DP-600 Maintain a Data Analytics Solution Practice Question
A large semantic model using Direct Lake mode is experiencing slow query performance. You want to optimize the model's maintenance and performance. Which THREE actions should you take?
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
✓
Run the OPTIMIZE command on the underlying Delta tables in the Lakehouse.
Optimizing Direct Lake models requires a combination of data engineering and model configuration. Direct Lake relies on the ability to load Parquet files directly into memory, so the physical layout of the files and the complexity of the DAX measures are critical factors. Maintaining these aspects ensures that the model can stay in 'Direct Lake' mode without falling back to 'DirectQuery'.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert all calculated columns in the semantic model to Power Query M columns.
Why it's wrong here
While moving logic upstream is generally good practice, converting calculated columns specifically to M does not address the core performance issues of Direct Lake. Direct Lake models actually perform best when logic is handled in the Lakehouse via Spark or SQL before the data reaches the model, as calculated columns in the model can trigger a fallback.
- ✓
Run the OPTIMIZE command on the underlying Delta tables in the Lakehouse.
Why this is correct
The OPTIMIZE command performs file compaction (bin-packing) on Delta tables, which reduces the number of small files and improves read performance. Since Direct Lake reads Parquet files from the Lakehouse, having optimized, appropriately sized files is essential for fast memory loading and efficient query execution within the semantic model's engine.
- ✓
Set the 'Direct Lake behavior' property to 'Direct Lake only' to prevent fallback.
Why this is correct
By default, Direct Lake models may fall back to DirectQuery mode if the query cannot be satisfied in memory, which significantly slows down performance. Setting the behavior to 'Direct Lake only' helps in identifying performance bottlenecks during development by forcing an error if the model cannot stay in memory, allowing for targeted optimization.
- ✗
Increase the 'Max Intermediate Rows' setting in the Power BI capacity settings.
Why it's wrong here
Adjusting the maximum intermediate rows is a capacity-level setting that limits the size of result sets during query processing. While it might prevent some queries from failing, it does not optimize the performance of the model itself. It is a safety threshold rather than an optimization technique for improving the speed of Direct Lake data retrieval.
- ✓
Ensure that the Delta tables use V-Order to improve the reading speed of Parquet files.
Why this is correct
V-Order is a Microsoft Fabric-specific optimization for Parquet files that reorganizes data to enable faster reading by the Power BI engine. When Delta tables are V-Ordered, the Direct Lake mode can load data into memory much more efficiently. This is a critical maintenance step for any high-performance Direct Lake implementation in Fabric.
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-600 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-600 exam.