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Microsoft AI-900 덤프구매전 한국어 온라인상담서비스부터 구매후 덤프 무료 업데이트버전제공 , Microsoft AI-900시험불합격시 덤프비용 전액환불 혹은 다른 과목으로 교환 등 저희는 구매전부터 구매후까지 철저한 서비스를 제공해드립니다. Microsoft AI-900 덤프는 인기덤프인데 지금까지 덤프를 구매한후 환불신청하신 분은 아직 없었습니다.
Microsoft AI-900 인증 시험은 Azure AI 기술을 개발하려는 개인에게 훌륭한 출발점입니다. 시험을 통과하면 AI 및 ML 개념에 대한 지식을 보여 주며 실제 문제를 해결하는 데 이러한 기술이 어떻게 사용될 수 있는지 더 잘 이해할 수 있습니다. 또한 Azure AI 서비스 및 도구를 사용하여 데이터를 분석하고 음성을 인식하며 자연어를 이해할 수있는 지능형 응용 프로그램을 구축하는 방법을 배웁니다.
Microsoft Azure AI 기초라고도하는 Microsoft AI-900은 개인의 인공 지능 (AI) 개념을 테스트하는 것을 목표로하는 인증 시험입니다. 이 시험에는 기계 학습, 자연어 처리, 컴퓨터 비전 및인지 서비스를 포함한 다양한 주제가 다룹니다. 이 인증은 AI의 기본 사항과 비즈니스 문제를 해결하는 데 사용되는 방법을 이해하려는 전문가를 위해 설계되었습니다.
지금 사회에 능력자들은 아주 많습니다.it인재들도 더욱더 많아지고 있습니다.많은 it인사들은 모두 관연 it인증시험에 참가하여 자격증취득을 합니다.자기만의 자리를 확실히 지키고 더 높은 자리에 오르자면 필요한 스펙이니까요.AI-900시험은Microsoft인증의 중요한 시험이고 또 많은 it인사들은Microsoft자격증을 취득하려고 노력하고 있습니다.
시험은 60 분 안에 완료 해야하는 40-60 개의 객관식 질문으로 구성됩니다. 이 질문은 AI 워크로드 및 고려 사항, 기계 학습의 기본 원칙, 컴퓨터 비전, 자연 언어 처리 및 대화식 AI의 지식과 기술을 테스트하도록 설계되었습니다. 시험에 합격하려면 1000 명 중 700 점 이상을 기록해야합니다.
질문 # 192
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?
정답：A
설명：
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/natural-language-processing
질문 # 193
You plan to apply Text Analytics API features to a technical support ticketing system.
Match the Text Analytics API features to the appropriate natural language processing scenarios.
To answer, drag the appropriate feature from the column on the left to its scenario on the right. Each feature may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
Box1: Sentiment analysis
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Box 2: Broad entity extraction
Broad entity extraction: Identify important concepts in text, including key Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
Box 3: Entity Recognition
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/architecture/data-guide/technology-choices/natural-language- processing
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
질문 # 194
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
* "The Azure OpenAI GPT-3.5 Turbo model can transcribe speech to text." - NOThis statement is false.
The GPT-3.5 Turbo model is a text-based large language model (LLM) designed for natural language understanding and generation, such as answering questions, summarizing text, or writing content. It does not process or transcribe audio input. Speech-to-text capabilities belong to Azure AI Speech Services, specifically the Speech-to-Text API, not Azure OpenAI.
* "The Azure OpenAI DALL-E model generates images based on text prompts." - YESThis statement is true. The DALL-E model, available within Azure OpenAI Service, is a generative AI model that creates original images from natural language descriptions (text prompts). For example, given a prompt like "a futuristic city at sunset," DALL-E generates a unique, high-quality image representing that concept. This aligns with generative AI workloads in the AI-900 study guide, where DALL-E is specifically mentioned as an image-generation model.
* "The Azure OpenAI embeddings model can convert text into numerical vectors based on text similarities." - YESThis statement is also true. The embeddings model in Azure OpenAI converts text into multi-dimensional numeric vectors that represent semantic meaning. These embeddings enable tasks such as semantic search, recommendations, and text clustering by comparing similarity scores between vectors. Words or phrases with similar meanings have vectors close together in the embedding space.
In summary:
* GPT-3.5 Turbo # Text generation (not speech-to-text)
* DALL-E # Image generation from text prompts
* Embeddings # Convert text into numerical semantic representations
Correct selections: No, Yes, Yes.
질문 # 195
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
정답：
설명：
질문 # 196
Select the answer that correctly completes the sentence.
정답：
설명：
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) study materials and Microsoft's Responsible AI guidelines, customers must obtain approval based on their intended usage before accessing and deploying Azure OpenAI Service. This requirement ensures that Microsoft upholds its commitment to Responsible AI principles, which include fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
The Azure OpenAI Service provides access to powerful language models such as GPT series and Codex, which can generate, summarize, and understand natural language and code. Because of the potential for misuse-such as generating harmful content, misinformation, or unethical automation-Microsoft enforces a use case review and approval process before granting customers access to the service. This process involves submitting an application describing the intended purpose, deployment method, and compliance measures.
Only after Microsoft validates that the proposed use aligns with responsible AI practices will access be approved.
This aligns with Microsoft's documented commitment that "customers are required to submit an application that describes their intended use of the Azure OpenAI Service," ensuring that all deployments follow ethical and legal standards. This approval step helps maintain transparency and prevent harmful or non-compliant use cases such as deepfake generation, biased automation, or malicious chatbot deployment.
Other options listed in the question are incorrect:
* Commit to a minimum level of expenditure - Microsoft does not require financial commitments for ethical approval.
* Pay an upfront fee - Payment is handled through normal Azure billing, not a special fee.
* Provide credit card details - Not a responsible AI requirement; this is standard for any Azure subscription.
Therefore, the correct and verified answer per Microsoft's Responsible AI framework and Azure AI-900 study
질문 # 197
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