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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Passing Score: | 700 (on a scale of 1–1000) |
| Exam Format: | Multiple select, Scenario-based questions, Multiple choice |
| Certificate Validity Period: | Valid indefinitely |
| Exam Duration: | 45 minutes |
| Exam Price: | $99 USD |
| Available Languages: | French, Indonesian (Indonesia), Japanese, English, Spanish, Arabic (Saudi Arabia), Italian, German, Russian, Portuguese (Brazil), Chinese (Traditional), Korean, Chinese (Simplified) |
| Related Certifications: | Microsoft Certified: Azure Fundamentals Microsoft Certified: Azure Data Fundamentals |
| Real Exam Qty: | 40–60 |
| Recommended Training: | Microsoft Learn: Azure AI Fundamentals Learning Path Instructor-led Training: AI-900 Course |
| Exam Registration: | Pearson VUE Registration Microsoft Certification Registration |
| Sample Questions: | Microsoft AI-900 Sample Questions |
| Exam Way: | Online proctored or onsite testing at Pearson VUE test centers |
| Pre Condition: | No required prerequisites; basic familiarity with cloud computing or AI concepts is recommended but not mandatory |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-900/ |
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The AI-900 Certification Exam tests the candidates' knowledge of AI concepts, such as machine learning, cognitive services, computer vision, and natural language processing. It also evaluates their ability to implement AI solutions using Azure AI services. AI-900 exam is intended for individuals who are interested in learning about AI and want to explore the possibilities of AI in their respective fields.
Microsoft AI-900 exam is a great starting point for individuals who want to pursue a career in artificial intelligence and machine learning. It provides a solid foundation for understanding the basic concepts and principles of these technologies and how they can be applied in real-world scenarios. Microsoft Azure AI Fundamentals certification also demonstrates to employers that you have the knowledge and skills necessary to work in this field.
NEW QUESTION # 257
You need to develop a chatbot for a website. The chatbot must answer users' questions based on the information in the following documents:
A product troubleshooting guide in a Microsoft Word document
A frequently asked questions (FAQ) list on a webpage
Which service should you use to process the documents?
Answer: A
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/QnAMaker/Overview/overview
NEW QUESTION # 258
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/studio-module-reference/anomaly-detection
NEW QUESTION # 259
Match the Azure OpenAI large language model (LLM) process to the appropriate task.
To answer, drag the appropriate process from the column on the left to its task on the right. Each process may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) study material and Azure OpenAI Service documentation, large language models (LLMs) such as GPT are capable of performing multiple natural language processing (NLP) tasks depending on the intent of the prompt. These tasks generally fall into categories like classification, generation, summarization, and translation, each with a distinct purpose and output type.
* Classifying - This process involves analyzing text and assigning it to a predefined category or label based on its content. The scenario "Detect the genre of a work of fiction" clearly fits this category. The model must evaluate the text and determine whether it belongs to genres like mystery, romance, or science fiction. This is a classic text classification problem, as the output is a discrete category derived from textual features.
* Summarizing - This process means condensing lengthy text into a shorter version that preserves the key information. In the scenario "Create a list of bullet points based on text input," the model extracts essential information and reformats it as concise bullet points, which is an abstraction form of summarization. Summarization models help users quickly understand the main ideas from long documents, meeting efficiency and readability goals.
* Generating - This refers to the LLM's ability to produce new, creative content based on input instructions. The task "Create advertising slogans from a product description" represents generation because it requires the model to construct original text that didn't previously exist. Generation tasks showcase the creativity and contextual fluency of models like GPT in marketing and content creation.
Thus, these mappings align directly with the Azure OpenAI LLM capabilities taught in AI-900, linking each NLP process with its most suitable real-world task.
NEW QUESTION # 260
You have the following apps:
* App1: Understands the public perception of a brand or topic
* App2: Applies profanity filters to speech-to-text
What does each app use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 261
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.
Answer:
Explanation:
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
NEW QUESTION # 262
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