This DP-203 practice question tests your understanding of design and implement data storage. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which THREE factors should be considered when designing a partitioning strategy for a large fact table in Azure Synapse Analytics dedicated SQL pool?
Index type
Why wrong: Index strategy is separate.
Data compression
Partition size affects compression efficiency.
Distribution key
Why wrong: Not directly related to partitioning.
Partition column
Determines partition boundaries and elimination.
Number of partitions
Impacts performance and maintenance.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Data compression
Data compression is a key factor in partitioning strategy because it directly impacts storage efficiency and query performance. In Azure Synapse Analytics dedicated SQL pool, columnstore compression is applied per partition, and choosing the right compression level (e.g., COLUMNSTORE vs. COLUMNSTORE_ARCHIVE) can significantly reduce I/O and improve scan speeds for large fact tables.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Index type
Why it's wrong here
Index strategy is separate.
Data compression
Why this is correct
Partition size affects compression efficiency.
Related concept
Read the scenario before looking for a memorised answer.
Distribution key
Why it's wrong here
Not directly related to partitioning.
Partition column
Why this is correct
Determines partition boundaries and elimination.
Related concept
Read the scenario before looking for a memorised answer.
Number of partitions
Why this is correct
Impacts performance and maintenance.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates confuse distribution key (which controls data placement across nodes) with partition column (which controls data organization within a node), leading them to incorrectly select distribution key as a partitioning factor.
Detailed technical explanation
Partitioning in Synapse dedicated SQL pool uses partition elimination to skip irrelevant partitions during queries, reducing I/O. The number of partitions must balance between too few (large partitions, less elimination) and too many (metadata overhead, small rowgroups in columnstore). Real-world scenarios often use date-based partitioning (e.g., monthly) to align with data loading and retention policies, while distribution key is chosen to minimize data movement during joins.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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FAQ
Design and implement data storage — This question tests Design and implement data storage — Read the scenario before looking for a memorised answer..
The correct answer is: Data compression — Data compression is a key factor in partitioning strategy because it directly impacts storage efficiency and query performance. In Azure Synapse Analytics dedicated SQL pool, columnstore compression is applied per partition, and choosing the right compression level (e.g., COLUMNSTORE vs. COLUMNSTORE_ARCHIVE) can significantly reduce I/O and improve scan speeds for large fact tables.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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