Design solutions for future adoption and performance.

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Multiple Choice

Design solutions for future adoption and performance.

Explanation:
Planning for how adoption will grow and how performance will be sustained is about future scalability requirements. It focuses on anticipating growth in users, transactions, and data, and designing the system to handle that without major rewrites. This includes modeling expected load, choosing scalable architectures (such as horizontally scalable, stateless services or microservices), and implementing data strategies like caching, partitioning, and sharding. It also involves setting performance targets, building in load testing and performance benchmarking, and planning for elastic resource provisioning (auto-scaling) to meet demand as the system expands. The other options address access control and governance rather than how the system will grow and perform. SoD in log management and implementing SoD relate to preventing conflicting duties and securing operations, while data management governance covers data quality and stewardship. While important, they don’t directly ensure design for future adoption and performance.

Planning for how adoption will grow and how performance will be sustained is about future scalability requirements. It focuses on anticipating growth in users, transactions, and data, and designing the system to handle that without major rewrites. This includes modeling expected load, choosing scalable architectures (such as horizontally scalable, stateless services or microservices), and implementing data strategies like caching, partitioning, and sharding. It also involves setting performance targets, building in load testing and performance benchmarking, and planning for elastic resource provisioning (auto-scaling) to meet demand as the system expands.

The other options address access control and governance rather than how the system will grow and perform. SoD in log management and implementing SoD relate to preventing conflicting duties and securing operations, while data management governance covers data quality and stewardship. While important, they don’t directly ensure design for future adoption and performance.

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