Quick Answer
The optimal file format for dedicated SQL pool is Parquet with Snappy compression. This combination is ideal because Parquet’s columnar storage enables predicate pushdown and column pruning, drastically reducing the I/O required when reading only relevant columns from CSV files with varying row lengths. Snappy compression offers fast decompression with minimal CPU overhead, which is critical for high-throughput reads in Azure Synapse Analytics’ distributed MPP architecture. On the DP-203 exam, this scenario tests your understanding of how dedicated SQL pools perform best with columnar formats over row-based ones like CSV; a common trap is choosing ORC or Gzip, but Parquet is the native favorite for Synapse, and Snappy beats Gzip for speed. Remember the mnemonic “Parquet Snappy = Fast and Columnar Happy” to recall that columnar storage plus low-CPU compression is the key to optimal performance in dedicated SQL pools.
This DP-203 practice question tests your understanding of develop data processing. 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.
You are designing a data pipeline in Azure Synapse Analytics to ingest data from Azure Blob Storage into a dedicated SQL pool. The source files are CSV with varying row lengths, and you need to ensure optimal performance for reads. Which file format and compression should you recommend?
Avro with Deflate compression
Why wrong: Avro is row-oriented, not optimal for analytical queries.
CSV with Gzip compression
Why wrong: CSV is not columnar, leading to higher I/O and slower performance.
Parquet with Snappy compression
Parquet is columnar and Snappy provides fast compression/decompression, ideal for Synapse dedicated SQL pool.
ORC with Zlib compression
Why wrong: ORC is not natively supported in Synapse dedicated SQL pools.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Parquet with Snappy compression
Parquet with Snappy compression is optimal for dedicated SQL pools in Azure Synapse Analytics because Parquet is a columnar format that enables efficient predicate pushdown and column pruning, reducing I/O. Snappy provides fast compression/decompression with minimal CPU overhead, which is critical for high-throughput reads in a distributed MPP environment.
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.
Avro with Deflate compression
Why it's wrong here
Avro is row-oriented, not optimal for analytical queries.
CSV with Gzip compression
Why it's wrong here
CSV is not columnar, leading to higher I/O and slower performance.
Parquet with Snappy compression
Why this is correct
Parquet is columnar and Snappy provides fast compression/decompression, ideal for Synapse dedicated SQL pool.
Related concept
Read the scenario before looking for a memorised answer.
ORC with Zlib compression
Why it's wrong here
ORC is not natively supported in Synapse dedicated SQL pools.
Common exam traps
Microsoft often tests the misconception that row-based formats like Avro or CSV are suitable for analytical workloads, but the trap here is that columnar formats (Parquet/ORC) are required for optimal read performance in Synapse dedicated SQL pools, and Snappy is preferred over Zlib for speed-critical pipelines.
Detailed technical explanation
Parquet stores data in column chunks with embedded statistics (min/max, null counts), allowing Synapse to skip entire row groups during predicate filtering. Snappy compression operates at the page level within Parquet, enabling parallel decompression across multiple nodes without data dependency. In practice, for a 100 GB CSV dataset, switching to Parquet with Snappy can reduce read times by 60-80% in dedicated SQL pools due to reduced data scanning and efficient compression.
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
Develop data processing — This question tests Develop data processing — Read the scenario before looking for a memorised answer..
The correct answer is: Parquet with Snappy compression — Parquet with Snappy compression is optimal for dedicated SQL pools in Azure Synapse Analytics because Parquet is a columnar format that enables efficient predicate pushdown and column pruning, reducing I/O. Snappy provides fast compression/decompression with minimal CPU overhead, which is critical for high-throughput reads in a distributed MPP environment.
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 30, 2026
This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.
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