20+ practice questions focused on Developing Code (Python/SQL) — 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 Developing Code (Python/SQL) PracticeA data engineer is implementing a Delta Lake table with streaming ingestion. To ensure high-concurrency writes while maintaining data integrity, what configuration must be enabled for the table to avoid 'Optimistic Concurrency Control' conflicts?
Explanation: Enabling change data feed and utilizing multi-cluster writes or optimizing the commit protocol is essential for high-frequency streaming. Delta Lake uses Optimistic Concurrency Control, which fails if two writers modify the same metadata file. By configuring appropriate isolation levels and using table properties for conflict management, engineers reduce failure rates, which is vital for maintaining low-latency pipelines that require continuous updates from multiple streaming sources simultaneously.
Which TWO of the following statements accurately describe the behavior of the 'MERGE' operation in Delta Lake regarding schema evolution and concurrency?
Explanation: MERGE operations in Delta Lake support row-level updates by rewriting only the files that contain matching rows (Copy-on-Write), which is the foundation for its optimistic concurrency control. Automatic schema evolution for MERGE is not enabled by default and specifically requires setting the Spark configuration 'spark.databricks.delta.schema.autoMerge.enabled' to true. The 'mergeSchema' write option is used for standard DataFrame append or overwrite operations, not for the MERGE DML command.
Refer to the exhibit. A data engineer encounters this error while running a SQL query on a Delta table. Based on the error message, what is the most likely cause?
Explanation: The error indicates a case-sensitivity or naming mismatch in the column identifier. Databricks SQL is generally case-insensitive, but when specific aliases or object names are referenced, strict matching is required. Resolving this requires verifying the table schema via DESCRIBE or ensuring the query matches the underlying Parquet metadata. This is fundamental for debugging production pipelines where schema changes can break downstream SQL scripts unexpectedly.
You are performing a 'Vacuum' operation on a large Delta table to remove expired files. You notice that the operation is running for an extended period and consuming significant cluster resources. What is the most likely reason for this performance overhead?
Explanation: Vacuuming scans the storage directory to find files not tracked by the current transaction log. When a Delta table accumulates millions of small files (often due to frequent streaming writes or small batch inserts), the file listing and deletion API calls become a major bottleneck, leading to extended execution times and high resource usage.
You have a large Delta table and you need to perform an update on a single row based on a complex condition. You have noticed that 'UPDATE' statements are slow. What is the most effective way to optimize this operation in a production pipeline?
Explanation: In Delta Lake, both UPDATE and MERGE operations perform the same underlying task: identifying the files that contain the target rows, reading them, applying the changes, and rewriting the files. MERGE is not inherently faster than UPDATE for a single row update; both are subject to the same file-level rewrite overhead. The most effective way to optimize a single row update in a large Delta table is to ensure the table is Z-Ordered or partitioned on the column used in the WHERE clause to enable data skipping, thereby minimizing the number of files scanned.
+15 more Developing Code (Python/SQL) questions available
Practice all Developing Code (Python/SQL) questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of Developing Code (Python/SQL). 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
Developing Code (Python/SQL) 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. Developing Code (Python/SQL) is tested as part of the Databricks Certified Data Engineer Professional blueprint. Practicing with targeted Developing Code (Python/SQL) questions ensures you can handle any format or difficulty that appears.
Yes. Courseiva provides free Databricks-DE-Pro practice questions across all exam topics and domains. The platform includes topic-based practice, mock exams, missed-question review, bookmarked questions, and readiness tracking — no account required.
Difficulty is subjective, but Developing Code (Python/SQL) is a high-priority exam concept tested in multiple ways — direct recall, scenario analysis, and command-output interpretation. Consistent practice is the best way to build confidence.
Launch a full Developing Code (Python/SQL) practice session with instant scoring and detailed explanations.
Start Developing Code (Python/SQL) Practice →