[2022] Use Valid New AI-900 Questions - Top choice Help You Gain Success
AI-900 Exam Practice Materials Collection
NEW QUESTION 43
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
Content Moderator is part of Microsoft Cognitive Services allowing businesses to use machine assisted moderation of text, images, and videos that augment human review.
The text moderation capability now includes a new machine-learning based text classification feature which uses a trained model to identify possible abusive, derogatory or discriminatory language such as slang, abbreviated words, offensive, and intentionally misspelled words for review.
Box 2: No
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.
Box 3: Yes
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://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
NEW QUESTION 44
You need to provide content for a business chatbot that will help answer simple user queries.
What are three ways to create question and answer text by using QnA Maker? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. Generate the questions and answers from an existing webpage.
- B. Connect the bot to the Cortana channel and ask questions by using Cortana.
- C. Import chit-chat content from a predefined data source.
- D. Use automated machine learning to train a model based on a file that contains the questions.
- E. Manually enter the questions and answers.
Answer: A,C,E
Explanation:
Explanation
Automatic extraction
Extract question-answer pairs from semi-structured content, including FAQ pages, support websites, excel files, SharePoint documents, product manuals and policies.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/content-types
NEW QUESTION 45
What are three Microsoft guiding principles for responsible AI? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. inclusiveness
- B. reliability and safety
- C. decisiveness
- D. opinionatedness
- E. knowledgeability
- F. fairness
Answer: A,B,F
Explanation:
Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
NEW QUESTION 46
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.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression Regression is a form of machine learning that is used to predict a numeric label based on an item's features.
https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/introduction
NEW QUESTION 47
You use natural language processing to process text from a Microsoft news story.
You receive the output shown in the following exhibit.
Which type of natural languages processing was performed?
- A. entity recognition
- B. key phrase extraction
- C. translation
- D. sentiment analysis
Answer: A
Explanation:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview
NEW QUESTION 48
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
The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, named entity recognition, and language detection.
Box 1: Yes
You can detect which language the input text is written in and report a single language code for every document submitted on the request in a wide range of languages, variants, dialects, and some regional/cultural languages. The language code is paired with a score indicating the strength of the score.
Box 2: No
Box 3: Yes
Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview
NEW QUESTION 49
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-us/azure/cognitive-services/custom-vision-service/get-started-build-detector
NEW QUESTION 50
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://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview/
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
NEW QUESTION 51
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
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-manage-channels?view=azure-bot-service-4.0
NEW QUESTION 52
In which two scenarios can you use the Form Recognizer service? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. Translate a form from French to English.
- B. Find image of product in a catalog.
- C. Extract the invoice number from an invoice.
- D. Identity the retailer from a receipt.
Answer: C,D
Explanation:
Section: Describe features of computer vision workloads on Azure
Explanation/Reference:
https://azure.microsoft.com/en-gb/services/cognitive-services/form-recognizer/#features
NEW QUESTION 53
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-us/azure/machine-learning/concept-designer
NEW QUESTION 54
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:
NEW QUESTION 55
You use natural language processing to process text from a Microsoft news story.
You receive the output shown in the following exhibit.
Which type of natural languages processing was performed?
- A. entity recognition
- B. key phrase extraction
- C. translation
- D. sentiment analysis
Answer: A
Explanation:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. 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.
https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure
NEW QUESTION 56
For a machine learning progress, how should you split data for training and evaluation?
- A. Randomly split the data into rows for training and rows for evaluation.
- B. Use features for training and labels for evaluation.
- C. Randomly split the data into columns for training and columns for evaluation.
- D. Use labels for training and features for evaluation.
Answer: A
Explanation:
Section: Describe Artificial Intelligence workloads and considerations
NEW QUESTION 57
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:
The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, named entity recognition, and language detection.
Box 1: Yes
You can detect which language the input text is written in and report a single language code for every document submitted on the request in a wide range of languages, variants, dialects, and some regional/cultural languages. The language code is paired with a score indicating the strength of the score.
Box 2: No
Box 3: Yes
Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview
NEW QUESTION 58
For a machine learning progress, how should you split data for training and evaluation?
- A. Randomly split the data into rows for training and rows for evaluation.
- B. Use features for training and labels for evaluation.
- C. Randomly split the data into columns for training and columns for evaluation.
- D. Use labels for training and features for evaluation.
Answer: A
Explanation:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/split-data
NEW QUESTION 59
You need to provide content for a business chatbot that will help answer simple user queries.
What are three ways to create question and answer text by using QnA Maker? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. Generate the questions and answers from an existing webpage.
- B. Connect the bot to the Cortana channel and ask questions by using Cortana.
- C. Import chit-chat content from a predefined data source.
- D. Use automated machine learning to train a model based on a file that contains the questions.
- E. Manually enter the questions and answers.
Answer: A,C,E
Explanation:
Section: Describe features of conversational AI workloads on Azure
Explanation:
Automatic extraction
Extract question-answer pairs from semi-structured content, including FAQ pages, support websites, excel files, SharePoint documents, product manuals and policies.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/content-types
NEW QUESTION 60
What are two tasks that can be performed by using computer vision? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. Extract key phrases.
- B. Translate text between languages.
- C. Predict stock prices.
- D. Detect the color scheme in an image
- E. Detect brands in an image.
Answer: A,E
Explanation:
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.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview
NEW QUESTION 61
In which two scenarios can you use the Form Recognizer service? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. Translate a form from French to English.
- B. Find image of product in a catalog.
- C. Extract the invoice number from an invoice.
- D. Identity the retailer from a receipt.
Answer: C,D
Explanation:
Reference:
https://azure.microsoft.com/en-gb/services/cognitive-services/form-recognizer/#features
NEW QUESTION 62
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. Set Max concurrent iterations to 0.
- B. Set Validation type to Auto.
- C. Enable Explain best model.
- D. Set Primary metric to accuracy.
Answer: C
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 63
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
Reference:
https://docs.microsoft.com/en-us/learn/paths/get-started-with-artificial-intelligence-on-azure/
NEW QUESTION 64
You have a frequently asked questions (FAQ) PDF file.
You need to create a conversational support system based on the FAQ.
Which service should you use?
- A. Language Understanding (LUIS)
- B. Text Analytics
- C. Computer Vision
- D. QnA Maker
Answer: D
Explanation:
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.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/qna-maker/
NEW QUESTION 65
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