DP-900 Describe an analytics workload on Azure Practice Question
A data engineering team needs to analyze petabytes of historical sales data stored in Azure Data Lake Storage Gen2. They require the ability to run complex SQL queries that join multiple tables and need high performance. The solution must separate compute from storage to allow independent scaling of resources. Which Azure service should they use?
⚠ Common exam trap
It's easy for candidates to confuse Azure SQL Database's familiar SQL interface with the ability to handle petabyte-scale analytics, overlooking the fundamental architectural difference between OLTP and MPP data warehouse systems.
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
✓
Azure Synapse Analytics dedicated SQL pool
Azure Synapse Analytics dedicated SQL pool is designed for petabyte-scale data warehousing, providing massively parallel processing (MPP) to run complex SQL queries across multiple tables with high performance. It separates compute from storage, allowing independent scaling of compute resources without moving data, which aligns with the requirement for decoupled scaling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Azure Synapse Analytics dedicated SQL pool
Why this is correct
Azure Synapse Analytics dedicated SQL pool is purpose-built for this scenario: it is a massively parallel processing (MPP) data warehouse that separates compute and storage, allowing independent scaling and query isolation. The control node distributes complex analytical T-SQL queries across compute nodes, each processing subsets of data stored in Azure Storage, enabling petabyte-scale historical analytics. This architecture is fundamentally different from OLTP or NoSQL systems, making it the correct choice for large-scale relational analytical workloads.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a fully managed relational database built on the SQL Server engine, optimized for online transaction processing (OLTP) with row-based storage, fine-grained locks, and high-concurrency point operations. It does not adopt a massively parallel processing (MPP) architecture; even the Hyperscale tier separates compute and storage but still executes queries on a single primary compute node, which cannot distribute scans and complex joins across many nodes. Consequently, it lacks the scalability and performance characteristics required for petabyte-scale historical analytics.
- ✗
Azure Cosmos DB
Why it's wrong here
Azure Cosmos DB is a NoSQL, multi-model, globally distributed database designed for low-latency, single-digit millisecond access to transactional data across regions. Its indexing model and query engine are optimized for point reads and small, key-based queries rather than large analytical scans; cross-partition queries are inherently slower and require significant request units (RUs). It also does not support complex relational joins or massive aggregations over petabytes, making it unsuitable for historical analytical workloads.
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Azure Table Storage
Why it's wrong here
Azure Table Storage is a highly available, low-cost, schema-free NoSQL key/value store that provides fast point lookups and simple range scans based on partition key and row key. It offers only limited query capabilities—no joins, grouping, or rich SQL operators—and its design prioritizes massive ingestion and cheap storage over complex analytical processing. Even with partitioning, it cannot efficiently scan and aggregate petabytes of data for historical analysis, so it is completely wrong for this requirement.
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Data Roles and Core Concepts
Key term
Data lake
A data lake is a centralized storage repository that holds vast amounts of raw data in its native format until it is needed for analysis.
Key term
Data Lake Storage Gen2
Data Lake Storage Gen2 is a cloud-based storage service that combines a scalable data lake with enterprise-grade file system capabilities for big data analytics.
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