Databricks-Spark-Assoc Using Spark SQL Practice Question
Which TWO of the following are benefits of using Delta Lake over standard Parquet files in Spark SQL?
⚠ Common exam trap
Candidates often mistakenly select performance-related features like 'automatic indexing' or 'auto-scaling' as benefits of Delta Lake, confusing general cloud platform capabilities with the specific ACID and schema features provided by the Delta format.
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
✓
Support for ACID transactions.
Delta Lake extends the capabilities of standard Parquet by adding a transaction log and metadata layer. This enables ACID transactions, Time Travel, and schema enforcement, which are critical for robust data engineering. For a Databricks certified developer, knowing why Delta is the preferred format for the Lakehouse architecture is essential for building scalable, reliable, and maintainable data systems that surpass the limitations of raw file-based storage.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Support for ACID transactions.
Why this is correct
ACID transactions ensure data integrity by allowing multiple concurrent readers and writers to interact with the data without corruption. This is a critical feature for data pipelines where consistency is paramount, and standard Parquet files do not provide this level of transactional guarantee by default.
- ✓
Ability to perform schema evolution and enforcement.
Why this is correct
Schema enforcement and evolution prevent bad data from being written to the table and allow the schema to adapt over time as business requirements change. Parquet files are schema-blind by default, requiring external metadata management, whereas Delta integrates these features directly into the storage layer.
- ✗
Faster raw I/O performance for single-column reads.
Why it's wrong here
Delta Lake uses Parquet as its underlying storage format. Therefore, raw I/O performance for single-column reads is identical for both. The performance benefits of Delta Lake come from metadata management, data skipping, and file compaction, not from changes to the underlying column-store format itself.
- ✗
Automatic conversion of CSV files to optimized Parquet.
Why it's wrong here
Delta Lake does not automatically convert CSV files. While it provides tools like 'CONVERT TO DELTA' to migrate existing Parquet data, the conversion of CSV data must be performed as a data transformation task, typically using 'spark.read.csv' followed by a 'write' operation.
- ✗
Compatibility with legacy Hive Metastore versions without modification.
Why it's wrong here
Delta Lake requires specific table properties and integration with the Databricks or Delta-aware metastore. It is not fully compatible with legacy Hive metastores without proper configuration. Incorrectly assuming legacy compatibility can lead to metadata sync issues and failures in existing data warehousing workflows.
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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
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