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NEW QUESTION 23 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: Explanation Box 1: Key phrase extraction Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations. Box 2: Sentiment analysis Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. Box 3: Translation Using Microsoft's Translator text API This versatile API from Microsoft can be used for the following: Translate text from one language to another. Transliterate text from one script to another. Detecting language of the input text. Find alternate translations to specific text. Determine the sentence length. Reference: https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics   **NEW QUESTION 24 Match the types of AI 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: Explanation Box 3: Natural language processing Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization. Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing   **NEW QUESTION 25 You build a machine learning model by using the automated machine learning user interface (UI). You need to ensure that the model meets the Microsoft transparency principle for responsible AI. What should you do?

• A. Enable Explain best model.
• B. Set Primary metric to accuracy.
• C. Set Validation type to Auto.
• D. Set Max concurrent iterations to 0.

Answer: A Explanation: Section: Describe Artificial Intelligence workloads and considerations Explanation: Model Explain Ability. Most businesses run on trust and being able to open the ML "black box" helps build transparency and trust. In heavily regulated industries like healthcare and banking, it is critical to comply with regulations and best practices. One key aspect of this is understanding the relationship between input variables (features) and model output. Knowing both the magnitude and direction of the impact each feature (feature importance) has on the predicted value helps better understand and explain the model. With model explain ability, we enable you to understand feature importance as part of automated ML runs. Reference: https://azure.microsoft.com/en-us/blog/new-automated-machine-learning-capabilities-in-azure-machine- learning-service/   NEW QUESTION 26 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 Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality. Box 2: No Box 3: Yes During training, Azure Machine Learning creates a number of pipelines in parallel that try different algorithms and parameters for you. The service iterates through ML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to "fit" your data. It will stop once it hits the exit criteria defined in the experiment. Box 4: No Apply automated ML when you want Azure Machine Learning to train and tune a model for you using the target metric you specify. The label is the column you want to predict. Reference: https://azure.microsoft.com/en-us/services/machine-learning/automatedml/#features   **NEW QUESTION 27 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. Translator Text
• C. Face
• D. QnA Maker

Answer: D Explanation: Section: Describe features of conversational AI workloads on Azure 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 Answers: 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. 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. Reference: https://azure.microsoft.com/en-us/services/cognitive-services/qna-maker/   NEW QUESTION 28 ......