No, overfitting is a real thing. Overfitted learning algorithms are generally worse at generalizing their ability to broader examples and new situations.<p>The types of candidates who spend the time necessary to memorize algorithm trivia for the sake of passing these exams are exactly like overfitted learning algorithms. What they happen to know is unlikely to generalize well. Of course you could get lucky and hire someone like that who can generalize, but that&#x27;s rare. More often, since hiring is political, you pat yourself on the back for how &quot;good&quot; the candidate is (based on some trivia) and make excuses when their on-the-job performance isn&#x27;t what you&#x27;d hoped, and find ways to deflect attention from that so that you, as an inefficient hirer, won&#x27;t be called out on it.<p>Willingness to waste time overfitting yourself to algorithm trivia absolutely predicts worse later-on performance than candidates with demonstrated experience and pragmatism (e.g. I&#x27;m not wasting my time memorizing how to solve tricky things that rarely matter. I will look them up &#x2F; derive them &#x2F; figure them out when&#x2F;if I need them).<p>If given the choice between hiring a math&#x2F;programming olympiad winner vs. a Macgyver&#x2F;Edison-like tinkerer who may not be able to explain how to convert between Thevenin and Norton circuit forms, but who took their family radio apart and put it back together, Macgyver&#x2F;Edison wins every time (unless you&#x27;re hiring for bullshit on-paper prestige, and of course many places are while proclaiming loudly that they aren&#x27;t).