Objective 3.3
DP-700Optimize performance
Objective 3.3 sits in Monitor and optimize an analytics solution, which carries 34% of the DP-700 Fabric Data Engineer exam. The questions below are original, written from the official objective title above, and each explanation cites the Microsoft Azure page it rests on.
Objective title verbatim from the official objectives. Microsoft Azure exam page ↗
A worked example
Shown solved, with the whole explanation open: this is what every question here carries.
A lakehouse Delta table has accumulated many small files and reads are slow. Which maintenance action compacts them?
Correct.
Concept
Compaction merges many small Parquet files into fewer larger ones to speed reads.
Why D
The Optimize option compacts small Parquet files into larger files for efficient reads.
Source
Microsoft Learn, Delta Table Maintenance in Microsoft Fabric, checked August 2026Select the On option to compact small Parquet files into larger files for more efficient reads.
Now you: objective 3.3 questions
No account needed. The explanation opens when you answer.
Sample question 1 of 3
A Spark-created string column with no defined length is read by the warehouse. As what type does the warehouse recognize it, and what should you do?
Sample question 2 of 3
For a string column of variable-length names in a warehouse, which choice improves I/O over char(n)?
Sample question 3 of 3
A decimal column is created as just decimal with no precision or scale. What definition does the warehouse apply?
That’s 3 of the full DP-700 Fabric Data Engineer bank.
Keep going free: 10 questions per certification in bank practice, with no account.
Continue practisingRead the sources
These are the official pages the questions above cite. Reading them is studying the objective from the primary source, which is what the explanations point you toward anyway.