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In the context of developing a sentiment analysis model for social media data aimed at enhancing user engagement and brand perception, a company faces constraints including limited computational resources, the need for real-time processing, and compliance with data privacy regulations. Considering these constraints, which professional is best suited to define success metrics that not only align with user engagement and brand perception but also ensure the model's scalability, cost-effectiveness, and compliance with data privacy laws? Choose the best option.
A
Chief Executive Officer (CEO), who can provide high-level business objectives but lacks the technical expertise to address computational and compliance constraints.
B
Data Engineer, who can optimize data infrastructure for real-time processing but may not fully grasp the nuances of aligning ML metrics with business goals.
C
Social Media Manager, who understands user engagement and brand perception but lacks the technical skills to define ML success metrics under given constraints.
D
Google Cloud Professional Machine Learning Engineer, who can align ML success metrics with business objectives while ensuring the model is scalable, cost-effective, and compliant with data privacy laws.
E
Both the Google Cloud Professional Machine Learning Engineer and the Data Engineer, as their combined expertise covers both the alignment of ML metrics with business goals and the optimization of data infrastructure for real-time processing.