Databricks Data Engineer Professional · 13% practice allocation
5 free sample questions from the Cost and Performance Optimization domain, with answers and explanations. The full Data Engineer Professional bank has 311 questions across 10 domains. Questions are scenario-based and written from the official Databricks exam guide.
Which Databricks feature accelerates query execution using a vectorized, native engine to reduce cost per query?
Photon is Databricks' vectorized native execution engine that speeds up SQL/DataFrame workloads, improving price/performance. VACUUM (C) is file cleanup, Delta Sharing (B) shares data, and widgets (A) parameterize notebooks.
A frequently queried Delta table is partitioned by a very high-cardinality column, producing millions of tiny partitions and slow queries. What is the BEST remediation?
Over-partitioning on high-cardinality columns creates many tiny files/partitions; repartitioning on a lower-cardinality key (or using liquid clustering) fixes this. Adding more partitions (A) or partitioning by a unique ID (D) worsens it, and JSON (C) abandons Delta's optimizations.
What does data skipping in Delta rely on to avoid reading irrelevant files during a filtered query?
Delta stores per-file min/max statistics in the transaction log, so the engine can skip files whose ranges can't satisfy the filter — enhanced by ZORDER/clustering. Reading every file (B) defeats skipping, file naming (C) isn't used, and broadcast variables (D) are a join mechanism.
When joining a very large fact table with a small dimension table, which technique avoids a costly shuffle of the large table?
Broadcasting the small dimension table to all executors lets each join locally without shuffling the large fact table — much cheaper. A full shuffle of both (B) is expensive, collecting to the driver (C) doesn't scale, and disabling the optimizer (D) removes helpful automation.
A pipeline over-provisions a large all-purpose cluster running 24/7 even though it only runs jobs a few hours a day. Which change reduces cost?
Job clusters that start per run and terminate afterward (plus autoscaling and right-sizing) avoid paying for idle time. A permanently larger cluster (B), driver-only execution (C), and pinning max nodes (D) all increase cost or reduce scalability.
The free Data Engineer Professional practice test mixes about 20 questions weighted like the exam. The full 311-question bank adds instant grading, flashcards and a timed mock exam.