DP-900 Describe an analytics workload on Azure Practice Question
A financial services company stores transaction data in Azure Data Lake Storage Gen2 as Parquet files, partitioned by date. The data volume is 5 TB per day. The analytics team runs ad-hoc SQL queries to detect fraudulent patterns. Queries are highly selective (filtering on AccountID and date range). The team also needs to create external tables and views for use in Power BI. They want to pay only for the data processed by each query and avoid provisioning any compute resources. Which Azure service should they use?
⚠ Common exam trap
Many exam-takers confuse Azure Synapse Serverless SQL pool with Azure Synapse Dedicated SQL pool (which requires provisioning compute) or assume that any Spark-based service (like Databricks or HDInsight) is serverless, but only the serverless SQL pool offers true pay-per-query without compute provisioning.
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 allows querying data directly from Azure Data Lake Storage Gen2 using T-SQL without provisioning any compute resources. It charges per terabyte of data processed, aligning with the requirement to pay only for data scanned by each query. It also supports creating external tables and views for Power BI, making it ideal for ad-hoc, selective queries on partitioned Parquet files.
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 the correct choice because it lets you run T-SQL queries directly against transaction files in Azure Data Lake Storage Gen2 without provisioning any dedicated compute. The service spins up compute on demand, charges only for the amount of data scanned per query, and supports creating external tables and views that expose a relational layer for tools like Power BI, making it a genuine pay-per-query analytics engine.
- ✗
Azure Databricks with interactive clusters
Why it's wrong here
Azure Databricks with interactive clusters is wrong because it requires a running Spark cluster: even if auto-termination is enabled, the cluster is provisioned and billed by DBU (data processing unit) per minute or hour, not per query. You must attach a notebook or job to the cluster, and billing continues while the cluster is active, so it does not match the pay-per-query model described in the question.
- ✗
Azure Stream Analytics
Why it's wrong here
Azure Stream Analytics is wrong for this scenario because it is a real-time stream processing service that runs continuous, windowed queries over data arriving from Event Hubs, IoT Hub, or Blob Storage, rather than ad-hoc SQL queries against historical transaction files. It bills per streaming unit and does not provide an interactive SQL endpoint; its job is to process live streaming events, not to wait for a one-off query from a BI tool.
- ✗
Azure HDInsight with Spark
Why it's wrong here
Azure HDInsight with Spark is wrong because it requires you to provision and manage a persistent cluster of virtual machines before running any queries, and you are billed for those VMs as long as the cluster is allocated, regardless of whether Spark jobs are running. It is not a serverless offering, so it lacks the pay-per-query pricing and zero-management model of the correct answer; the cluster also needs to be created, scaled, and eventually deleted to stop costs.
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
Power BI
Power BI is a Microsoft business analytics tool that transforms raw data into interactive visual reports and dashboards for informed decision-making.
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.
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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.