DP-900 Describe an analytics workload on Azure Practice Question
A financial services company needs to run ad-hoc SQL queries on petabytes of data stored in Azure Data Lake Storage without provisioning a dedicated data warehouse. Which Azure service should they use?
⚠ Common exam trap
Many exam-takers confuse Azure Data Lake Storage (a storage service) with a query engine, or assume that a provisioned data warehouse like Azure SQL Database is required for any SQL workload, missing the serverless, on-demand nature of Synapse serverless SQL pool.
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 Analytics serverless SQL pool
Azure Synapse Analytics serverless SQL pool is the correct choice because it allows you to run ad-hoc SQL queries directly against data in Azure Data Lake Storage without provisioning any dedicated compute resources. It uses a pay-per-query model, automatically scaling compute to handle petabytes of data, making it ideal for intermittent, exploratory workloads.
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 serverless SQL pool
Why this is correct
Azure Synapse Analytics serverless SQL pool is a query service that runs T-SQL directly over files in Azure Data Lake Storage, using a distributed compute model that scales to petabyte-scale datasets without provisioning or managing dedicated infrastructure. It supports familiar SQL syntax for ad-hoc exploration, including OPENROWSET queries, and charges only for data scanned, making it ideal for ad-hoc SQL analytics on lake data.
- ✗
Azure Analysis Services
Why it's wrong here
Azure Analysis Services is an enterprise OLAP engine providing broadly cached, pre-aggregated tabular models and hierarchies for fast interactive reporting; it does not execute ad-hoc SQL against raw data lake files. You first need to build, partition, and process a cube or tabular model, and querying happens via DAX or MDX, not free-form T-SQL over storage. For a quick, on-the-fly SQL query over lake data, it adds heavy design-time overhead and is the wrong tool.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a fully managed, relational database service with persistent storage and a fixed-sized transactional log, built for OLTP workloads and row-oriented query patterns. To run ad-hoc queries on it, you must first provision a database, design schemas, and load data; it also lacks native integration for querying external files in Azure Data Lake Storage at petabyte scale using a distributed SQL engine. This provisioning and schema-first model is incompatible with the requirement for on-demand SQL queries across a data lake.
- ✗
Azure Data Lake Storage
Why it's wrong here
Azure Data Lake Storage is a highly scalable, hierarchical file system for storing structured and unstructured data, not a compute service; it provides no query processing engine and cannot execute a single SQL statement by itself. Any ad-hoc SQL query requires an external compute layer such as Azure Synapse serverless SQL, Databricks, or HDInsight to read the files. Choosing it as the tool to run SQL queries confuses storage with query processing.
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
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.
About these practice questions
Courseiva writes every DP-900 question from scratch — 820 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
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.