DP-203 Design and implement data storage Practice Question
You are a data engineer for a large e-commerce company. The company uses Azure Synapse Analytics dedicated SQL pool as its enterprise data warehouse. A new business requirement mandates that the Sales fact table, which contains 2 billion rows, must support real-time analytics with a maximum query latency of 1 second for aggregations on the most recent 24 hours of data. The table is currently hash-distributed on CustomerID and partitioned monthly by SaleDate. The current query performance for recent data is slow due to full partition scans. The data is ingested via Azure Event Hubs and processed by Azure Stream Analytics, which writes to staging tables every minute. You need to redesign the storage to meet the latency requirement while minimizing cost and maintaining data integrity. Which approach should you take?
⚠ Common exam trap
Watch out — candidates often assume finer partitioning (Option A) always improves query performance, but they overlook that partition elimination still requires scanning the entire partition, and that a separate hot table with columnstore indexing is more efficient for real-time aggregations on a sliding window of recent data.
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
✓
Create a separate staging table for the last 24 hours of data with round-robin distribution and a clustered columnstore index. After each batch load, merge the staging table into the main partitioned Sales table. Queries for recent data should target the staging table.
It isolates the hot (recent 24-hour) data into a separate staging table with a clustered columnstore index, which is optimized for fast aggregations and high compression. This avoids full partition scans on the 2-billion-row main table, and merging into the partitioned table after each batch load maintains data integrity without requiring expensive repartitioning or additional dedicated SQL pools.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase partition granularity to hourly partitions for the Sales table.
Why it's wrong here
Even hourly partitions would be large and may not meet 1-second latency; also increases partition management overhead.
- ✗
Change the Sales table to use a clustered index on SaleDate and a round-robin distribution.
Why it's wrong here
Clustered index is inefficient for large fact tables; round-robin distribution causes data movement.
- ✓
Create a separate staging table for the last 24 hours of data with round-robin distribution and a clustered columnstore index. After each batch load, merge the staging table into the main partitioned Sales table. Queries for recent data should target the staging table.
Why this is correct
The staging table is small, enabling fast queries for recent data; merging maintains the historical archive.
- ✗
Provision a second dedicated SQL pool optimized for real-time queries and replicate the recent data there.
Why it's wrong here
A second pool adds significant cost and complexity; replication overhead may impact latency.
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
Key term
Azure Stream Analytics
Azure Stream Analytics is a fully managed, real-time data processing service that analyzes and transforms high volumes of streaming data from various sources to deliver low-latency insights and trigger actions.
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