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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Fundamental principles of machine learning on Azure | 15–20% | - Describe capabilities of Azure Machine Learning - Describe machine learning pipelines - Describe core concepts of machine learning - Describe automated machine learning |
| Topic 2: Features of generative AI workloads on Azure | 20–25% | - Describe responsible AI practices for generative AI - Describe generative AI concepts - Describe use cases for generative AI - Describe capabilities of Azure OpenAI Service |
| Topic 3: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Describe capabilities of Azure Translator - Describe capabilities of Azure Speech - Identify types of NLP solutions |
| Topic 4: Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Describe responsible AI principles - Identify types of AI workloads |
| Topic 5: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Face - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Computer Vision - Identify types of computer vision solutions |
>> Latest AI-900 Braindumps Questions <<
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NEW QUESTION # 283
Match the tool to the Azure Machine Learning task.
To answer, drag the appropriate tool from the column on the left to its tasks on the right. Each tool may be used once, more than once, or not at all NOTE: Each correct match is worth one point.
Answer:
Explanation:
NEW QUESTION # 284
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore natural language processing (NLP) in Azure", Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. NLP is used to extract meaning and intent from text or speech, perform sentiment analysis, identify entities, and classify content based on context.
One of the primary applications of NLP is text classification, where an AI model automatically categorizes text documents or messages into predefined classes. Classifying emails as work-related or personal is a textbook example of this NLP capability. It involves analyzing the words, phrases, and structure of the text to determine the email's category. Microsoft Learn highlights this type of problem as document classification, an essential NLP use case often implemented through Azure Cognitive Services such as Text Analytics or Language Studio.
Let's examine why the other options are incorrect:
* Predict the number of future car rentals - This is a time series forecasting or regression task, not NLP.
* Predict which website visitors will make a transaction - This is a predictive analytics or machine learning classification problem based on behavioral data, not language understanding.
* Stop a process in a factory when extremely high temperatures are registered - This relates to IoT automation or sensor-based anomaly detection, not NLP.
Therefore, based on Microsoft's AI-900 materials, Natural Language Processing is best used for tasks involving understanding and classifying text, such as classifying email messages as work-related or personal.
This example perfectly aligns with NLP's goal-to enable machines to process and derive insights from human language inputs.
NEW QUESTION # 285
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:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of Natural Language Processing (NLP) workloads and services," the Azure Cognitive Service for Language - Question Answering capability is designed to allow applications to respond to user questions using information from a prebuilt or custom knowledge base. It relies on Natural Language Processing (NLP) to match user queries to the most relevant answers but does not directly execute queries against databases or infer user intent.
* "You can use Language Service's question answering to query an Azure SQL database." # NOThe Question Answering feature does not connect directly to or query structured databases such as Azure SQL. Instead, it retrieves answers from unstructured or semi-structured content (FAQs, manuals, documents). Querying SQL databases would require traditional database access, not a cognitive service.
* "You should use Language Service's question answering when you want a knowledge base to provide the same answer to different users who submit similar questions." # YESThis statement is correct and aligns exactly with Microsoft's official documentation. Question Answering enables organizations to create a knowledge base that can automatically answer repeated or similar customer queries using natural language understanding. For instance, two users asking "How do I reset my password?" and
"Can you help me change my password?" would receive the same predefined response.
* "Language Service's question answering can determine the intent of a user utterance." # NODetermining user intent is handled by Language Understanding (LUIS) or Conversational Language Understanding, not by Question Answering. While both belong to the Language Service, Question Answering focuses on retrieving relevant answers, whereas LUIS focuses on intent detection and entity extraction.
NEW QUESTION # 286
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:
# Yes - Extract key phrases
# No - Generate press releases
# Yes - Detect sentiment
The Azure AI Language service is a powerful set of natural language processing (NLP) tools within Azure Cognitive Services, designed to analyze, understand, and interpret human language in text form. According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation, this service includes several capabilities such as key phrase extraction, sentiment analysis, language detection, named entity recognition (NER), and question answering.
* Extract key phrases from documents # YesThe Key Phrase Extraction feature identifies the most relevant words or short phrases within a document, helping summarize important topics. This is useful for indexing, summarizing, or organizing content. For instance, from "Azure AI Language helps analyze customer feedback," it may extract "Azure AI Language" and "customer feedback" as key phrases.
* Generate press releases based on user prompts # NoThis functionality falls under generative AI, specifically within Azure OpenAI Service, which uses models such as GPT-4 for text creation. The Azure AI Language service focuses on analyzing and understanding existing text, not generating new content like press releases or articles.
* Build a social media feed analyzer to detect sentiment # YesThe Sentiment Analysis capability determines the emotional tone (positive, neutral, negative, or mixed) of text data, making it ideal for analyzing social media posts, reviews, or feedback. Businesses often use this to gauge customer satisfaction or brand reputation.
In summary, the Azure AI Language service analyzes text to extract insights and detect sentiment but does not generate new textual content.
NEW QUESTION # 287
Which feature of the Azure Al Language service should you use to automate the masking of names and phone numbers in text data?
Answer: A
NEW QUESTION # 288
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