DP-700 Implement and Manage an Analytics Solution Practice Question
You are optimizing a Fabric Warehouse for complex analytical queries involving large fact tables. Which TWO actions should you perform to improve query performance?
⚠ Common exam trap
Candidates often suggest adding more compute resources or changing the warehouse size, which is a costly 'brute force' approach that ignores the fundamental performance gains from proper indexing and partitioning.
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 on the fact table.
Optimizing warehouse performance requires a combination of effective indexing strategies and data distribution management. By choosing the right clustering columns, you reduce data scanning requirements, while partition pruning ensures the engine only reads relevant segments. These techniques are fundamental for scaling analytical workloads in Microsoft Fabric, as they directly influence the amount of data processed during query execution and help maintain consistent performance under high concurrency.
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 on the fact table.
Why this is correct
Clustered columnstore indexes provide high compression and efficient analytical query performance by storing data by column. This is the optimal structure for large fact tables in a relational warehouse, allowing the storage engine to skip irrelevant columns during query execution, significantly reducing I/O and CPU overhead.
- ✗
Convert all tables to heap structures.
Why it's wrong here
Heap structures lack inherent ordering, forcing the SQL engine to perform full table scans for most analytical queries. This results in poor performance for large datasets, as the engine cannot leverage metadata for data skipping or efficient retrieval, making heaps unsuitable for primary fact tables in a data warehouse.
- ✓
Use partitioning columns that are frequently used in WHERE clauses.
Why this is correct
Partitioning on columns used in filter predicates allows the query engine to prune partitions, eliminating large amounts of irrelevant data from the scan. This effectively reduces query duration and resource consumption, which is critical for maintaining high performance when working with massive fact tables in Fabric environments.
- ✗
Increase the number of rows per file to 1 billion.
Why it's wrong here
Extremely large files hinder parallel processing capabilities in Fabric. The engine benefits from a balance where file sizes are large enough to be efficient but small enough to allow for effective parallel tasks. A billion rows per file would likely lead to massive memory pressure and failed query executions.
- ✗
Disable statistics collection for all columns.
Why it's wrong here
Statistics are vital for the query optimizer to create efficient execution plans. Without accurate statistics, the optimizer might choose suboptimal join orders or scan methods, leading to severe performance degradation. Managing statistics is a core maintenance task for ensuring the engine generates the most performant execution plans.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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