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DP-203 Practice Question: Which THREE factors should you consider when…
Which THREE factors should you consider when choosing between rowstore and columnstore indexes in Azure Synapse Analytics?
⚠ Common exam trap
The trap here is that candidates may mistakenly think NULL handling or partitioning frequency are key differentiators, when in fact the core decision hinges on workload type—aggregations/scans (columnstore) versus singleton lookups (rowstore)—and table size thresholds like 1 TB where columnstore compression becomes critical.
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
✓
The table size is expected to be over 1 TB.
Columnstore indexes in Azure Synapse Analytics are optimized for large-scale data warehousing workloads, where table sizes exceeding 1 TB benefit from high compression and columnar storage, significantly improving scan and aggregation performance. Rowstore indexes, in contrast, are less efficient for such large datasets due to higher I/O and storage overhead.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The table contains many NULL values in indexed columns.
Why it's wrong here
Both index types handle NULL values.
- ✗
The table will be partitioned frequently.
Why it's wrong here
Partitioning is independent of index type.
- ✓
The table size is expected to be over 1 TB.
Why this is correct
Columnstore compression is more effective on large tables.
- ✓
The table has a high number of singleton lookups by a primary key.
Why this is correct
Rowstore is better for point lookups.
- ✓
The workload is heavy on aggregations and large scans.
Why this is correct
Columnstore excels at aggregations and scans.
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