- A
Azure Synapse Analytics
Correct. Synapse Analytics provides a SQL-based engine optimized for large-scale analytical queries and can directly query data in Data Lake Storage with PolyBase or CETAS.
- B
Azure SQL Database
Why wrong: Incorrect. Azure SQL Database is designed for transactional workloads (OLTP) and is not optimized for petabyte-scale analytical queries. It would require data loading and may not handle joins across large datasets efficiently.
- C
Azure Analysis Services
Why wrong: Incorrect. Azure Analysis Services provides semantic modeling and in-memory caching for business intelligence, but it is not a primary data store for large CSV files and does not offer SQL querying of raw data.
- D
Azure HDInsight
Why wrong: Incorrect. HDInsight is a managed Hadoop/Spark service that can process large datasets, but it typically requires more complex programming (e.g., Spark SQL) and is not as optimized for instant SQL querying as Synapse Analytics.
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 retail chain collects daily sales data from hundreds of stores. The data is stored as CSV files in Azure Data Lake Storage Gen2. The analytics team needs to run complex SQL queries that join sales data with product dimensions and aggregate results across petabytes of data. Queries must return results within seconds. Which Azure service is best suited for this analytical workload?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
Azure Synapse Analytics (formerly SQL Data Warehouse) is the correct choice because it is a distributed query engine designed for petabyte-scale data warehousing. It uses Massively Parallel Processing (MPP) to distribute data across compute nodes, enabling complex SQL joins and aggregations on data stored in Azure Data Lake Storage Gen2 to return results in seconds via its SQL pool or serverless SQL endpoint.
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
Why this is correct
Correct. Synapse Analytics provides a SQL-based engine optimized for large-scale analytical queries and can directly query data in Data Lake Storage with PolyBase or CETAS.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Azure SQL Database
Why it's wrong here
Incorrect. Azure SQL Database is designed for transactional workloads (OLTP) and is not optimized for petabyte-scale analytical queries. It would require data loading and may not handle joins across large datasets efficiently.
- ✗
Azure Analysis Services
Why it's wrong here
Incorrect. Azure Analysis Services provides semantic modeling and in-memory caching for business intelligence, but it is not a primary data store for large CSV files and does not offer SQL querying of raw data.
- ✗
Azure HDInsight
Why it's wrong here
Incorrect. HDInsight is a managed Hadoop/Spark service that can process large datasets, but it typically requires more complex programming (e.g., Spark SQL) and is not as optimized for instant SQL querying as Synapse Analytics.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure Synapse Analytics with Azure SQL Database, assuming both can handle large analytical queries, but Azure SQL Database lacks the MPP architecture and external table support needed for petabyte-scale data lake queries.
Detailed technical explanation
How to think about this question
Under the hood, Azure Synapse separates compute from storage, allowing its SQL pool to scale out by adding more compute nodes that process data in parallel using a control node to distribute queries. The PolyBase technology in Synapse enables it to query external data in Azure Data Lake Storage Gen2 directly without moving it, using T-SQL statements that are automatically parallelized across nodes. In a real-world scenario, a retail chain with billions of daily transactions can use Synapse's result-set caching and materialized views to achieve consistent sub-second response times even on aggregated queries over petabytes.
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.
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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 — Azure Synapse Analytics (formerly SQL Data Warehouse) is the correct choice because it is a distributed query engine designed for petabyte-scale data warehousing. It uses Massively Parallel Processing (MPP) to distribute data across compute nodes, enabling complex SQL joins and aggregations on data stored in Azure Data Lake Storage Gen2 to return results in seconds via its SQL pool or serverless SQL endpoint.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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