DP-700 Ingest and Transform Data Practice Question
You are optimizing a Spark Notebook in Fabric that processes large Parquet files. Which TWO actions will improve the performance of data transformations?
⚠ Common exam trap
Candidates often choose manual file compression or generic partitioning methods without realizing that Fabric specifically relies on native V-Order and well-defined column partitioning to accelerate Spark engine performance on Delta tables.
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
✓
Enable V-Order on the Delta tables.
Optimizing Spark performance in Fabric involves managing how data is distributed across the cluster and how it is physically stored. Using V-Order ensures that files are optimized for Fabric's compute engines, while proper partitioning prevents data skew and allows for efficient parallel processing across the available Spark nodes in the workspace.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable V-Order on the Delta tables.
Why this is correct
V-Order is a write-time optimization that applies special sorting, row group distribution, and compression to Delta files. This allows Fabric compute engines, including Spark and the SQL Analytics Endpoint, to read the data significantly faster by reducing the amount of data scanned during query execution.
- ✗
Increase the 'isSequential' property to True.
Why it's wrong here
The isSequential property is a setting used in Data Factory pipeline ForEach activities to control whether iterations run one after another or in parallel. It has no relevance to the internal transformation performance of a Spark Notebook or how Spark handles data processing on its cluster.
- ✓
Apply partitioning to the data based on frequently filtered columns.
Why this is correct
Partitioning divides data into smaller, manageable chunks based on specific column values. This enables Spark to skip irrelevant data files during queries, which reduces I/O overhead and improves transformation speed, especially when dealing with massive datasets that are frequently filtered by specific attributes like date or region.
- ✗
Convert all files to the Avro format for faster reads.
Why it's wrong here
Avro is a row-based format that is excellent for write-intensive workloads but is generally slower for analytical queries compared to columnar formats like Parquet or Delta. Fabric is optimized for Delta Lake, and moving away from Delta would actually degrade performance across the platform's engines.
- ✗
Disable the Spark 'High Concurrency' mode.
Why it's wrong here
High Concurrency mode in Fabric Spark allows multiple users or jobs to share the same Spark session, which reduces startup latency and improves resource utilization. Disabling this mode would not improve transformation performance and would likely increase the time spent waiting for session initialization.
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Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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