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Discuss the impact of AutoML on the skill set required for data scientists. Explain how AutoML tools can augment the capabilities of data scientists and enable them to focus on higher-value tasks. Provide examples of skills that are becoming more important due to the adoption of AutoML.
A
Domain expertise, problem formulation, and model interpretation.
B
Data engineering, system architecture, and software development.
C
Statistical analysis, feature engineering, and model tuning.
D
Machine learning algorithms, deep learning, and natural language processing.