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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Passing Score: | 700 / 1000 |
| Certificate Validity Period: | 1 year (renewable via free online assessment on Microsoft Learn) |
| Related Certifications: | Microsoft Certified: Azure AI Fundamentals |
| Real Exam Qty: | 40-60 |
| Exam Duration: | 45-60 |
| Exam Format: | Scenario-based, Drag and drop, Match the service to use case, Multiple choice, Multiple select |
| Available Languages: | French, German, English, Korean, Chinese (Simplified), Arabic (Saudi Arabia), Indonesian (Indonesia), Japanese, Russian, Italian, Spanish, Portuguese (Brazil), Chinese (Traditional) |
| Exam Price: | USD 99 |
| Sample Questions: | Microsoft AI-901 Sample Questions |
| Exam Way: | Online (remote proctored via Pearson VUE) or at a Pearson VUE test center |
| Pre Condition: | No prerequisites. This exam is intended for both technical and non-technical backgrounds. Data science and software engineering experience are not required. Awareness of basic cloud concepts and client-server applications is beneficial. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-900 |
This updated Microsoft AI-901 exam study material of 2Pass4sure consists of these 3 formats: Microsoft AI-901 PDF, desktop practice test software, and web-based practice exam. Each format of 2Pass4sure aids a specific preparation style and offers unique advantages, each of which is beneficial for strong Microsoft Azure AI Fundamentals (AI-901) exam preparation. The features of our three formats are listed below. You can choose any format as per your practice needs.
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NEW QUESTION # 45
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
When content is submitted to Azure Content Understanding in Foundry Tools, the analysis is asynchronous .
This means the service does not return results immediately within the same HTTP request. Instead, it uses the standard Azure long-running operation (LRO) pattern - you call begin_analyze() to submit the content, which immediately returns a poller object, and then call poller.result() to wait for processing to complete and retrieve the structured extraction results.
Why the other options are wrong:
* Synchronous is incorrect - the analysis pipeline involves multiple AI steps (OCR, speech transcription, schema mapping) that take time; a blocking synchronous call is not supported.
* Returned only as unstructured plain text is incorrect - Azure Content Understanding returns richly structured JSON output with named fields mapped to your defined schema, not plain unstructured text.
* Limited to OCR-only processing is incorrect - Content Understanding goes far beyond OCR; it supports document, audio, image, and video analyzers, and performs semantic field extraction using AI, not just character recognition.
This asynchronous design is consistent across all Azure AI services that perform complex, multi-step content processing.
NEW QUESTION # 46
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 # 47
Which Azure Machine Learning capability should you use to quickly build and deploy a predictive model without extensive coding?
Answer: C
Explanation:
To easily build and deploy a predictive model using Azure Machine Learning, start by selecting the appropriate tools for your task, such as Azure Machine Learning Studio or AutoML, and follow a streamlined process of data preparation, model training, and deployment.
AutoML stands for Automated Machine Learning. It's a process that automates the various tasks involved in building machine learning models, including data preparation, feature selection, model training, hyperparameter tuning, and model evaluation, according to IBM. This automation makes machine learning more accessible to a wider range of users, including those without extensive machine learning expertise.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-automated-ml?view=azureml-api-2
NEW QUESTION # 48
Which Azure AI Foundry Service can be used to identify documents that contain sensitive information?
Answer: B
Explanation:
Azure AI Language can be used to identify documents containing sensitive information through its Personally Identifiable Information (PII) detection feature, which uses Named Entity Recognition (NER) to detect and categorize data like names, addresses, phone numbers, and financial details within unstructured text and native documents. This feature classifies sensitive data into predefined categories and can also redact the identified information, replacing it with asterisks to protect privacy.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/personally-identifiable- information/overview
NEW QUESTION # 49
Select the answer that correct completes the sentence.
Answer:
Explanation:
Explanation:
Azure Text Analytics client library
The completed sentence is:
To develop an application that analyzes text by using Azure Language in Foundry Tools, use the Azure Text Analytics client library package.
Azure Language text analysis features include sentiment analysis, key phrase extraction, named entity recognition, language detection, and other text analytics capabilities. These are accessed by using the Azure Text Analytics client library.
The highlighted Azure Speech SDK option is incorrect because it is used for speech workloads, such as speech recognition and speech synthesis, not text analysis.
NEW QUESTION # 50
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