Azure Synapse Serverless SQL Pool: Query Parquet and CSV Files in Data Lake Storage
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?
Quick Answer
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.
⚠ Common exam trap
Candidates often 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.
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 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.
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.
- ✗
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.
When this WOULD be correct
A question where the team needs consistent, high-performance T-SQL querying with predictable workloads and is willing to provision and manage dedicated compute resources, such as for large-scale data warehousing with complex queries.
- ✗
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.
When this WOULD be correct
A data science team needs to perform advanced analytics and machine learning on raw clickstream data using Apache Spark, with collaborative notebooks and automated cluster management. They prefer a serverless option that supports Python, Scala, and SQL.
- ✗
Azure HDInsight
Why it's wrong here
Azure HDInsight is a managed cluster service that requires provisioning and managing compute resources, not serverless.
When this WOULD be correct
A question where the team needs to run custom MapReduce jobs or use open-source frameworks like Spark, Hive, or HBase on a managed Hadoop cluster, and they are willing to manage dedicated compute resources.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The DP-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Azure Synapse Serverless SQL poolCorrect answer▾
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.
✗Azure Synapse Dedicated SQL poolWrong answer — click to see why▾
Why this is wrong here
Azure Synapse Dedicated SQL pool requires provisioning and managing dedicated compute resources, which contradicts the team's preference for a serverless option to avoid such overhead.
★ When this WOULD be the correct answer
A question where the team needs consistent, high-performance T-SQL querying with predictable workloads and is willing to provision and manage dedicated compute resources, such as for large-scale data warehousing with complex queries.
Why candidates choose this
Candidates may confuse Dedicated SQL pool with serverless options, or assume that all Synapse SQL pools are serverless, not realizing the key difference in resource management.
✗Azure DatabricksWrong answer — click to see why▾
Why this is wrong here
Azure Databricks does not support standard T-SQL queries; it uses Spark SQL or Python/Scala for transformations, not T-SQL. The question explicitly requires standard T-SQL, making Databricks incorrect.
★ When this WOULD be the correct answer
A data science team needs to perform advanced analytics and machine learning on raw clickstream data using Apache Spark, with collaborative notebooks and automated cluster management. They prefer a serverless option that supports Python, Scala, and SQL.
Why candidates choose this
Candidates may associate Azure Databricks with serverless data transformation and overlook the T-SQL requirement, assuming it supports standard SQL queries.
✗Azure HDInsightWrong answer — click to see why▾
Why this is wrong here
Azure HDInsight requires provisioning and managing a cluster (dedicated compute), which contradicts the team's preference for a serverless option. It also does not support standard T-SQL queries natively; it uses Spark, Hive, or other big data frameworks.
★ When this WOULD be the correct answer
A question where the team needs to run custom MapReduce jobs or use open-source frameworks like Spark, Hive, or HBase on a managed Hadoop cluster, and they are willing to manage dedicated compute resources.
Why candidates choose this
Candidates may associate HDInsight with big data processing on Azure and overlook the serverless and T-SQL requirements, thinking any Hadoop-based service can handle Parquet files.
Analysis generated from the official DP-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Serverless SQL pool
Serverless SQL pool is an on-demand, pay-per-query analytics service in Azure that lets you query data stored in data lakes without provisioning or managing any dedicated infrastructure.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
About these practice questions
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Same concept, more angles
2 more ways this is tested on DP-900
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.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.