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Explain Conversational Artificial Intelligence Workloads Features on Azure (15-20%)
In the last section, you will face with the following subtopics:
- Establish Azure services for Conversational Artificial Intelligence – The potential candidates for the Microsoft AI-900 exam should have the capacity to identify the capabilities of Azure Bot Service and QnA Maker service.
- Identify the basic use cases for Conversational Artificial Intelligence (AI) – The students should be able to identify the features and usage for a range of elements. These include personal digital assistants, webchat bots, and telephone voice menus. It also covers the skills in identifying the basic features of conversational Artificial Intelligence solutions.
The Microsoft AI-900: Microsoft Azure AI Fundamentals exam is aimed at those professionals who want to demonstrate their knowledge of basic Machine Learning (ML) and Artificial Intelligence (AI) workloads. The test evaluates their understanding of how to implement both ML and AI on Azure. The candidates who pass this exam are eligible for the Microsoft Certified: Azure AI Fundamentals certification.
NEW QUESTION 26
Match the Microsoft guiding principles for responsible AI to the appropriate descriptions.
To answer, drag the appropriate principle from the column on the left to its description on the right. Each principle may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Reliability and safety
To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.
Box 2: Fairness
Fairness: AI systems should treat everyone fairly and avoid affecting similarly situated groups of people in different ways. For example, when AI systems provide guidance on medical treatment, loan applications, or employment, they should make the same recommendations to everyone with similar symptoms, financial circumstances, or professional qualifications.
We believe that mitigating bias starts with people understanding the implications and limitations of AI predictions and recommendations. Ultimately, people should supplement AI decisions with sound human judgment and be held accountable for consequential decisions that affect others.
Box 3: Privacy and security
As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people. AI systems must comply with privacy laws that require transparency about the collection, use, and storage of data and mandate that consumers have appropriate controls to choose how their data is used Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
NEW QUESTION 27
Your company wants to build a recycling machine for bottles. The recycling machine must automatically identify bottles of the correct shape and reject all other items.
Which type of AI workload should the company use?
- A. anomaly detection
- B. conversational AI
- C. computer vision
- D. natural language processing
Answer: C
Explanation:
Azure's Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview
NEW QUESTION 28
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Anomaly detection encompasses many important tasks in machine learning:
Identifying transactions that are potentially fraudulent.
Learning patterns that indicate that a network intrusion has occurred.
Finding abnormal clusters of patients.
Checking values entered into a system.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/anomaly-detection
NEW QUESTION 29
You send an image to a Computer Vision API and receive back the annotated image shown in the exhibit.
Which type of computer vision was used?
- A. optical character recognition (OCR)
- B. object detection
- C. image classification
- D. semantic segmentation
Answer: B
Explanation:
Section: Describe features of computer vision workloads on Azure
Explanation:
Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like "indoor", which can't be localized with bounding boxes.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
NEW QUESTION 30
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-label-data
NEW QUESTION 31
You need to determine the location of cars in an image so that you can estimate the distance between the cars.
Which type of computer vision should you use?
- A. optical character recognition (OCR)
- B. face detection
- C. object detection
- D. image classification
Answer: C
Explanation:
Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like "indoor", which can't be localized with bounding boxes.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
NEW QUESTION 32
You send an image to a Computer Vision API and receive back the annotated image shown in the exhibit.
Which type of computer vision was used?
- A. optical character recognition (OCR)
- B. object detection
- C. image classification
- D. semantic segmentation
Answer: B
Explanation:
Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like "indoor", which can't be localized with bounding boxes.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
NEW QUESTION 33
You have a dataset that contains information about taxi journeys that occurred during a given period.
You need to train a model to predict the fare of a taxi journey. What should you use as a feature?
- A. the fare of individual taxi journeys
- B. the number of taxi journeys in the dataset
- C. the trip ID of individual taxi journeys
- D. the trip distance of individual taxi journeys
Answer: D
Explanation:
The label is the column you want to predict. The identified Featuresare the inputs you give the model to predict the Label.
