Question 560 of 820
DP-900 Describe an analytics workload on Azure Practice Question
A data analyst needs to run interactive SQL queries against petabytes of sales data stored in Parquet format in Azure Data Lake Storage Gen2. The analyst wants the fastest query performance for ad-hoc exploration without provisioning or managing any infrastructure. Which Azure service should they use?
⚠ Common exam trap
Many exam-takers confuse Azure Synapse Serverless SQL pool with Azure SQL Database, assuming both are for querying data, but Azure SQL Database cannot directly query external files in Data Lake Storage without additional tools like PolyBase.
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
✓
B. Azure Synapse Serverless SQL pool
Azure Synapse Serverless SQL pool is correct because it enables running interactive T-SQL queries directly against Parquet files in Azure Data Lake Storage Gen2 without provisioning any infrastructure. It uses a pay-per-query model and leverages a distributed query engine to deliver fast performance on petabytes of data, making it ideal for ad-hoc exploration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A. Azure SQL Database
Why it's wrong here
Azure SQL Database is a relational database service optimized for OLTP workloads. It does not natively query external files in a data lake without additional tools like PolyBase, and it is not designed for petabyte-scale analytics on Parquet files.
- ✓
B. Azure Synapse Serverless SQL pool
Why this is correct
Serverless SQL pool in Azure Synapse Analytics provides a distributed query engine that can query data directly in Azure Data Lake Storage (including Parquet) using T-SQL. It is serverless, scales automatically, and charges per query, making it ideal for interactive ad-hoc analytics.
- ✗
C. Azure HDInsight
Why it's wrong here
Azure HDInsight is a managed Hadoop/Spark service, but it still requires you to provision, configure, and scale clusters, and you pay for the cluster's uptime rather than per-query execution. While you can run interactive Hive or Spark queries against Parquet files in Azure Data Lake Storage, the cluster lifecycle management and fixed capacity make it fundamentally different from a serverless SQL engine. For an ad-hoc interactive querying task that demands no infrastructure management, HDInsight is the wrong choice because it places cluster administration responsibility on the analyst.
- ✗
D. Azure Data Factory
Why it's wrong here
Azure Data Factory is a cloud-based ETL and data orchestration service, not a query engine. It enables you to build pipelines that copy, move, and transform data using activities such as Copy Data or Mapping Data Flow, but it does not expose an interactive SQL endpoint for querying data directly in files. You cannot run arbitrary ad-hoc T-SQL queries against Parquet or other data lake formats using Data Factory; it would be used to load or transform data before querying it with a tool like Synapse. Therefore, it fails the requirement to run interactive SQL queries directly on the data.
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 |
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Last reviewed: Jun 11, 2026
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