DP-900 Describe an analytics workload on Azure Practice Question
A company needs to run complex SQL queries on petabytes of data stored in Azure Data Lake Storage Gen2. They want to pay only for the queries they run and do not want to manage any infrastructure. Which Azure service should they use?
⚠ Common exam trap
It's easy for candidates to confuse 'serverless' with 'dedicated' SQL pools in Azure Synapse, assuming both can query Data Lake Storage Gen2, but only the serverless pool offers a pay-per-query model without infrastructure management.
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 the correct choice because it enables running complex SQL queries directly against data in Azure Data Lake Storage Gen2 without provisioning any infrastructure. It uses a pay-per-query billing model, charging only for the amount of data processed, which aligns with the requirement to pay only for queries run and avoid infrastructure management.
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
Azure Synapse Serverless SQL pool is a serverless query engine that reads T-SQL queries directly against files in Azure Data Lake Storage, eliminating any provisioning or cluster management. It charges only for the amount of data processed by each query, making it a true pay-per-query model that scales automatically to handle petabytes. This matches both the 'complex SQL queries' and the 'no management, pay only for queries' constraints exactly.
- ✗
Azure Synapse dedicated SQL pool
Why it's wrong here
Azure Synapse dedicated SQL pool is a provisioned, fixed-capacity data warehouse that requires you to choose a DWU performance level and scale compute separately from storage. You pay for the reserved compute per second, even when no queries are running, so costs continue regardless of usage. It also demands ongoing oversight of distribution, indexing, and partitioning, which fails the 'no management' and 'pay per query' requirements.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a managed relational database meant for transactional (OLTP) applications, where data is stored in pages on dedicated database files, not in an external data lake. It does not provide a serverless pay-per-query model for ad-hoc analytics over petabytes of unstructured or structured files; instead, it uses provisioned vCores or DTUs and is sized for specific database workloads. Attempting to query petabytes in a data lake with Azure SQL Database would require importing the data first, which is neither efficient nor supported for that scale.
- ✗
Azure HDInsight
Why it's wrong here
Azure HDInsight is a managed service for running open-source clusters like Apache Spark, Hadoop, and Hive, but you must explicitly create and configure the cluster with a chosen node count and VM sizes. You are responsible for cluster lifecycle, monitoring, and scaling, and billing is based on cluster uptime per node-hour, not per query processed. This contradicts the pay-per-query and zero-management conditions, as the cluster is on continuous compute even without active SQL queries.
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 Lake Storage Gen2
Data Lake Storage Gen2 is a cloud-based storage service that combines a scalable data lake with enterprise-grade file system capabilities for big data analytics.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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