Reference Solution for ai-900.vce AI-900 Microsoft Azure AI Fundamentals (beta) Version 1.0 Score: 800/1000.

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Reference Solution for ai-900.vce AI-900 Microsoft Azure AI Fundamentals (beta) Version 1.0 Score: 800/1000 Exam A (62 questions) Question 1 HOTSPOT For each of the following statements, s... elect Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Solution: Explanation: Explanation: Box 1: Yes In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict. In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing. 3 Licensed to Examcollection Premium Box 2: No Box 3: No Accuracy is simply the proportion of correctly classified instances. It is usually the first metric you look at when evaluating a classifier. However, when the test data is unbalanced (where most of the instances belong to one of the classes), or you are more interested in the performance on either one of the classes, accuracy doesn't really capture the effectiveness of a classifier. Reference: https://www.cloudfactory.com/data-labeling-guide https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance Question 2 DRAG 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. NOTE: Each correct selection is worth one point. 4 Licensed to Examcollection Premium Solution: 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: 5 Licensed to Examcollection Premium https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles Question 3 Your website has a chatbot to assist customers. You need to detect when a customer is upset based on what the customer types in the chatbot. Which type of AI workload should you use? o anomaly detection o semantic segmentation o regression ý natural language processing Explanation: Explanation: Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization. Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/naturallanguage-processing Question 4 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? o language detection ý sentiment analysis o key phrase extraction o entity recognition Explanation: Explanation: Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. 6 Licensed to Examcollection Premium Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/naturallanguage-processing Question 5 HOTSPOT To complete the sentence, select the appropriate option in the answer area. Solution: Explanation: Explanation: With Microsoft’s Conversational AI tools developers can build, connect, deploy, and manage intelligent bots that naturally interact with their users on a website, app, Cortana, Microsoft Teams, Skype, Facebook Messenger, Slack, and more. Reference: 7 Licensed to Examcollection Premium https://azure.microsoft.com/en-in/blog/microsoft-conversational-ai-tools-enable-developers-tobuild-connect-and-manage-intelligent-bots Question 6 DRAG DROP Match the machine learning tasks to the appropriate scenarios. To answer, drag the appropriate task from the column on the left to its scenario on the right. Each task may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Solution: Explanation: Explanation: Box 1: Model evaluation The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, as well as ROC, Precision/Recall, and Lift curves. 8 Licensed to Examcollection Premium Box 2: Feature engineering Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization. Note: Often, features are created from raw data through a process of feature engineering. For example, a time stamp in itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day. Box 3: 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. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml Question 7 Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents? ý Form Recognizer o Text Analytics o Ink Recognizer o Custom Vision Explanation: 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 onpremises 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. Reference: 9 Licensed to Examcollection Premium [Show More]

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