Tailwind Traders is piloting a model to recommend credit products to small businesses. The historical training data comes mostly from one region, primarily tech startups, and is missing many declined applications due to legacy retention rules. The risk team is concerned that the model’s outputs may not generalise to other regions and industries.Which statement best explains why the data profile is a fundamental risk here?
How can organizations address challenges such as incorrect information and bias in generative AI?
StatementIn a typical Azure AI solution, Azure AI Vision is used primarily for OCR and image analysis, Azure AI Search is used as the search and retrieval engine over indexed content, and Azure AI Foundry is used as the workspace to integrate these services into governed AI applications and agents.
A transformation lead at Fabrikam claims:“As long as we collect very large volumes of data, it doesn’t matter if it’s noisy or poorly labeled; modern generative AI models will automatically correct for low data quality during training.”Is this statement correct?
A change lead at Tailwind Traders claims:“Copilot in Outlook can draft new email messages from a prompt, and it can also summarize long email threads so users can quickly see key points and action items without reading every message.”Is this statement correct?
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