DP-700 Monitor and Optimize an Analytics Solution Practice Question
Your organization uses a Fabric Spark notebook to process large volumes of streaming data into a Delta table. You notice that over time, query performance on the table is degrading significantly. Upon investigation, you find thousands of small files in the underlying OneLake folder. Which optimization strategy should you implement to resolve the performance issue while maintaining data integrity?
⚠ Common exam trap
Candidates often suggest manual file deletion or re-partitioning, which are inefficient and do not address the metadata overhead caused by small files in Delta tables as effectively as OPTIMIZE.
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
✓
Execute the OPTIMIZE command on the Delta table.
Small file problems are a common performance bottleneck in distributed systems like Spark when writing streaming data. Frequent small writes create metadata overhead and slow down file scanning during reads. Implementing the OPTIMIZE command with V-Order or enabling automatic compaction during the write process consolidates these small files into larger, more efficient Parquet files, significantly improving query execution speed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Run the VACUUM command with a retention period of zero hours.
Why it's wrong here
The VACUUM command is designed to remove files that are no longer referenced by a Delta table's transaction log to save storage space. It does not consolidate small files into larger ones; instead, it deletes old versions of files, which could lead to data loss if concurrent readers are active.
- ✗
Increase the Spark executor count to distribute the load.
Why it's wrong here
Adding more executors might speed up the initial write process, but it does not address the underlying issue of small file creation in the storage layer. In fact, increasing parallelism without proper shuffling or compaction can sometimes lead to even more small files being written to OneLake.
- ✓
Execute the OPTIMIZE command on the Delta table.
Why this is correct
The OPTIMIZE command performs data compaction by merging small files into larger, more optimal Parquet files while preserving the transaction log's integrity. This process reduces the number of file metadata operations required during a read, which directly addresses the root cause of the observed query performance degradation.
- ✗
Disable V-Order on the Spark session to reduce write overhead.
Why it's wrong here
V-Order is a Fabric-specific optimization that improves read performance by reordering data within Parquet files. Disabling it would likely make read performance worse rather than better. While it adds slight overhead during the write phase, it is not responsible for the creation of numerous small files.
Visual reference
About these practice questions
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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
This DP-700 practice question is part of Courseiva's free Microsoft 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 DP-700 exam.