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DP-900 Describe core data concepts Practice Question

A data analyst needs to query a large dataset stored in Azure Blob Storage using serverless SQL pool in Azure Synapse Analytics. Which data format should they use to minimize storage costs while still supporting efficient querying?

⚠ Common exam trap

Candidates often assume all compressed formats (like Avro) are equally efficient for analytics, but Azure Synapse serverless SQL pool is specifically optimized for columnar formats like Parquet, not row-oriented ones.

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

Parquet

Parquet is a columnar storage format that compresses data efficiently and supports predicate pushdown, allowing serverless SQL pool in Azure Synapse to read only the necessary columns and rows. This minimizes storage costs while maintaining high query performance, unlike row-oriented formats such as CSV or JSON.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • CSV

    Why it's wrong here

    CSV is a row-oriented, plain-text format with no schema or type enforcement, so every query must parse and scan entire rows even when only a few columns are needed. It also compresses poorly compared with columnar formats, leading to higher storage costs and slower analytical performance on large Azure datasets. This makes CSV inefficient for read-heavy analytical workloads.

  • JSON

    Why it's wrong here

    JSON stores data as nested, self-describing text, adding significant overhead from repeated field names and brackets, which drives up size and parse time. Because it lacks schema enforcement, data types can drift and queries must handle inconsistent shapes, making scans slower and storage costs higher. While flexible for APIs and documents, JSON is a poor fit for large-scale analytical queries.

  • Parquet

    Why this is correct

    Parquet is a columnar storage format that groups values by column, enabling modern compression techniques like dictionary and run-length encoding to dramatically reduce storage footprint. Analytical engines can push predicate filters and column projections down to the file layer, reading only the needed columns and row groups, which minimizes I/O and query latency. This design makes Parquet the optimal choice for large analytical workloads in Azure, including Azure Synapse, Databricks, and Data Lake Storage.

  • Avro

    Why it's wrong here

    Avro is row-based and optimized for compact, fast serialization, especially in write-heavy or streaming pipelines where records are appended sequentially. Each record stores its values contiguously, so querying a subset of columns still requires reading full rows, and compression on column subsets is typically worse than with columnar storage. That row-oriented orientation makes Avro less efficient than Parquet for read-heavy analytics on large datasets.

Quick reference

Azure Blob Storage Tier Comparison

TierStorage CostRetrieval CostLatencyUse Case
HotHighestLowestImmediateActive data, frequent reads
CoolLowerHigherImmediateData accessed < once / month
ColdLower stillHigherImmediateData accessed < once / quarter
ArchiveLowestHighest + rehydration delayHoursLong-term compliance retention

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