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Microsoft AI-901 Exam Syllabus Topics:

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

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AI-901 Dumps Collection: Microsoft Azure AI Fundamentals & AI-901 Test Cram & AI-901 Study Materials

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Microsoft Azure AI Fundamentals Sample Questions (Q125-Q130):

NEW QUESTION # 125
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

The correct answer is classify email messages as work-related or personal. Natural language processing (NLP) is concerned with analyzing, interpreting, and processing human language, including unstructured text contained in documents, messages, reviews, and emails. Text classification is therefore a direct NLP workload: an AI model analyzes the linguistic content of an email and assigns it to a predefined category such as work-related or personal.
Microsoft ' s current AI-901 study guide explicitly requires candidates to identify scenarios for common AI workloads, including text analysis, and to understand common text-analysis techniques. Microsoft Azure Language in Foundry Tools similarly supports capabilities for extracting information from text and classifying text, demonstrating that categorization based on textual content is an NLP function.
Predicting future car rentals is primarily a regression/forecasting problem because the output is a numerical quantity. Predicting whether a website visitor will transact is typically a binary classification machine- learning problem based on behavioral or transactional features, not necessarily language. Stopping industrial machinery when a temperature threshold is exceeded is a rule-based monitoring/control scenario.


NEW QUESTION # 126
For each of the following statements, Select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Statement 1: To process prompts that contain images, you must deploy a multimodal generative AI model. = Yes Image prompts require a vision-enabled or multimodal model. A text-only model cannot interpret image content.
Statement 2: In the Foundry playground, you can provide an image by uploading a local file or by specifying a publicly accessible URL. = Yes The Foundry playground for vision-capable models supports adding image input to a prompt, including local uploads and image URLs.
Statement 3: A multimodal model can interpret multiple images included in a single prompt. = Yes Vision-enabled multimodal models can accept image content items in a prompt, and multiple images can be included for the model to analyze together.


NEW QUESTION # 127
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:
Box: a multimodal
Processing prompts that contain images requires you to deploy and use a multimodal model (such as GPT-4o) via Microsoft Foundry.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/flow-process-image


NEW QUESTION # 128
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 129
Which feature of the Azure Language in Foundry Tools service should you use to automate the masking of names and phone numbers in text data?

Answer: B

Explanation:
To automate masking of names and phone numbers in text data, use Personally Identifiable Information (PII) detection in Azure Language.
PII detection identifies sensitive personal information such as names, phone numbers, email addresses, addresses, and other personal identifiers. It can be used to detect and redact or mask that information from text.
The other options are incorrect:
A). custom text classification categorizes documents or text into labels.
C). entity linking identifies entities and links them to knowledge base entries.
D). custom named entity recognition (NER) can extract custom entities, but for built-in masking of names and phone numbers, PII detection is the correct feature.


NEW QUESTION # 130
......

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