Example:
The provided data set contains the following columns:
passenger_count: The number of passengers on the trip is a feature.
trip_time_in_secs: The amount of time the trip took. You want to predict the fare of the trip before the trip is completed. At that moment, you don't know how long the trip would take. Thus, the trip time is not a feature and you'll exclude this column from the model.
trip_distance: The distance of the trip is a feature.
payment_type: The payment method (cash or credit card) is a feature. fare_amount: The total taxi fare paid is the label.
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/predict-prices
NEW QUESTION 34
Which type of machine learning should you use to predict the number of gift cards that will be sold next month?
- A. regression
- B. clustering
- C. classification
Answer: A
Explanation:
Section: Describe fundamental principles of machine learning on Azure
Explanation:
In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression
NEW QUESTION 35
In which two scenarios can you use a speech synthesis solution? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. an automated voice that reads back a credit card number entered into a telephone by using a numeric keypad
- B. an Al character in a computer game that speaks audibly to a player
- C. generating live captions for a news broadcast
- D. extracting key phrases from the audio recording of a meeting
Answer: A,B
Explanation:
Azure Text to Speech is a Speech service feature that converts text to lifelike speech.
Reference:
https://azure.microsoft.com/en-in/services/cognitive-services/text-to-speech/
NEW QUESTION 36
For a machine learning progress, how should you split data for training and evaluation?
- A. Use features for training and labels for evaluation.
- B. Randomly split the data into columns for training and columns for evaluation.
- C. Randomly split the data into rows for training and rows for evaluation.
- D. Use labels for training and features for evaluation.
Answer: C
Explanation:
Explanation
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/split-data
NEW QUESTION 37
Match the types of natural languages processing workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
NEW QUESTION 38
You are evaluating whether to use a basic workspace or an enterprise workspace in Azure Machine Learning.
What are two tasks that require an enterprise workspace? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. Use a graphical user interface (GUI) to define and run machine learning experiments from Azure Machine Learning designer.
- B. Create a compute instance to use as a workstation.
- C. Create a dataset from a comma-separated value (CSV) file.
- D. Use a graphical user interface (GUI) to run automated machine learning experiments.
Answer: A,D
Explanation:
Section: Describe fundamental principles of machine learning on Azure
Explanation:
Note: Enterprise workspaces are no longer available as of September 2020. The basic workspace now has all the functionality of the enterprise workspace.
Reference:
https://www.azure.cn/en-us/pricing/details/machine-learning/
https://docs.microsoft.com/en-us/azure/machine-learning/concept-workspace
NEW QUESTION 39
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Yes
Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models.
Box 2: Yes
With the designer you can connect the modules to create a pipeline draft.
As you edit a pipeline in the designer, your progress is saved as a pipeline draft.
Box 3: No
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer
NEW QUESTION 40
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer
NEW QUESTION 41
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-gb/azure/cognitive-services/qnamaker/concepts/data-sources-and-content
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service QnA maker conversational AI service and has nothing to do with SQL database You can easily create a user support bot solution on Microsoft Azure using a combination of two core technologies:
- QnA Maker. This cognitive service enables you to create and publish a knowledge base with built-in natural language processing capabilities.
- Azure Bot Service. This service provides a framework for developing, publishing, and managing bots on Azure.
https://docs.microsoft.com/en-us/learn/modules/build-faq-chatbot-qna-maker-azure-bot-service/2-get-started-qna-bot LUIS is used to understand user intent from utterances.
Creating a language understanding application with Language Understanding consists of two main tasks. First you must define entities, intents, and utterances with which to train the language model - referred to as authoring the model. Then you must publish the model so that client applications can use it for intent and entity prediction based on user input.
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service
NEW QUESTION 42
Which type of machine learning should you use to predict the number of gift cards that will be sold next month?
- A. classification
- B. clustering
- C. regression
Answer: B
Explanation:
Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.
Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize- model-clustering
NEW QUESTION 43
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