20+ practice questions focused on Cost and Performance Optimization — one of the most tested topics on the Databricks Certified Data Engineer Professional exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Cost and Performance Optimization PracticeA data engineering team manages a Delta Lake table that is frequently updated and queried by multiple downstream jobs. Users report that queries are increasingly slow over time. Inspection shows thousands of small JSON files in the storage location due to streaming appends. Which optimization technique should the data engineer apply to restore query performance cost-effectively?
Explanation: Delta Lake tables store data in Parquet format, not JSON. While the transaction log (_delta_log) contains JSON files, the 'small file problem' that impacts query performance and is resolved by the OPTIMIZE command refers to the data files (Parquet). Using 'JSON' in the stem is technically incorrect for a Delta Lake data storage description and would confuse a professional-level candidate.
To optimize performance for a table that is frequently filtered by a date column, what is the best strategy?
Explanation: Databricks now recommends Liquid Clustering for all new Delta tables as it replaces manual partitioning and Z-Ordering. It provides better performance, simplifies data layout, and allows for flexible clustering keys without rewriting data. While partitioning by date (Option C) is a valid legacy technique, it is no longer the 'best strategy' in the context of modern Databricks features (DBR 13.3+).
A data engineer observes that a Delta table containing 50TB of data is experiencing slow scan performance. The table is partitioned by 'date', but many queries filter by 'region' and 'customer_id'. Which optimization strategy should the engineer implement to improve query performance with minimal overhead?
Explanation: Z-Ordering on 'region' and 'customer_id' organizes data files to colocate related information within existing partitions. By physically clustering data based on these high-cardinality columns, the Delta Lake engine can maximize data skipping during scans. While Z-Ordering requires an OPTIMIZE command to rewrite data files, it is the most effective strategy for high-cardinality columns where partitioning (Option A) would lead to the 'small file problem' and excessive metadata overhead. This technique is essential for large datasets where partitioning alone does not sufficiently narrow down the search space.
Refer to the exhibit. A data engineer is running a heavy join operation on a cluster with the provided configuration. Despite the auto-scaling being set to a maximum of 8 workers, the join operation is consistently spilling to disk. What is the most likely cause?
Explanation: Spilling to disk during a heavy join operation typically occurs when the shuffle data exceeds the available memory per executor, often related to insufficient memory-optimized instance types, improper memory fractions, or inadequate shuffle partitions, rather than the Spark cache consuming memory (which would actually be evicted under memory pressure if properly managed).
Which THREE actions should be taken to minimize the cost of running a Databricks Job that processes large volumes of intermittent data?
Explanation: To optimize costs for intermittent, large-volume workloads, you should: 1) Use Job clusters, which have a significantly lower DBU rate than All-Purpose (interactive) clusters. 2) Use Spot instances for workers to reduce infrastructure costs by up to 90%. 3) Enable auto-scaling so the cluster size adjusts to the data volume, ensuring you do not pay for idle resources during periods of low activity.
+15 more Cost and Performance Optimization questions available
Practice all Cost and Performance Optimization questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of Cost and Performance Optimization. This tells you whether you need a concept refresher or just practice.
2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
Cost and Performance Optimization questions on the Databricks-DE-Pro frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. Cost and Performance Optimization is tested as part of the Databricks Certified Data Engineer Professional blueprint. Practicing with targeted Cost and Performance Optimization questions ensures you can handle any format or difficulty that appears.
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