The answer is Azure Synapse Analytics dedicated SQL pool. This service is the correct choice because it is purpose-built for petabyte-scale data warehousing, leveraging massively parallel processing (MPP) to execute complex SQL queries across multiple tables with high performance, while its architecture separates compute from storage, allowing you to independently scale compute resources without moving or duplicating data in Azure Data Lake Storage Gen2. On the Microsoft Azure Data Fundamentals DP-900 exam, this scenario tests your understanding of when to choose a dedicated SQL pool over serverless options or Azure SQL Database; a common trap is selecting Azure SQL Database for large-scale analytics, but remember that dedicated SQL pool is the only service designed for petabyte-scale MPP workloads with decoupled compute and storage. Memory tip: think "Dedicated for Data Warehousing" — if the question mentions petabytes and separate scaling of compute, it's a dedicated SQL pool.
DP-900 Describe an analytics workload on Azure Practice Question
This DP-900 practice question tests your understanding of describe an analytics workload on azure. 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.
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?
Correct. It is built for petabyte-scale data warehousing with separate compute and storage, ideal for complex analytical queries.
B
Azure SQL Database
Why wrong: Incorrect. While it supports SQL, it is designed for OLTP workloads and does not scale to petabyte-level analytics with separate compute/storage in the same way.
C
Azure Cosmos DB
Why wrong: Incorrect. Cosmos DB is a NoSQL database optimized for low-latency, globally distributed access, not for large-scale SQL analytical queries.
D
Azure Table Storage
Why wrong: Incorrect. Table Storage is a simple key-value store with limited query capabilities, not suitable for petabyte-scale analytics.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Correct. It is built for petabyte-scale data warehousing with separate compute and storage, ideal for complex analytical queries.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure SQL Database
Why it's wrong here
Incorrect. While it supports SQL, it is designed for OLTP workloads and does not scale to petabyte-level analytics with separate compute/storage in the same way.
✗
Azure Cosmos DB
Why it's wrong here
Incorrect. Cosmos DB is a NoSQL database optimized for low-latency, globally distributed access, not for large-scale SQL analytical queries.
✗
Azure Table Storage
Why it's wrong here
Incorrect. Table Storage is a simple key-value store with limited query capabilities, not suitable for petabyte-scale analytics.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
Azure Synapse dedicated SQL pool uses a control node to distribute queries across multiple compute nodes, each processing a portion of data stored in Azure Storage blobs via PolyBase. This separation enables pausing compute to reduce costs while data remains intact, and scaling out compute nodes for faster query execution without data movement. In real-world scenarios, teams can run TPC-H benchmark queries on hundreds of terabytes with consistent performance by leveraging table distribution and partitioning strategies.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this DP-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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