DP-900 Describe an analytics workload on Azure Practice Question
A data analyst needs to run ad-hoc SQL queries on petabytes of log data stored as Parquet files in Azure Data Lake Storage Gen2. The queries join multiple tables and require high concurrency from multiple analysts. The solution should minimize cost by only paying for queries executed. Which Azure service should they use?
⚠ Common exam trap
Test-takers frequently confuse 'serverless' with 'Dedicated SQL pool' or choose Spark-based services like Databricks or HDInsight, failing to recognize that the key requirement is pay-per-query billing for ad-hoc SQL on data lake storage, which only Serverless SQL pool provides.
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 running ad-hoc T-SQL queries directly on Parquet files in Azure Data Lake Storage Gen2 without provisioning any infrastructure. It uses a pay-per-query billing model, making it cost-effective for sporadic, high-concurrency workloads where you only want to pay for the compute resources consumed during query execution.
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 files in Azure Data Lake Storage without provisioning any compute infrastructure. You are billed only for the amount of data processed per query (per TB scanned), and the service automatically scales to handle concurrent ad-hoc workloads, making it the most cost-effective and operationally simple option for sporadic SQL queries on petabytes of data.
- ✗
Azure Synapse Dedicated SQL pool
Why it's wrong here
Azure Synapse Dedicated SQL pool requires you to provision and pay for a fixed set of compute nodes (DWU/capacity) that remain running even when no queries are executed. For sporadic ad-hoc SQL workloads, this means you incur continuous infrastructure costs and must manually scale up or down to match demand, which directly contradicts the requirement to minimize cost for occasional queries.
- ✗
Azure HDInsight with Spark
Why it's wrong here
Azure HDInsight with Spark is a managed cluster service designed for batch and streaming big-data processing using Apache Spark, not for lightweight ad-hoc SQL. Running it requires you to keep a cluster active (or start/stop it), incurring cluster-hour charges and management overhead, and you must write Spark SQL/DataFrame code rather than pure T-SQL—so it lacks the instant, pay-per-query SQL experience of a serverless pool.
- ✗
Azure Databricks
Why it's wrong here
Azure Databricks is a Spark-based analytics platform built for data engineering, machine learning, and collaborative notebooks, requiring an active cluster to execute queries. Its pricing is based on cluster compute (DBU hours) rather than per-query data scanned, so sporadic ad-hoc SQL workloads would still incur cluster startup time and idle compute costs, making it both expensive and operationally heavier than a serverless SQL endpoint.
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
Serverless SQL pool
Serverless SQL pool is an on-demand, pay-per-query analytics service in Azure that lets you query data stored in data lakes without provisioning or managing any dedicated infrastructure.
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.