DP-900 Describe an analytics workload on Azure Practice Question
A company needs to build a centralized analytics platform that can query both structured data in a relational data warehouse and unstructured data in a data lake using a single SQL-based interface. They want to minimize data movement and use a serverless, on-demand compute model for ad-hoc queries. Which Azure service should they use?
⚠ Common exam trap
Test-takers frequently confuse Azure Synapse Serverless SQL pool with Azure SQL Database or HDInsight, mistakenly thinking a traditional relational database or a managed cluster is needed for querying unstructured data, when the serverless SQL pool is specifically designed for this hybrid, on-demand scenario.
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 provides a SQL-based interface to query both structured data in a relational data warehouse and unstructured data in a data lake (e.g., Parquet, CSV, JSON) without moving data. It uses a serverless, on-demand compute model that charges per query, making it ideal for ad-hoc analytics with minimal data movement.
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 PaaS relational database that runs on provisioned compute and storage, not a serverless pay-per-query engine. Querying data in the lake requires moving/importing it into SQL tables first; SQL Database lacks the OPENROWSET and external-table capabilities that allow in-place querying of ADLS Gen2 or Parquet files. This makes it wrong for a centralized analytics platform that needs to query the lake without data movement.
- ✓
B. Azure Synapse Serverless SQL pool
Why this is correct
Azure Synapse Serverless SQL pool is a serverless, on-demand T-SQL query engine built for directly reading data from Azure Data Lake Storage (ADLS Gen2) and Blob Storage. It uses OPENROWSET with AUTO_TYPE detection to query Parquet, CSV, Delta, and JSON files in place, with no data movement and no provisioning — you are billed only for bytes scanned. Its ability to create external tables and metadata over lake files makes it the right fit for a centralized analytics platform that must query the lake with standard SQL.
- ✗
C. Azure HDInsight
Why it's wrong here
Azure HDInsight is a managed Hadoop ecosystem requiring you to deploy and pay for a persistent cluster running Spark, Hive, or HBase. It is designed for batch processing and ETL workloads, not for a lightweight serverless SQL query that auto-scales to zero. Using it for on-demand lake queries would force you to manage cluster lifecycle, monitor workloads, and pay for uptime even when idle — all unnecessary for ad-hoc T-SQL queries.
- ✗
D. Azure Analysis Services
Why it's wrong here
Azure Analysis Services is an OLAP/semantic-model engine that loads and caches data into in-memory tabular models, exposed via DAX and MDX, not T-SQL. It cannot directly execute T-SQL queries against raw data-lake files; instead, you must design a model, refresh from data sources, and then query that model. This makes it an option for pre-aggregated dashboarding, not for on-demand 'query any file' experiences over a lake.
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
Data warehouse
A data warehouse is a central repository that stores large amounts of structured data from multiple sources, optimized for querying and analysis rather than day-to-day transactions.
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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