SKILL BENCHMARK
AI and ML Data Strategy Proficiency (Advanced Level)
- 28m
- 28 questions
The AI and ML Data Strategy Proficiency (Advanced Level) benchmark measures your knowledge of how to guide managers and decision-makers in the steps required to formulate a comprehensive artificial intelligence (AI) strategy aligned with business goals. You will be evaluated on your ability to outline the importance of recognizing and addressing data bias and ethical considerations in AI projects and the importance of establishing effective data management and governance strategies to support AI initiatives. A learner who scores high on this benchmark demonstrates that they have the skills necessary to develop an AI and ML data strategy in their organization and can build a robust data strategy.
Topics covered
- analyze strategies for data collection, storage, and integration in AI projects
- analyze the role of metadata management in enhancing data quality
- define the key components of a robust artificial intelligence (AI) strategy, from vision to execution
- define the principles of effective data management and its relevance in artificial intelligence (AI) projects
- describe the methods for handling large and complex datasets in AI projects
- describe the role of data quality, integrity, and availability in AI model development
- formulate guidelines for addressing ethical concerns in AI projects
- formulate guidelines for managing ethical considerations in data usage for AI
- formulate strategies for fostering cross-functional collaboration in AI initiatives
- identify methods for auditing AI models for fairness and inclusivity
- Identify potential risks and challenges in AI strategy implementation
- identify real-world examples of data bias affecting AI systems
- identify the significance of aligning AI strategies with existing technology initiatives
- illustrate the role of data privacy regulations and compliance in AI initiatives
- outline regulatory and compliance considerations in AI projects
- outline the ethical considerations related to AI decision-making
- outline the importance of conducting a comprehensive AI readiness assessment
- outline the importance of data governance frameworks and their components
- outline the managerial responsibilities in communicating AI strategies to stakeholders
- outline the potential risks of biased or incomplete data in AI models
- outline the process of identifying high-potential AI use cases within the organization
- outline the role of executive leadership in shaping and championing AI strategies
- provide an overview of strategies for managing data silos and ensuring data accessibility
- provide an overview of the managerial responsibilities in overseeing data governance committees
- recognize methods for defining clear success metrics for AI projects
- recognize the managerial responsibilities in ensuring unbiased AI solutions
- recognize the role of transparency and fairness in AI model development
- recognize the sources of bias in data and their implications in AI applications
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