DP-700 Monitor and Optimize an Analytics Solution Practice Question
You are optimizing a Data Warehouse in Microsoft Fabric. Which TWO strategies will effectively improve the performance of read-heavy workloads?
⚠ Common exam trap
Candidates incorrectly suggest transactional features or streaming options designed for write performance instead of choosing structures optimized for analytical read queries.
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
✓
Implement Clustered Columnstore Indexes.
For read-heavy workloads in a data warehouse, indexing and proper table design are critical. Clustered Columnstore Indexes provide massive data compression and are optimized for analytical queries that scan large volumes of data. Additionally, implementing partitions allows the engine to prune data at the storage level, ensuring that only relevant partitions are scanned, thus reducing the total I/O and accelerating query execution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement Clustered Columnstore Indexes.
Why this is correct
Clustered Columnstore Indexes are designed for high-performance analytical queries. They store data in columns rather than rows, allowing for significant compression and efficient scanning, which is ideal for large-scale data warehouses where read performance is the primary concern for users.
- ✗
Increase the number of rows per transaction.
Why it's wrong here
Increasing the number of rows per transaction is generally associated with write-heavy workloads and transactional processing. In a data warehouse environment, this does not help read performance and can actually lead to lock contention, slowing down read queries during concurrent operations.
- ✓
Apply table partitioning.
Why this is correct
Partitioning divides a large table into smaller, more manageable pieces based on a specific column. This enables partition pruning, where the engine skips entire partitions that do not meet the query filter criteria, significantly reducing the amount of data scanned and improving query performance.
- ✗
Use row-based storage exclusively.
Why it's wrong here
Row-based storage is optimized for transactional systems (OLTP) where individual records are frequently updated or retrieved. It is inefficient for analytical workloads because it requires reading entire rows even when only a few columns are needed, resulting in excessive I/O for analytical queries.
- ✗
Disable data compression.
Why it's wrong here
Disabling compression increases the amount of data read from disk, which severely degrades performance in an analytical environment. Compression is essential for reducing I/O, and since read-heavy workloads rely on efficient scanning, keeping data compressed is vital for keeping queries running quickly.
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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.