Final answer:
Problems with solutions that scale well include web hosting and cloud storage services, while solutions that don't scale well are found in physical infrastructure and personalized healthcare. NP-complete problems, such as the traveling salesman problem with a large number of cities, have no feasible computational solution.
Step-by-step explanation:
When discussing solvability and scalability in computational problem-solving, it's important to consider real-world examples where scaling solutions either works well or does not. Two examples of problems whose solutions scale well are:
- Web hosting services can efficiently scale to support an increasing number of websites as more businesses go online.
- Cloud storage services can effectively scale to accommodate growing amounts of data generated by users.
In contrast, two examples of problems whose solutions do not scale well are:
- Physical infrastructure, like roads or bridges, which cannot be easily expanded to match rapid increases in traffic and population growth.
- Personalized healthcare, as it involves individualized attention and can't be mass-produced or automated easily.
Among problems with no feasible computational solution, due to their complexity, is the NP-complete problems like the traveling salesman problem with a very large number of cities, for which no efficient algorithm exists to solve it in polynomial time.
In engineering, creativity and judgement are essential to solve complex societal problems. Engineering often requires teamwork and functions best within a broader social structure. The field is also a source of both intended beneficial outcomes and sometimes unintended negative consequences.