Databricks-DE-Assoc Databricks Intelligence Platform Practice Question
A data engineer is designing a pipeline on Databricks that requires ACID transactions and schema enforcement for streaming data. Which storage abstraction should they use to ensure data reliability and support time travel?
⚠ Common exam trap
Test-takers often choose Parquet files or standard Spark dataframes, overlooking the requirement for built-in transactional logs needed for time travel and ACID guarantees.
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
✓
Delta Lake tables
Delta Lake provides the foundation for the Databricks Lakehouse, offering ACID transactions, schema enforcement, and versioning capabilities. By utilizing Delta tables, engineers can perform time travel via transaction logs to audit data or recover from accidental deletions. This storage layer is essential for maintaining high-quality data throughout the pipeline lifecycle, preventing partial writes and ensuring consistent reads for downstream analytics users in the Lakehouse environment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Parquet files in S3 without a transaction log
Why it's wrong here
Raw Parquet files lack the transaction log required to enforce ACID properties or track schema evolution. Without the Delta Lake metadata layer, concurrent reads and writes can lead to file corruption or partial data reads, making it unsuitable for reliable production pipelines requiring consistent transactional state management.
- ✓
Delta Lake tables
Why this is correct
Delta Lake tables provide the necessary ACID transaction logs, enabling reliable reads and writes while supporting schema enforcement and evolution. These features ensure that data remains consistent during concurrent operations, and the transaction log specifically facilitates time travel, which is critical for compliance, auditing, and debugging production data pipelines.
- ✗
Hive-style partitioned CSV files
Why it's wrong here
CSV files are text-based and do not natively support schema enforcement or ACID transactions. Utilizing these for production pipelines risks data quality issues because there is no metadata layer to prevent corrupt writes or handle structural changes to the data schema during ongoing batch or streaming ingestion processes.
- ✗
A standard memory-mapped temporary table
Why it's wrong here
Memory-mapped temporary tables exist only for the duration of the Spark session and are not durable. They do not provide the persistent storage required for ACID transactions or time travel, as they are volatile and disappear once the cluster terminates, rendering them useless for long-term reliable data storage.
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JA
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-Assoc 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-Assoc exam.