DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
You are a data engineer for a large e-commerce company. You have an Azure Synapse Analytics dedicated SQL pool that stores transactional data. The pool is currently at DWU1000c. You have a critical dashboard that runs a complex query every 5 minutes. The query scans a large fact table partitioned by date. The query performance is degrading over time as data accumulates. You need to improve performance without increasing DWUs or changing the dashboard query. You also need to minimize data movement overhead. You have the following options:
A. Create a columnstore index on the fact table with a partition alignment. B. Create a materialized view that aggregates the data at the partition level. C. Implement result-set caching and set the cache to expire every 5 minutes. D. Redistribute the fact table using hash distribution on the date column.
Which option should you choose?
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 result-set caching and set the cache to expire every 5 minutes.
Result-set caching stores the exact query results and can serve the dashboard query instantly if the underlying data has not changed. Since the query runs every 5 minutes, setting the cache expiration to 5 minutes ensures fresh data. Option A (columnstore index) is wrong because the table likely already has a columnstore index (the default in Synapse). Option B (materialized view) is wrong because materialized views require maintenance and may not match the exact query. Option D (hash distribution on date) is wrong because it can cause data skew and does not reduce scan overhead as effectively as caching.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a columnstore index on the fact table with a partition alignment.
Why it's wrong here
Hash distribution on date can cause skew and does not reduce scan overhead.
- ✗
Create a materialized view that aggregates the data at the partition level.
Why it's wrong here
Materialized views require maintenance and may not match the exact query.
- ✓
Implement result-set caching and set the cache to expire every 5 minutes.
Why this is correct
The table likely already has a columnstore index; this may not provide additional benefit.
- ✗
Redistribute the fact table using hash distribution on the date column.
Why it's wrong here
Result-set caching stores query results and can serve repeated queries quickly.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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