- A
Azure Data Lake Storage Gen2
Why wrong: Azure Data Lake Storage Gen2 is a scalable storage service, but it does not provide a query engine. It must be queried using services like Azure Synapse or Azure Databricks, adding complexity.
- B
Azure SQL Database
Why wrong: Azure SQL Database is a transactional relational database. It is not designed for petabyte-scale analytical workloads and would struggle with performance and cost at such volumes.
- C
Azure Synapse Analytics dedicated SQL pool
Azure Synapse Analytics dedicated SQL pool uses MPP and columnar storage to execute complex queries over huge datasets efficiently. It is purpose-built for large-scale data warehousing and analytical workloads.
- D
Azure Cosmos DB
Why wrong: Azure Cosmos DB is a NoSQL operational database optimized for low-latency reads/writes. It is not designed for large-scale analytical queries or columnar storage and would be inefficient for this use case.
Quick Answer
Azure Synapse Analytics dedicated SQL pool is the correct choice because it is a massively parallel processing (MPP) engine architected for petabyte-scale data warehousing, enabling fast results on complex aggregations and time-series queries by distributing data across 60 distributions and leveraging columnstore indexes for high compression and scan efficiency. On the Microsoft Azure Data Fundamentals DP-900 exam, this scenario tests your understanding of which Azure service handles large-scale analytical workloads—a common trap is confusing Azure Synapse serverless SQL pool (which is better for ad-hoc queries over data lakes) with the dedicated SQL pool’s persistent, MPP-optimized storage. Remember that dedicated SQL pool is purpose-built for petabyte-scale data warehousing with predictable performance, while serverless is for on-demand exploration. Memory tip: think “Dedicated for Data Warehousing, Serverless for Spot Queries.”
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 manufacturer collects sensor data from thousands of IoT devices every second. The data is ingested into Azure Event Hubs and then needs to be stored for historical analysis. The analytics team will run complex aggregations and time-series queries over petabytes of data, expecting fast results even with large scans. Which Azure service should be used as the analytical data store?
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 the correct choice because it is a massively parallel processing (MPP) engine designed for petabyte-scale data warehousing. It can run complex aggregations and time-series queries with fast results by distributing data across 60 distributions and using columnstore indexes for high compression and scan efficiency.
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 Data Lake Storage Gen2
Why it's wrong here
Azure Data Lake Storage Gen2 is a scalable storage service, but it does not provide a query engine. It must be queried using services like Azure Synapse or Azure Databricks, adding complexity.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a transactional relational database. It is not designed for petabyte-scale analytical workloads and would struggle with performance and cost at such volumes.
- ✓
Azure Synapse Analytics dedicated SQL pool
Why this is correct
Azure Synapse Analytics dedicated SQL pool uses MPP and columnar storage to execute complex queries over huge datasets efficiently. It is purpose-built for large-scale data warehousing and analytical workloads.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Azure Cosmos DB
Why it's wrong here
Azure Cosmos DB is a NoSQL operational database optimized for low-latency reads/writes. It is not designed for large-scale analytical queries or columnar storage and would be inefficient for this use case.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse Azure Data Lake Storage Gen2 (a storage layer) with a query engine, assuming it can directly perform fast analytical queries, when in fact it requires a compute service like Synapse or Spark on top.
Detailed technical explanation
How to think about this question
Under the hood, Synapse dedicated SQL pool uses a control node to distribute queries to compute nodes, each managing a set of distributions (hash-distributed or round-robin). Columnstore indexes store data column-wise, enabling compression ratios of 10x or more and allowing queries to skip entire columns, dramatically reducing I/O for large scans. In real-world IoT scenarios, this architecture allows the analytics team to run windowed aggregations like AVG over 30-second intervals across billions of sensor readings in seconds.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this DP-900 question test?
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 the correct choice because it is a massively parallel processing (MPP) engine designed for petabyte-scale data warehousing. It can run complex aggregations and time-series queries with fast results by distributing data across 60 distributions and using columnstore indexes for high compression and scan efficiency.
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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Last reviewed: Jun 11, 2026
This DP-900 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-900 exam.
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