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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals (AI-900) Exam |
| Exam Number: | AI-900 |
| Available Languages: | Spanish, English, Chinese (Traditional), French, Chinese (Simplified), German, Portuguese (Brazil), Korean, Japanese |
| Related Certifications: | Microsoft Azure Data Fundamentals (DP-900) Microsoft Azure Fundamentals (AZ-900) |
| Passing Score: | 700 (out of 1000) |
| Exam Price: | Approx. 99 USD (varies by region) |
| Real Exam Qty: | 40-60 |
| Exam Format: | Case study (limited), Drag and drop, Multiple choice, Multiple response |
| Exam Duration: | 60 minutes |
| Certificate Validity Period: | Does not expire (Fundamentals certification) |
| 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 # 109
Which natural language processing feature can be used to identify the main talking points in customer feedback surveys?
Answer: D
Explanation:
To identify main talking points in feedback using Azure natural language processing, the Key Phrase Extraction feature within the Azure AI Language service is the most appropriate choice.
This feature analyzes text and extracts the most relevant and important phrases, effectively highlighting the core topics or themes present in the feedback.
Reference:
https://learn.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
NEW QUESTION # 110
What are two purposes of instructions when prompting a generative AI model? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
Giving a generative AI model clear, structural instructions (commonly referred to as Prompt Engineering) is the standard method used to define its persona, guide its behavior, and place strict operational boundaries on its responses.This is typically achieved through explicit instruction sets like System Instructions and targeted Prompt Guidelines.
1. Defining the Agent's Role and BehaviorEstablishing a persona prevents the AI from wandering into areas outside its intended scope and ensures the delivery style matches your needs.
2. Defining Constraints on ResponsesConstraints act as guardrails that prevent the AI from hallucinating, rambling, or providing incorrect information.
Reference:
https://ai.google.dev/gemini-api/docs/prompting-strategies
NEW QUESTION # 111
You are developing an application that continuously transcribes speech from a default microphone by using Azure Speech in Microsoft Foundry Tools.
You need to perform speech-to-text transcription from the microphone.
Which Python class should you use?
Answer: A
NEW QUESTION # 112
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: analyzes images to detect
A multimodal model analyzes images to detect, recognize, and reason about objects by combining computer vision with natural language processing.
Reference:
https://farukalamai.substack.com/p/object-detection-with-multimodal
NEW QUESTION # 113
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:
Explanation:
The completed code is:
poller = client.begin_analyze(
analyzer_id= " invoice " ,
input_url=url
)
result = poller.result()
Azure Content Understanding analysis uses a long-running operation pattern. The Python SDK returns a poller from begin_analyze(), and Microsoft documentation states that the SDK poller handles polling automatically when you call .result().
Therefore, to retrieve the analysis results after processing completes, the correct option is:
result
The other options are incorrect because status checks operation state, wait waits without returning the final analysis object, and get_results is not the method shown for retrieving the begin_analyze() result in this code pattern.
NEW QUESTION # 114
......
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