Memory · interview question

What is the healthiest performance judgment around long-lived objects?

Multiple choiceHardchapter-16-curatedpremiummemorylong-lived-objects

python

cache = []
for row in rows:
    cache.append(row)  # keeps every row alive for the whole process

Measure before optimizing, then change the part that actually dominates cost instead of guessing from style alone.Delay clarity until production traffic reveals the edge case and then patch it under pressure.Solve the symptom with comments only and avoid changing the code shape around long-lived objects.Keep long-lived objects implicit so the script stays short and hope the next engineer infers the rule.

Answer and explanation

Correct answer

A. Measure before optimizing, then change the part that actually dominates cost instead of guessing from style alone.

Why

Premature optimization is usually weaker than targeted optimization backed by measurement.

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What is the healthiest performance judgment around long-lived objects?

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