DP-900 Describe an analytics workload on Azure Practice Question
A data analyst needs to query large datasets stored as Parquet files in Azure Data Lake Storage Gen2. The queries are ad-hoc and infrequent. The analyst wants to run SQL queries directly on the data without creating any storage or compute infrastructure, and only pay for the amount of data processed. They also need to create T-SQL views to simplify queries for Power BI reports. Which Azure service should they use?
⚠ Common exam trap
The trap is that candidates may confuse 'serverless' with 'no infrastructure' and incorrectly choose Azure SQL Database, HDInsight with Spark, or Azure Databricks, failing to recognize that Synapse Serverless SQL pool specifically provides a T-SQL interface over data lake storage with a pay-per-query model, while the other options require persistent compute resources and are not optimized for ad-hoc, infrequent queries on data lake files.
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 (Option B) is the correct choice because it allows querying data directly from Azure Data Lake Storage Gen2 using T-SQL without provisioning any compute or storage infrastructure. It supports ad-hoc, infrequent queries with a pay-per-query pricing model based on the amount of data processed, and it enables the creation of T-SQL views that can be used directly by Power BI for reporting. In contrast, Option A (Azure SQL Database) requires provisioning a database with compute resources, incurring continuous costs. Option C (Azure HDInsight with Spark) requires a persistent cluster and is not optimized for infrequent ad-hoc queries. Option D (Azure Databricks) also requires compute resources and is not a pay-per-query service.
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 SQL Database
Why it's wrong here
Azure SQL Database is a provisioned, platform-as-a-service relational database for transactional (OLTP) workloads, not an ad-hoc query engine over external files. While it can reference external data in ADLS Gen2 via PolyBase external tables, this requires schema definition, data type mapping, and a provisioned DTU/vCore tier that incurs ongoing cost even when idle. Its architecture is optimized for row-store indexes and T-SQL connections from applications, not for serverless, pay-per-query exploration of Parquet files at scale.
- ✓
Azure Synapse Serverless SQL pool
Why this is correct
Azure Synapse Serverless SQL pool is a serverless query service that lets you run T-SQL queries directly against files in ADLS Gen2, including Parquet, using OPENROWSET or external tables. There is no infrastructure to provision; compute starts on demand and you pay only for the amount of data processed by each query, making it ideal for ad-hoc, read-only exploration of large datasets. It also supports logical views and metadata to simplify reporting, while scaling automatically with the size of the data scanned.
- ✗
Azure HDInsight with Spark
Why it's wrong here
Azure HDInsight with Spark is a managed cluster service that requires you to provision and run an always-on cluster, incurring compute charges 24/7 even when no queries execute. Querying Parquet files with Spark also requires authoring Spark SQL or DataFrame code, compiling and submitting jobs, and managing cluster lifecycles—unlike the zero-ops, pure T-SQL serverless experience of an on-demand SQL pool. Even with autoscaling, there is still a persistent master node and minimum worker capacity, so it does not offer a true pay-per-query model.
- ✗
Azure Databricks
Why it's wrong here
Azure Databricks is a unified analytics platform centered on Apache Spark, where interactive querying occurs through notebooks or SQL endpoints backed by one or more active clusters. Although Databricks can auto-terminate clusters, you must still create and start a cluster (or a SQL warehouse) before running a query, and you are billed for VM uptime, not for data scanned by each individual command. Its primary interfaces are Spark SQL, Python, Scala, and R—not the serverless T-SQL environment of Azure Synapse Serverless SQL pool—and it is therefore not the best fit for simple ad-hoc T-SQL queries over Parquet.
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
Azure SQL Database
Azure SQL Database is a fully managed relational database-as-a-service (DBaaS) in Microsoft Azure, based on the SQL Server engine, that handles scaling, backups, patching, and high availability automatically.
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
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