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
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
Key term
Blob storage
Blob storage is a cloud service for storing large amounts of unstructured data, such as text or binary data, like documents, images, and videos.
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