Microsoft AI-901 Certification Materials | Vce AI-901 File

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Microsoft AI-901 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Identify AI concepts and responsibilities40-45%- Identify AI model components and configurations
  • 1. Describe model deployment and consumption concepts
  • 2. Describe data preparation for AI models
  • 3. Describe model training and evaluation concepts
- Describe principles of responsible AI
  • 1. Describe privacy and security considerations
  • 2. Describe fairness considerations
  • 3. Describe accountability considerations
  • 4. Describe inclusiveness considerations
  • 5. Describe reliability and safety considerations
  • 6. Describe transparency considerations
- Identify AI concepts and features
  • 1. Describe capabilities of machine learning
  • 2. Describe capabilities of natural language processing
  • 3. Describe characteristics of generative AI
  • 4. Describe capabilities of computer vision
  • 5. Describe types of AI workloads
Topic 2: Implement AI solutions by using Microsoft Foundry55-60%- Work with models in Microsoft Foundry
  • 1. Customize models with prompt engineering
  • 2. Fine-tune models
  • 3. Discover and deploy foundation models
  • 4. Evaluate model performance and safety
- Get started with Microsoft Foundry
  • 1. Describe core features and use cases of Microsoft Foundry
  • 2. Navigate the Microsoft Foundry portal
  • 3. Create and configure a Foundry project
- Implement AI solutions
  • 1. Use code examples to call AI services
  • 2. Implement single-agent solutions
  • 3. Integrate AI services into applications
  • 4. Monitor and manage AI solutions

>> Microsoft AI-901 Certification Materials <<

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Microsoft Azure AI Fundamentals Sample Questions (Q50-Q55):

NEW QUESTION # 50
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

A schema defines which fields to extract when analyzing content.
In Azure Content Understanding, the schema or fieldSchema defines the structured data that the analyzer extracts from content, including field names, types, and extraction behavior. Microsoft documentation states that Content Understanding lets you define a schema to extract, classify, or generate field values from unstructured content.
The other options are incorrect:
A keyword list does not define the complete structured output fields.
OCR-only processing extracts text, but it does not define structured fields.
A synchronous API call describes a request pattern, not the extraction schema.


NEW QUESTION # 51
You need to build an AI solution that responds to user queries with detailed written explanations.
Which type of AI model should you use?

Answer: A


NEW QUESTION # 52
You have an Azure Machine Learning model that generates a large quantity of false negatives.
You need to reduce the number of false negatives without re-training the model.
What should you do?

Answer: C

Explanation:
What are different methods to minimize False Negatives and False Positives in Binary Classification?
Threshold Line
One of the easiest methods to minimize the outcomes of a certain case is simply changing the decision boundary line from the basic 0.5 to above (when reducing False Positives) or below (when reducing False Negatives). It should be noted that by doing this, the possibility of False Positives increases. In other words, by decreasing the False Negatives we are increasing the False Positives.
Reference:
https://www.kaggle.com/discussions/general/376229


NEW QUESTION # 53
Which Azure Machine Learning capability should you use to quickly build and deploy a predictive model without extensive coding?

Answer: B

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 # 54
Which natural language processing feature can be used to identify the main talking points in customer feedback surveys?

Answer: B

Explanation:
To identify the main talking points in customer feedback surveys, use key phrase extraction . This feature identifies important words and phrases in text, which is useful for summarizing themes in feedback.


NEW QUESTION # 55
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