Databricks-DE-Pro Developing Code (Python/SQL) Practice Question
Which THREE features are provided by Delta Lake when compared to standard Parquet files? (Select THREE)
⚠ Common exam trap
Candidates often include features like 'data compression' or 'partitioning', which are inherent to Parquet itself, rather than features added specifically by the Delta Lake layer.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
ACID transactions
Delta Lake adds a transaction log (the _delta_log) to standard Parquet files, enabling ACID transactions, time travel, and schema enforcement. These features solve common data engineering pain points such as partial writes, data corruption, and the inability to revert to previous versions of data. Mastering these capabilities is essential for building reliable data lakes that provide the same consistency and reliability as traditional data warehouses.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
ACID transactions
Why this is correct
ACID compliance ensures that data reads and writes are atomic, consistent, isolated, and durable. This prevents partially written files from appearing in queries if a job fails mid-run. This capability is the foundation of reliable data pipelines, allowing for concurrent reads and writes without risking data corruption or inconsistency across the platform.
- ✗
Automatic indexing for all columns
Why it's wrong here
Delta Lake does not automatically index every column in the dataset. While it supports Z-Ordering and data skipping, these are opt-in features that the engineer must configure. Assuming automatic indexing is a misconception that can lead to performance issues if queries are not properly optimized using the correct clustering and partitioning strategies.
- ✓
Time travel
Why this is correct
Time travel allows users to query earlier versions of a table using the 'VERSION AS OF' or 'TIMESTAMP AS OF' syntax. This is invaluable for auditing, debugging, and reproducing previous analytical results without needing to maintain separate snapshots of the data, significantly reducing storage costs and management complexity in production environments.
- ✓
Schema enforcement
Why this is correct
Schema enforcement prevents the insertion of data that does not match the defined table schema. This protects the data lake from 'schema drift' where unexpected data formats cause downstream failures. By enforcing structure at write time, Delta Lake ensures that analytical tables remain consistent and usable for all downstream BI and ML applications.
- ✗
Built-in support for real-time web socket streaming
Why it's wrong here
Delta Lake does not handle web socket streaming; it is a storage layer for structured data. While it integrates with Structured Streaming for incremental data processing, it is not a networking or communication tool. Confusing storage capabilities with communication protocols is a common error when planning the architecture of real-time data ingestion systems.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-DE-Pro practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-DE-Pro exam.