Test AI-901 Testking - Exam Sample AI-901 Questions

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Microsoft AI-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Describe Artificial Intelligence workloads and considerations (15-20%)	15-20%	- Identify guiding principles for responsible AI 1. Describe considerations for transparency in an AI solution 2. Describe considerations for fairness in an AI solution 3. Describe considerations for inclusiveness in an AI solution 4. Describe considerations for reliability and safety in an AI solution 5. Describe considerations for privacy and security in an AI solution 6. Describe considerations for accountability in an AI solution - Identify features of common AI workloads 1. Identify features of knowledge mining workloads 2. Identify features of generative AI workloads 3. Identify features of Natural Language Processing (NLP) workloads 4. Identify features of document intelligence workloads 5. Identify features of content moderation and moderation workloads 6. Identify features of computer vision workloads
Topic 2: Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)	15-20%	- Identify features of common NLP workload scenarios 1. Identify features and uses for speech recognition and synthesis 2. Identify features and uses for language modeling 3. Identify features and uses for sentiment analysis 4. Identify features and uses for entity recognition 5. Identify features and uses for translation 6. Identify features and uses for key phrase extraction - Identify Azure tools and services for NLP workloads 1. Describe capabilities of the Azure AI Speech service 2. Describe capabilities of the Azure AI Language service
Topic 3: Describe features of computer vision workloads on Azure (15-20%)	15-20%	- Identify common types of computer vision solution 1. Identify features of face detection and identification solutions 2. Identify features of optical character recognition (OCR) solutions 3. Identify features of semantic segmentation solutions 4. Identify features of object detection solutions 5. Identify features of image classification solutions 6. Identify features of image analysis solutions - Identify Azure tools and services for computer vision tasks 1. Describe capabilities of the Azure AI Vision service 2. Describe capabilities of the Azure AI Face detection service
Topic 4: Describe features of generative AI workloads on Azure (20-25%)	20-25%	- Identify features of generative AI solutions 1. Identify common scenarios for generative AI 2. Identify features of generative AI models 3. Identify responsible AI considerations for generative AI - Identify generative AI services and capabilities in Microsoft Azure 1. Describe features and capabilities of Azure OpenAI service 2. Describe features and capabilities of Azure AI Foundry model catalog 3. Describe features and capabilities of Azure AI Foundry
Topic 5: Describe fundamental principles of machine learning on Azure (15-20%)	15-20%	- Describe Azure Machine Learning capabilities 1. Describe data and compute services for data science and machine learning 2. Describe model management and deployment capabilities in Azure Machine Learning 3. Describe capabilities of automated machine learning - Identify common machine learning techniques 1. Identify classification machine learning scenarios 2. Identify features of deep learning techniques 3. Identify clustering machine learning scenarios 4. Identify regression machine learning scenarios - Describe core machine learning concepts 1. Describe how training and validation datasets are used in machine learning 2. Identify features and labels for training data in machine learning

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Pass4cram provides proprietary preparation guides for the certification exam offered by the AI-901 exam dumps. In addition to containing numerous questions similar to the AI-901 Exam, the Microsoft Azure AI Fundamentals (AI-901) exam questions are a great way to prepare for the Microsoft AI-901 exam dumps.

Microsoft Azure AI Fundamentals Sample Questions (Q122-Q127):

NEW QUESTION # 122
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:

NEW QUESTION # 123
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:

NEW QUESTION # 124
You have a website that includes customer reviews.
You need to store the reviews in English and present the reviews to users in their respective language based on each user ' s geographical location.
Which type of natural language processing workload should you use?

A. language modeling
B. speech recognition
C. key phrase extraction
D. translation

Answer: C

NEW QUESTION # 125
What should you use to identify similar faces in a set of images?

A. Azure AI Custom Vision
B. Azure OpenAI Service
C. Azure AI Vision
D. Azure AI Language

Answer: C

Explanation:
To identify similar faces across different images using Azure, the Azure AI Face service is the most suitable choice. This service leverages AI algorithms to detect, recognize, and analyze human faces, including features like face grouping based on visual similarity.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/find-similar-faces

NEW QUESTION # 126
You are developing an application that analyzes invoices by using Azure Content Understanding in Foundry Tools.
You have a custom analyzer named invoiceAnalyzer.
You need to use the analyzer to process invoice files.
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 correct parameter is analyzer_id . The Azure AI Content Understanding Python SDK uses the ContentUnderstandingClient.begin_analyze() method to submit content for processing. Microsoft defines the method signature with analyzer_id as the required identifier that specifies which analyzer should process the supplied input.
Therefore, the code should be completed as:
poller = client.begin_analyze(analyzer_id= " invoiceAnalyzer " , ...)
A custom analyzer encapsulates the configuration that determines how Content Understanding interprets the source content and which fields or structured information it extracts. Because the analyzer has already been created with the name invoiceAnalyzer , that value is supplied through analyzer_id when the analysis operation begins.
extraction_type is not the parameter used to select an analyzer. model_name would refer conceptually to an underlying AI model, but Content Understanding analysis is invoked through an analyzer abstraction rather than by specifying a model directly. Likewise, schema_id does not identify the analyzer to execute.
Microsoft ' s custom-analyzer guidance explicitly demonstrates client.begin_analyze (analyzer_id=analyzer_id, inputs=[...]) , confirming the required SDK pattern

NEW QUESTION # 127
......

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