The answer is Azure Synapse Serverless SQL pool. This service is the correct choice because it enables you to query data lake files—specifically Parquet and CSV files stored in Azure Data Lake Storage Gen2—using standard T-SQL without provisioning or managing any dedicated compute resources. It operates on a serverless, pay-per-query model, automatically scaling compute based on demand, which makes it ideal for ad-hoc transformations and aggregations on raw clickstream data. On the Microsoft Azure Data Fundamentals DP-900 exam, this question tests your understanding of the serverless versus dedicated compute options within Azure Synapse Analytics. A common trap is confusing this with Azure SQL Database or a dedicated SQL pool, both of which require provisioning resources. Remember the key differentiator: if the scenario emphasizes “no provisioning” and “standard T-SQL on data lake files,” think serverless. A helpful memory tip is “Serverless for storage, Dedicated for warehouse”—serverless queries files directly, while dedicated pools manage structured tables.
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 transform raw clickstream data stored as Parquet files in Azure Data Lake Storage Gen2. They want to use standard T-SQL queries to perform transformations and aggregations. The team prefers a serverless option to avoid provisioning and managing dedicated compute resources. Which Azure service should they use?
This serverless option enables querying data lake files with T-SQL on-demand, without provisioning compute resources, aligning with the team's requirements.
B
Azure Synapse Dedicated SQL pool
Why wrong: Dedicated SQL pool requires provisioning and managing a fixed cluster, which contradicts the serverless preference and adds administrative overhead.
C
Azure Databricks
Why wrong: Azure Databricks is a managed Spark-based analytics platform that requires a cluster to run, not a serverless SQL option.
D
Azure HDInsight
Why wrong: Azure HDInsight is a managed cluster service that requires provisioning and managing compute resources, not serverless.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Synapse Serverless SQL pool
Azure Synapse Serverless SQL pool is correct because it allows querying Parquet files in Azure Data Lake Storage Gen2 using standard T-SQL without provisioning any dedicated compute resources. It automatically scales compute based on query demand, making it ideal for ad-hoc transformations and aggregations on raw data with a serverless, pay-per-query model.
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 Serverless SQL pool
Why this is correct
This serverless option enables querying data lake files with T-SQL on-demand, without provisioning compute resources, aligning with the team's requirements.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Synapse Dedicated SQL pool
Why it's wrong here
Dedicated SQL pool requires provisioning and managing a fixed cluster, which contradicts the serverless preference and adds administrative overhead.
✗
Azure Databricks
Why it's wrong here
Azure Databricks is a managed Spark-based analytics platform that requires a cluster to run, not a serverless SQL option.
✗
Azure HDInsight
Why it's wrong here
Azure HDInsight is a managed cluster service that requires provisioning and managing compute resources, not serverless.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse 'serverless' with any cloud service that can run SQL, but only Azure Synapse Serverless SQL pool provides T-SQL support without provisioning compute, while Databricks and HDInsight require cluster management and use non-T-SQL query languages.
Detailed technical explanation
How to think about this question
Azure Synapse Serverless SQL pool uses a distributed query engine that reads data directly from Azure Data Lake Storage Gen2 via the T-SQL endpoint, leveraging the OPENROWSET function with BULK operations to parse Parquet files. Under the hood, it dynamically allocates compute nodes per query, enabling efficient columnar scans and predicate pushdown without any persistent storage or cluster management. In real-world scenarios, this is ideal for data exploration and lightweight ETL where cost efficiency and simplicity are prioritized over consistent performance.
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 Serverless SQL pool — Azure Synapse Serverless SQL pool is correct because it allows querying Parquet files in Azure Data Lake Storage Gen2 using standard T-SQL without provisioning any dedicated compute resources. It automatically scales compute based on query demand, making it ideal for ad-hoc transformations and aggregations on raw data with a serverless, pay-per-query model.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A retail company collects sales data from multiple stores. Data is ingested into Azure Data Lake Storage Gen2 as CSV files. The data team needs to run ad-hoc SQL queries on this data without moving it, and they want to pay only for the amount of data processed. They also need to integrate with Power BI for visualization. Which Azure service should they use?
medium
A.Azure Synapse Analytics dedicated SQL pool
B.Azure SQL Database
C.Azure Data Lake Analytics
✓ D.Azure Synapse Serverless SQL pool
Why D: Azure Synapse Serverless SQL pool (option D) is correct because it allows querying data directly from Azure Data Lake Storage Gen2 using T-SQL without moving the data, and it uses a pay-per-query model where you are billed only for the amount of data processed. It also integrates seamlessly with Power BI for visualization, making it ideal for ad-hoc SQL queries on CSV files.
Variation 2. A retail company needs to run complex SQL queries on petabytes of historical sales data stored in Parquet files in Azure Data Lake Storage Gen2. They want a solution that provides fast query performance without managing infrastructure, and they prefer a pay-per-query pricing model. Which Azure service should they use?
medium
A.Azure Synapse Analytics dedicated SQL pool
B.Azure SQL Database
✓ C.Azure Synapse Serverless SQL pool
D.Azure HDInsight with Hive
Why C: Azure Synapse Serverless SQL pool is correct because it allows querying petabytes of Parquet files in Azure Data Lake Storage Gen2 using T-SQL without provisioning any infrastructure, and it charges per terabyte of data processed (pay-per-query). This matches the requirements for fast query performance on historical sales data with a serverless, consumption-based pricing model.
Last reviewed: Jun 11, 2026
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