Databricks Certified Machine Learning Professional Practice Mode

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose A0

  - Correct answer : A — The training algorithm is inherently sequential and cannot be partitioned across workers, so a larger single machine avoids the coordination overhead of a distributed job that would not actually parallelize the computation. A . Correct: when an algorithm cannot be meaningfully partitioned, distributing it across nodes adds network and coordination overhead without unlocking real parallel speedup, so a bigger single machine is the more effective lever. B . A dataset too large for any single instance is a textbook argument for horizontal scaling, not vertical scaling, since no single machine can hold the data regardless of size. C . Near-linear scaling with added workers is precisely the property that makes horizontal scaling attractive, so this observation favors adding nodes rather than favoring a bigger single machine. D . Indefinite future growth is an argument for an elastic, horizontally scalable architecture rather than for committing to ever-larger single instances, which eventually hit a hardware ceiling.