DP-900 Describe an analytics workload on Azure Practice Question
A retail company needs to run complex SQL queries on petabytes of historical sales data stored in Parquet files in Azure Data Lake Storage Gen2. They want a solution that provides fast query performance without managing infrastructure, and they prefer a pay-per-query pricing model. Which Azure service should they use?
⚠ Common exam trap
It's easy for candidates to confuse Azure Synapse Analytics dedicated SQL pool (provisioned, always-on) with the serverless SQL pool (pay-per-query), or assume that Azure SQL Database can handle big data analytics on Parquet files, when it is designed for OLTP workloads and lacks native support for querying external data lakes without additional services like PolyBase.
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 correct because it allows querying petabytes of Parquet files in Azure Data Lake Storage Gen2 using T-SQL without provisioning any infrastructure, and it charges per terabyte of data processed (pay-per-query). This matches the requirements for fast query performance on historical sales data with a serverless, consumption-based pricing model.
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 Analytics dedicated SQL pool
Why it's wrong here
Azure Synapse Analytics dedicated SQL pool is a provisioned service where you allocate a fixed number of Data Warehouse Units (DWUs) and pay per hour for that compute capacity, even when no queries are running. It is designed for predictable, high-performance enterprise data warehousing, not for on-demand, pay-per-query exploration of petabyte-scale data lakes. This model requires upfront capacity planning and incurs ongoing costs regardless of actual query activity, making it unsuitable for the stated requirement.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a PaaS relational database engine optimized for Online Transaction Processing (OLTP) workloads with row-level storage and per-session concurrency limits. It lacks native query federation to Azure Data Lake Storage and cannot scale to petabyte-size datasets for analytical queries, as its maximum database size and query parallelism are far below what a distributed data lake query service provides. Pricing is based on provisioned DTUs or vCores, not the volume of data scanned per query, so it does not offer the pay-per-query model the company needs.
- ✓
Azure Synapse Serverless SQL pool
Why this is correct
Azure Synapse Serverless SQL pool is a distributed query engine that can directly read data from Azure Data Lake Storage using T-SQL, without provisioning any dedicated infrastructure. It charges only for the amount of data processed per query, making it a true pay-per-query service ideal for ad-hoc and interactive analysis of petabytes of data stored in open formats like Parquet or CSV. Because it is serverless, it automatically scales resources to handle large queries and requires no cluster management or idle time billing.
- ✗
Azure HDInsight with Hive
Why it's wrong here
Azure HDInsight with Hive is a managed Apache Hadoop cluster that requires you to provision and pay for a set of virtual machines continuously, even when no Hive jobs are running. Hive translates queries into MapReduce or Tez jobs with high latency, making it suboptimal for interactive petabyte-scale queries, and it is not a serverless service with consumption-based pricing. Additionally, Hive often requires data in a convenient format and may involve cluster maintenance, adding operational overhead that contradicts the goal of simple, on-demand querying.
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
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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.
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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.