Valid AI-901 Test Answers | Exam Dumps AI-901 Pdf

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Microsoft AI-901 Exam Syllabus Topics:

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

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Microsoft Azure AI Fundamentals Sample Questions (Q52-Q57):

NEW QUESTION # 52
What should you use to explore pretrained generative AI models available from Microsoft and third-party providers?

Answer: B

Explanation:
Azure AI Foundry Models gives you access to flagship models in Azure AI Foundry to consume them as APIs with flexible deployment options.
Depending on what kind of project you're using in Azure AI Foundry, you might see a different selection of these models. Specifically, if you're using a Foundry project, built on an Azure AI Foundry resource, you see the models that are available for standard deployment to a Foundry resource. Alternatively, if you're using a hub-based project, hosted by an Azure AI Foundry hub, you see models that are available for deployment to managed compute and serverless APIs.
These model selections do overlap in many cases, since many models support the multiple deployment options.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/foundry-models/concepts/models


NEW QUESTION # 53
Your company wants to build a recycling machine for bottles. The recycling machine must use cameras to automatically identify bottles of the correct shape and reject all other items.
Which type of Al workload should the company use?

Answer: C

Explanation:
A recycling machine that uses cameras to identify bottles of the correct shape and reject other items requires analysis of visual input. This is a computer vision workload.
Computer vision is used for image and video analysis tasks such as object detection, classification, shape recognition, and visual inspection.
The other options are incorrect:
A). knowledge mining extracts insights from large collections of documents or data.
B). natural language processing analyzes or generates language.
C). generative AI creates new content, but the requirement is to inspect camera images and identify objects.


NEW QUESTION # 54
You are developing an application that uses the Azure Language SDK and a Microsoft Foundry resource.
You need to return a list of important topics from unstructured text.
Which Python method should you use?

Answer: A

Explanation:
To return a list of important topics from unstructured text using the Azure Language Python SDK within a Microsoft Foundry resource, you should use the extract_key_phrases method.
Depending on whether you are running synchronous or asynchronous operations for large batches of documents, choose one of the following methods from the TextAnalyticsClient:
client.extract_key_phrases(documents)
Used for immediate, synchronous processing of text data. It returns results in real time and is ideal for smaller or interactive application workloads.client.begin_extract_key_phrases(documents) Used for asynchronous (batch) processing. It returns a long-running operation poller, which is highly recommended when analyzing large volumes of unstructured text or multiple documents simultaneously.
Reference:
https://notes.kodekloud.com/docs/AI-102-Microsoft-Certified-Azure-AI-Engineer-Associate/Analyzing-Text/Working-with-Azure-AI-Language-Services/page


NEW QUESTION # 55
Capturing text from images is an example of which type of AI capability?

Answer: A

Explanation:
Optical Character Recognition (OCR) is a technology that converts images of text (like scanned documents or photos) into machine-readable, editable text. It's used to extract text from various sources and make it accessible for digital editing, searching, and other manipulations.
Reference:
https://www.ibm.com/think/topics/optical-character-recognition


NEW QUESTION # 56
Drag and Drop Question
Match the description to the corresponding Microsoft Foundry tools.
To answer, drag the appropriate description from the column on the left to its tool on the right.
Each description may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: Transcribes spoken input
Speech recognition in Microsoft Foundry transcribes spoken input into text using built-in speech- to-text capabilities.
Transcription FeaturesReal-time transcription: Converts microphone inputs or live audio streams into text instantly.
Batch transcription: Processes pre-recorded audio or video files asynchronously.
Advanced audio models: Leverages tools like GPT-transcribe and GPT-live-transcribe to handle natural speech patterns, numbers, addresses, and industry-specific terminology.
Broad language support: Handles over 140 locales and languages for spoken input.
Box 2: Generates spoken output
Speech synthesis in Microsoft Foundry (via Azure Speech in Foundry Tools) converts text input into realistic, human-like spoken audio output.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/speech-service/speech-to-text


NEW QUESTION # 57
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