Databricks Certified Context Engineer Associate 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 — Config B is justified: the few-shot examples drive a large accuracy gain in a safety-critical domain where the added latency is quite acceptable. A . Correct. The few-shot examples in Config B lead to a substantial 12-point accuracy increase, which is critical for safety in medical-device troubleshooting. The added latency (from 1.2s to 2.1s) is modest and acceptable given the high stakes, so the tradeoff strongly favors Config B. B . Incorrect. The scenario does not state any pre-defined safety threshold for accuracy, and a hard 1-second latency limit is an arbitrary constraint not supported by the domain. In safety-critical contexts, the priority is typically on reducing wrong diagnoses, not an absolute latency ceiling. C . Incorrect. A higher token count alone does not inherently improve output quality; it is the specific few-shot examples that likely drive the accuracy gain. Selecting Config B simply because it is longer ignores the fact that prompt content, not length, determines effectiveness. D . Incorrect. Config A’s 82% accuracy suggests it does not adequately cover the edge cases captured by the few-shot examples in Config B. The lower accuracy implies that the plain instructions miss important failure patterns that the examples help diagnose correctly. E . Incorrect. MLflow experiment tracking can compare prompts of different token lengths without bias, as metrics like accuracy and latency are measured based on model outputs, not prompt sizes. The token count difference does not invalidate the comparison; it is a legitimate variable in the experiment.