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Free Microsoft AI-900 Exam Braindumps

What is an example of the Microsoft responsible AI principle of transparency?

  1. ensuring that opportunities are allocated equally to all applicants
  2. helping users understand the decisions made by an AI system
  3. ensuring that developers are accountable for the solutions they create
  4. ensuring that the privileged data of users is stored in a secure manner

Answer(s): B

Explanation:

Transparency
Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way.
Each predictive value should be broken down into individual features or vectors by importance or impact and deliver thorough prediction explanations that can be exported into a business report for audit and compliance reviews, customer transparency, and business readiness.
Transparency Notes provide our customers with information about the intended uses, capabilities, and limitations of our AI platform services.


Reference:

https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai https://www.microsoft.com/en-us/ai/responsible-ai



HOTSPOT (Drag and Drop is not supported)
Select the answer that correctly completes the sentence.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Box: reliability and safety
Unusual or missing values is an example of the application of the     principle of responsible AI.
The handling of unusual or missing values given to an AI system falls under the reliability and safety principle of Microsoft's guidelines for responsible AI.
Note: Reliability and safety
To build trust, it's critical that AI systems operate reliably, safely, and consistently. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation. How they behave and the variety of conditions they can handle reflect the range of situations and circumstances that developers anticipated during design and testing.
Reliability and safety in Azure Machine Learning: The error analysis component of the Responsible AI dashboard enables data scientists and developers to:
Get a deep understanding of how failure is distributed for a model.
Identify cohorts (subsets) of data with a higher error rate than the overall benchmark.
These discrepancies might occur when the system or model underperforms for specific demographic groups or for infrequently observed input conditions in the training data.


Reference:

https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai



You plan to use Azure Machine Learning Studio and automated machine learning (automated ML) to build and train a model.
What should you create first?

  1. a Machine Learning workspace
  2. a Machine Learning designer pipeline
  3. a registered dataset
  4. a Jupyter notebook

Answer(s): A

Explanation:

Set up no-code AutoML training for tabular data with the studio UI Prerequisites
An Azure subscription.
*-> An Azure Machine Learning workspace.
Note: Azure Machine Learning workspace
Workspaces are places to collaborate with colleagues to create machine learning artifacts and group related work. For example, experiments, jobs, datasets, models, components, and inference endpoints.


Reference:

https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-automated-ml-for-ml-models?



Verifying that machine learning models do NOT show racial or gender bias is an example of which Microsoft responsible AI principle?

  1. fairness
  2. privacy and security
  3. safety
  4. reliability

Answer(s): A

Explanation:

Fairness and inclusiveness in Azure Machine Learning: The fairness assessment component of the Responsible AI dashboard enables data scientists and developers to assess model fairness across sensitive groups defined in terms of gender, ethnicity, age, and other characteristics.


Reference:

https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai?view=azureml-api-2



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