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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals (AI-900) Exam |
| Exam Number: | AI-900 |
| Passing Score: | 700 (out of 1000) |
| Exam Duration: | 60 minutes |
| Exam Format: | Multiple response, Multiple choice, Drag and drop, Case study (limited) |
| Related Certifications: | Microsoft Azure Data Fundamentals (DP-900) Microsoft Azure Fundamentals (AZ-900) |
| Certificate Validity Period: | Does not expire (Fundamentals certification) |
| Available Languages: | German, English, Chinese (Traditional), Japanese, Korean, Portuguese (Brazil), Chinese (Simplified), Spanish, French |
| Real Exam Qty: | 40-60 |
| Exam Price: | Approx. 99 USD (varies by region) |
| Recommended Training: | Microsoft Learn - AI-900 Learning Path Azure AI Fundamentals Course |
| Exam Registration: | Schedule exam via Pearson VUE Microsoft Certification Portal |
| Sample Questions: | Microsoft AI-901 Sample Questions |
| Exam Way: | Online proctored exam or in-person test center |
| Pre Condition: | No formal prerequisites required. Basic understanding of cloud computing and AI concepts is recommended. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-fundamentals/ |
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NEW QUESTION # 131
You use natural language processing to process text from a Microsoft news story. You receive the output shown in the following table.
Which type of natural language processing was performed?
Answer: C
Explanation:
The output identifies text values and labels them with entity categories, such as:
now [DateTime]
students [PersonType]
Microsoft [Organization]
175 [Quantity-Number]
Today [DateTime-Date]
This is entity recognition, also known as Named Entity Recognition (NER). NER identifies and categorizes entities in unstructured text, such as people, organizations, locations, dates, quantities, numbers, and other named or structured values.
Why the other options are incorrect:
B). translation would convert text from one language to another.
C). key phrase extraction would return important phrases or main topics, not categorized entity labels.
D). sentiment analysis would return sentiment such as positive, negative, neutral, or mixed.
NEW QUESTION # 132
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The correct answer is natural language processing . In this scenario, a user communicates with a chatbot using ordinary human language, such as asking, "Will it rain today?" The chatbot must interpret the meaning and intent of the user ' s text, process the request, and formulate a natural-language response containing the relevant weather information. These are characteristic natural language processing (NLP) functions.
The current AI-901 study guide explicitly includes text analysis among the common AI workloads candidates must identify, and it also covers techniques for processing and interpreting human language. Microsoft documentation on conversational language understanding explains that conversational systems interpret user utterances to determine the user ' s intent and extract relevant information before the application performs an appropriate action.
Although the weather information itself may originate from a forecasting system, the scenario being tested centers on the chatbot interaction -understanding a natural-language question and responding conversationally. Therefore, prediction and forecasting is not the best classification of the complete interaction. Computer vision is also incorrect because no images or visual information are being analyzed.
NEW QUESTION # 133
Hotspot Question
You are developing an application that analyze invoices by using Azure Content Understanding in Foundry Tools.
You need to ensure that the application retrieves the analysis results after processing completes.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 134
You have a Microsoft Foundry project that contains a vision-enabled model deployment.
You are developing an application that sends images to the model.
You need to ensure that the model can analyze the images.
In which two formats can you provide the images? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
For vision-enabled Azure OpenAI / Microsoft Foundry model requests, image input can be provided by using an image URL or base64-encoded image data. Microsoft's Azure OpenAI REST API reference states that the image content part URL field can contain either a URL of the image or the base64 encoded image data. It also states that the Responses API input_image.image_url value can be a fully qualified URL or a base64 encoded image in a data URL.
NEW QUESTION # 135
Capturing text from images is an example of which type of Al capability?
Answer: A
Explanation:
Capturing or reading text from images is optical character recognition (OCR) . OCR is used to detect and extract printed or handwritten text from images, scanned documents, and other visual content.
NEW QUESTION # 136
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