Why the answer is not semantic segmentation?
Semantic segmentation, or image segmentation, is the task of clustering parts of an image together which belong to the same object class. It is a form of pixel-level prediction because each pixel in an image is classified according to a category.
More info at :Â https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure
Unattempted
What is Sentiment analysis? Is it a part of NLP?
The Text Analytics service can evaluate text and return sentiment scores and labels for each sentence. This capability is useful for detecting positive and negative sentiment in social media, customer reviews, discussion forums and more.
The Text Analytics service is a part of the Azure Cognitive Services offerings that can perform advanced natural language processing over raw text.
Why the answer is not semantic segmentation?
Semantic segmentation, or image segmentation, is the task of clustering parts of an image together which belong to the same object class. It is a form of pixel-level prediction because each pixel in an image is classified according to a category.
More info at :Â https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure
Question 55 of 64
55. Question
You are building an AI system for a remote proctor testing system where you need to check if two images of a face belong to the same person.
Which facial recognition tasks will be appropriate for the above scenario.
You have developed a web chatbot for a retail company and now you want to add additional features to the bot so that it can detect how upset the customer is based on what the customer types.
This is an example of which type of Natural Language Processing workload?
scenarios:
(1) automatically tag known friends in social media photographs
(2) digitizing historical documents
(3) locate vehicles in images
Choose the option that matches the scenarios to appropriate machine learning types. Select one.
(1 or 2 or 3) represents a scenario statement in the options below.
Correct
Incorrect
Unattempted
Question 59 of 64
59. Question
Select the most appropriate Natural Language Processing workload for below scenario.
scenario:
Translate email communication to a specific language.
The Text Translator service supports text-to-text translation between more than 60 languages. When using the service, you must specify the language you are translating from and the language you are translating to using ISO 639-1 language codes, such as en for English, fr for French, and zh for Chinese. Alternatively, you can specify cultural variants of languages by extending the language code with the appropriate 3166-1 cultural code – for example, en-US for US English, en-GB for British English, or fr-CA for Canadian French.
When using the Text Translator service, you can specify one from language with multiple to languages, enabling you to simultaneously translate a source document into multiple languages.
Speech service language support
As with the Translator Text service, you can specify one source language and one or more target languages to which the source should be translated. You can translate speech into over 60 languages.
The source language must be specified using the extended language and culture code format, such as es-US for American Spanish. This requirement helps ensure that the source is understood properly, allowing for localized pronunciation and linguistic idioms.
The target languages must be specified using a two-character language code, such as en for English or de for German
The Text Translator service supports text-to-text translation between more than 60 languages. When using the service, you must specify the language you are translating from and the language you are translating to using ISO 639-1 language codes, such as en for English, fr for French, and zh for Chinese. Alternatively, you can specify cultural variants of languages by extending the language code with the appropriate 3166-1 cultural code – for example, en-US for US English, en-GB for British English, or fr-CA for Canadian French.
When using the Text Translator service, you can specify one from language with multiple to languages, enabling you to simultaneously translate a source document into multiple languages.
Speech service language support
As with the Translator Text service, you can specify one source language and one or more target languages to which the source should be translated. You can translate speech into over 60 languages.
The source language must be specified using the extended language and culture code format, such as es-US for American Spanish. This requirement helps ensure that the source is understood properly, allowing for localized pronunciation and linguistic idioms.
The target languages must be specified using a two-character language code, such as en for English or de for German
The Text Translator service supports text-to-text translation between more than 60 languages. When using the service, you must specify the language you are translating from and the language you are translating to using ISO 639-1 language codes, such as en for English, fr for French, and zh for Chinese. Alternatively, you can specify cultural variants of languages by extending the language code with the appropriate 3166-1 cultural code – for example, en-US for US English, en-GB for British English, or fr-CA for Canadian French.
When using the Text Translator service, you can specify one from language with multiple to languages, enabling you to simultaneously translate a source document into multiple languages.
Speech service language support
As with the Translator Text service, you can specify one source language and one or more target languages to which the source should be translated. You can translate speech into over 60 languages.
The source language must be specified using the extended language and culture code format, such as es-US for American Spanish. This requirement helps ensure that the source is understood properly, allowing for localized pronunciation and linguistic idioms.
The target languages must be specified using a two-character language code, such as en for English or de for German
Question 60 of 64
60. Question
Which facial recognition task can answer below question appropriately.
Do all the faces belong together?
You have been tasked to build an AI solution which takes English text as input and generates natural, reliable and expressive sounding speech that fits different emotions in the context of various patterns of human voices like lyrical, empathy etc.
Which azure cognitive service is best suited for the above use case?
Select the CORRECT statements from below.
Select all that apply.
Correct
https://en.wikipedia.org/wiki/Logistic_regression
An explanation of logistic regression can begin with an explanation of the standard logistic function. The logistic function is a sigmoid function, which takes any real input and outputs a value between zero and one for the logit, this is interpreted as taking input log-odds and having output probability.
Incorrect
https://en.wikipedia.org/wiki/Logistic_regression
An explanation of logistic regression can begin with an explanation of the standard logistic function. The logistic function is a sigmoid function, which takes any real input and outputs a value between zero and one for the logit, this is interpreted as taking input log-odds and having output probability.
Unattempted
https://en.wikipedia.org/wiki/Logistic_regression
An explanation of logistic regression can begin with an explanation of the standard logistic function. The logistic function is a sigmoid function, which takes any real input and outputs a value between zero and one for the logit, this is interpreted as taking input log-odds and having output probability.
Question 63 of 64
63. Question
A media company is implementing an AI system that entitles everyone including people with disabilities such as vision impairment, deaf or hard of hearing. Identify the Microsoft guiding principle for responsible AI which the company is trying to implement.
Correct
AI can improve access to education, government services, employment, information, and a wide range of other opportunities. Intelligent solutions such as real-time speech-to-text transcription, visual recognition services, and predictive text functionality are already empowering those with hearing, visual, and other impairments.
AI can improve access to education, government services, employment, information, and a wide range of other opportunities. Intelligent solutions such as real-time speech-to-text transcription, visual recognition services, and predictive text functionality are already empowering those with hearing, visual, and other impairments.
AI can improve access to education, government services, employment, information, and a wide range of other opportunities. Intelligent solutions such as real-time speech-to-text transcription, visual recognition services, and predictive text functionality are already empowering those with hearing, visual, and other impairments.
For the below statement select YES if the statement is true otherwise select NO.
Automated machine learning (AutoML) automating the process of applying machine learning model’s iterative and time-consuming tasks to real-world problems.