DP-700 Ingest and Transform Data Practice Question
Which file format should you choose for a Lakehouse to ensure the best support for ACID transactions and high-performance analytical queries?
⚠ Common exam trap
Candidates often choose standard Parquet or CSV formats, forgetting that Delta format is mandatory for unlocking native ACID transactions and time travel in Fabric Lakehouses.
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
Delta Lake is the open-source storage layer that brings reliability and performance to the data lake. It is the native format for Fabric Lakehouses, providing ACID transactions, schema enforcement, and time travel capabilities. Using Delta format allows Fabric to optimize file access, leverage metadata, and significantly improve query performance for BI and data science workloads, making it the industry standard for modern data lake architectures.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
CSV
Why it's wrong here
CSV is a text-based format that lacks metadata and schema support. It does not provide ACID transactional integrity, making it unsuitable for modern data engineering pipelines that require data consistency. Analytical engines cannot optimize CSV files, resulting in slow query performance and increased overhead for data management tasks.
- ✓
Delta
Why this is correct
Delta Lake is the optimized format for Fabric, providing essential ACID features and high performance. It supports time travel, schema evolution, and efficient data versioning. By using Delta, you enable the Lakehouse to perform at the speed of a data warehouse while maintaining the flexibility of a data lake.
- ✗
JSON
Why it's wrong here
JSON is useful for semi-structured data ingestion but is inefficient for large-scale analytical queries. It does not support ACID transactions natively and lacks the performance optimizations found in columnar formats. Using JSON for analytical storage leads to massive I/O overhead as the engine must parse the entire file.
- ✗
Avro
Why it's wrong here
Avro is a row-based format commonly used for data streaming and messaging systems. While it provides good schema support, it is not optimized for OLAP-style analytical queries. Columnar formats like Delta or Parquet are vastly superior for query performance in a Lakehouse environment where filtering and aggregation are frequent.
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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 Microsoft exam blueprint
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