DP-700 Monitor and Optimize an Analytics Solution Practice Question
You are tasked with optimizing the performance of a Delta table in a Fabric Lakehouse that is queried frequently by both Spark notebooks and the SQL Analytics Endpoint. Which TWO techniques should you use to improve data skipping and read performance?
⚠ Common exam trap
Candidates suggest general workspace settings or external caching solutions instead of utilizing built-in Delta table maintenance commands and Fabric optimizations.
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 table.
Optimizing Delta tables in Fabric involves managing file sizes and metadata. V-Order is a Fabric-specific optimization that sorts data to improve read speeds for Power BI and SQL, while the OPTIMIZE command consolidates small files. Together, these techniques ensure that the engine can effectively skip irrelevant data during a scan operation.
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 table.
Why this is correct
V-Order is a write-time optimization that applies a special sorting and compression algorithm to Parquet files in OneLake. This makes them highly compatible with the Fabric compute engines, particularly the SQL Analytics Endpoint and Power BI, by enabling more efficient data skipping and faster decompression during query execution.
- ✓
Run the OPTIMIZE command on the table.
Why this is correct
The OPTIMIZE command performs file compaction, taking many small Parquet files and merging them into larger, more efficient files. This reduces the overhead of opening and closing files during a query and allows the storage system to utilize high-throughput sequential reads, significantly improving overall scan performance for large datasets.
- ✗
Convert the table to a CSV format.
Why it's wrong here
Converting a Delta table to CSV would significantly degrade performance and lose all the benefits of the Delta Lake format, such as ACID transactions and metadata-based data skipping. CSV files are not optimized for analytical queries and would prevent the SQL Analytics Endpoint from functioning efficiently with the data.
- ✗
Increase the Spark executor memory only.
Why it's wrong here
Increasing executor memory can help with processing, but it does not address the underlying storage efficiency or data skipping capabilities of the table itself. Without optimizing the physical layout of the data, Spark will still have to read unnecessary data into memory, which is an inefficient use of resources.
- ✗
Disable the use of shortcuts to the data.
Why it's wrong here
Shortcuts are a virtualization feature that allows you to access data without moving it; disabling them does not inherently improve the performance of the underlying data. In fact, using shortcuts effectively can reduce data duplication and simplify management without impacting the read speed of the Delta tables themselves.
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.