When designing solutions for performance and cost efficiency, which practice is recommended?

Question

Options

• AUsing the same compute option for all types of workloads
• BRegularly reviewing and optimizing the choice of compute and storage services
• CAvoiding the use of monitoring and logging to reduce operational overhead
• DScaling resources manually to maintain tight control over the environment

• A
  6% (1)
• B
  78% (14)
• C
  6% (1)
• D
  11% (2)

Explanation

Regularly reviewing and optimizing compute and storage services (B) is the cornerstone of both performance and cost efficiency - workload requirements change over time, and cloud providers continuously release new, better-priced options, so periodic review ensures you're not over-provisioning or using outdated resource types.

Why the distractors are wrong:

• A is incorrect because different workloads have different profiles (CPU-intensive, memory-intensive, I/O-bound) - a one-size-fits-all compute choice wastes money and degrades performance.
• C is the opposite of best practice; monitoring and logging are essential for identifying bottlenecks, waste, and cost anomalies - skipping them leads to blind spots.
• D is inefficient; manual scaling is slow, error-prone, and defeats the purpose of elastic cloud infrastructure where auto-scaling handles demand dynamically.

Memory tip: Think of it as a fitness routine - you don't use the same workout forever and you never skip tracking progress. "Review, Optimize, Repeat" = option B.

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