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NEW QUESTION # 98
You have an AI-based loan approval system.
During testing, you discover that the system has a gender bias.
Which responsible AI principle does this violate?
Answer: C
Explanation:
Fairness is the responsible AI principle that addresses gender bias. It ensures that artificial intelligence systems treat all people fairly and do not create or spread unfair differences between groups based on gender, race, or other traits.
Stops Bias: It keeps systems from treating one group worse than another.
Checks Data: It looks at training data to remove unfair patterns.
Protects Users: It ensures equal outcomes for everyone.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai
NEW QUESTION # 99
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:
NEW QUESTION # 100
You need to identify harmful content in a generative AI solution that uses Azure OpenAI Service.
What should you use?
Answer: D
Explanation:
Azure AI Content Safety is a new Azure AI Service that can detect harmful user-generated and AI-generated content. This service is being integrated across MS products, including Azure OpenAI Service and Machine Learning prompt flow.
Reference:
https://ivanatilca.medium.com/testing-azure-ai-content-safety-ccd512f9b350
NEW QUESTION # 101
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: In the new Microsoft Foundry portal, you must fine-tune a model before you can deploy the model. = No Fine-tuning is optional. Microsoft's Foundry model deployment documentation describes deploying Foundry Models directly from the model catalog for inference. It does not require fine-tuning first.
Statement 2: In the new Microsoft Foundry portal, you can test a model from the model catalog only after you deploy the model. = No Microsoft documentation states that some Foundry Tools are available to try via the model catalog without a project, and Foundry playgrounds are used for prototyping and validation before production. Therefore, the statement using "only after you deploy" is too restrictive.
Statement 3: In the new Microsoft Foundry portal, you can deploy a model from the model catalog only after retraining the model. = No Retraining/fine-tuning is not required before deployment. Microsoft states that after you deploy a Foundry Model, you can interact with it in the Foundry Playground and use it from code, and the deployment workflow starts by selecting a model from the model catalog and choosing Deploy.
NEW QUESTION # 102
In Azure Machine Learning, what are two ensemble methods for combining models in automated machine learning (automated ML)? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
In Azure Machine Learning's automated machine learning (AutoML), two prominent ensemble methods for combining models are Voting and Stacking.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-automated-ml 1
NEW QUESTION # 103
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The Microsoft AI-901 certification exam is an industrial-recognized Microsoft Azure AI Fundamentals (AI-901) certificate that is designed to validate candidates' skills, experience, and knowledge quickly. The Microsoft Azure AI Fundamentals (AI-901) certification exam has been inspiring Microsoft professionals since its beginning. Now this AI-901 certification exam has become solid proof of certain skills set and knowledge.
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