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NEW QUESTION 1
You are designing an AI system that empowers everyone, including people who have hearing, visual, and other impairments. This is an example of which Microsoft guiding principle for responsible AI?
A. fairness
B. inclusiveness
C. reliability and safety
D. accountability
Answer: B
Explanation:
Inclusiveness: At Microsoft, we firmly believe everyone should benefit from intelligent technology, meaning it must incorporate and address a broad range of human needs and experiences. For the 1 billion people with disabilities around the world, AI technologies can be a game-changer.
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
NEW QUESTION 2
Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents?
A. Form Recognizer
B. Text Analytics
C. Ink Recognizer
D. Custom Vision
Answer: A
Explanation:
Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.
https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/
NEW QUESTION 3
You use Azure Machine Learning designer to publish an inference pipeline. Which two parameters should you use to consume the pipeline? (Each correct answer presents part of the solution. Choose two.)
A. the model name
B. the training endpoint
C. the authentication key
D. the REST endpoint
Answer: AD
Explanation:
A: The trained model is stored as a Dataset module in the module palette. You can find it under My Datasets. Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models.
D: You can consume a published pipeline in the Published pipelines page. Select a published pipeline and find the REST endpoint of it.
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-run-batch-predictions-designer
https://docs.microsoft.com/enus/azure/machine-learning/concept-designer
NEW QUESTION 4
Which metric can you use to evaluate a classification model?
A. true positive rate
B. mean absolute error (MAE)
C. coefficient of determination (R2)
D. root mean squared error (RMSE)
Answer: A
Explanation:
What does a good model look like? An ROC curve that approaches the top left corner with 100% true positive rate and 0% false positive rate will be the best model. A random model would display as a flat line from the bottom left to the top right corner. Worse than random would dip below the y=x line.
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#classification
NEW QUESTION 5
Which two components can you drag onto a canvas in Azure Machine Learning designer? (Each correct answer presents a complete solution. Choose two.)
A. dataset
B. compute
C. pipeline
D. module
Answer: AD
Explanation:
You can drag-and-drop datasets and modules onto the canvas.
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer
NEW QUESTION 6
Which type of machine learning should you use to predict the number of gift cards that will be sold next month?
A. classification
B. regression
C. clustering
Answer: C
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.
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize-model-clustering
NEW QUESTION 7
You need to predict the sea level in meters for the next 10 years. Which type of machine learning should you use?
A. classification
B. regression
C. clustering
Answer: B
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.
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression
NEW QUESTION 8
You need to develop a mobile app for employees to scan and store their expenses while travelling. Which type of computer vision should you use?
A. semantic segmentation
B. image classification
C. object detection
D. optical character recognition (OCR)
Answer: D
Explanation:
Azure’s Computer Vision API includes Optical Character Recognition (OCR) capabilities that extract printed or handwritten text from images. You can extract text from images, 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
NEW QUESTION 9
What are two tasks that can be performed by using the Computer Vision service? (Each correct answer presents a complete solution. Choose two.)
A. Train a custom image classification model.
B. Detect faces in an image.
C. Recognize handwritten text.
D. Translate the text in an image between languages.
Answer: BC
Explanation:
B: Azure’s Computer Vision service provides developers with 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.
C: Computer Vision includes Optical Character Recognition (OCR) capabilities. You can use the new Read API to extract printed and handwritten text from images and documents.
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/home
NEW QUESTION 10
What are two tasks that can be performed by using computer vision? (Each correct answer presents a complete solution. Choose two.)
A. Predict stock prices.
B. Detect brands in an image.
C. Detect the color scheme in an image.
D. Translate text between languages.
E. Extract key phrases.
Answer: BE
Explanation:
B: 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.
E: Computer Vision includes Optical Character Recognition (OCR) capabilities. You can use the new Read API to extract printed and handwritten text from images and documents. It uses the latest models and works with text on a variety of surfaces and backgrounds. These include receipts, posters, business cards, letters, and whiteboards. The two OCR APIs support extracting printed text in several languages.
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview
NEW QUESTION 11
You are developing a chatbot solution in Azure. Which service should you use to determine a user’s intent?
A. Translator Text
B. QnA Maker
C. Speech
D. Language Understanding (LUIS)
Answer: D
Explanation:
Language Understanding (LUIS) is a cloud-based API service that applies custom machine-learning intelligence to a user’s conversational, natural language text to predict overall meaning, and pull out relevant, detailed information. Design your LUIS model with categories of user intentions called intents. Each intent needs examples of user utterances. Each utterance can provide data that needs to be extracted with machine-learning entities.
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/what-is-luis
NEW QUESTION 12
You are developing a natural language processing solution in Azure. The solution will analyze customer reviews and determine how positive or negative each review is. This is an example of which type of natural language processing workload?
A. language detection
B. sentiment analysis
C. key phrase extraction
D. entity recognition
Answer: B
Explanation:
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-languageprocessing
NEW QUESTION 13
Which two scenarios are examples of a conversational AI workload? (Each correct answer presents a complete solution. Choose two.)
A. a telephone answering service that has a pre-recorder message
B. a chatbot that provides users with the ability to find answers on a website by themselves
C. telephone voice menus to reduce the load on human resources
D. a service that creates frequently asked questions (FAQ) documents by crawling public websites
Answer: BC
Explanation:
B: A bot is an automated software program designed to perform a particular task. Think of it as a robot without a body.
C: Automated customer interaction is essential to a business of any size. In fact, 61% of consumers prefer to communicate via speech, and most of them prefer self-service. Because customer satisfaction is a priority for all businesses, selfservice is a critical facet of any customer-facing communications strategy.
Incorrect:
Not D: Early bots were comparatively simple, handling repetitive and voluminous tasks with relatively straightforward algorithmic logic. An example would be web crawlers used by search engines to automatically explore and catalog web content.
https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/ai-overview
https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/interactive-voice-response-bot
NEW QUESTION 14
You need to develop a web-based AI solution for a customer support system. Users must be able to interact with a web app that will guide them to the best resource or answer. Which service should you use?
A. Custom Vision
B. QnA Maker
C. Translator Text
D. Face
Answer: B
Explanation:
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.
Incorrect:
Not A: Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent classes) to images, according to their visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify the labels to apply.
Not D: Azure Cognitive Services Face Detection API: At a minimum, each detected face corresponds to a faceRectangle field in the response. This set of pixel coordinates for the left, top, width, and height mark the located face. Using these coordinates, you can get the location of the face and its size. In the API response, faces are listed in size order from largest to smallest.
https://azure.microsoft.com/en-us/services/cognitive-services/qna-maker/
NEW QUESTION 15
Hotspot
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
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.
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
NEW QUESTION 16
Drag and Drop
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.)
Answer:
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.
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.
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
NEW QUESTION 17
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