DP-900 Describe an analytics workload on Azure Practice Question
An organization needs to run complex queries on petabytes of data stored in Azure Data Lake Storage. They want to use serverless compute to avoid managing infrastructure. Which Azure service should they use?
⚠ Common exam trap
Test-takers frequently confuse Azure Synapse Serverless SQL pool with Azure SQL Database or HDInsight, mistakenly thinking that any SQL-based service can handle serverless data lake queries, but only Synapse Serverless SQL pool provides true serverless compute with direct, on-demand querying of external data in Azure Data Lake Storage.
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 provides serverless compute that can run complex T-SQL queries directly against data stored in Azure Data Lake Storage without requiring any infrastructure management. It uses a pay-per-query billing model and can scale automatically to handle petabytes of data, making it ideal for ad-hoc analytics on large-scale data lakes.
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 Analysis Services
Why it's wrong here
Azure Analysis Services is a PaaS OLAP engine used to host tabular models that are typically refreshed in memory or via DirectQuery to relational sources. It does not provide a serverless T-SQL endpoint for querying raw files in a data lake, nor does it perform distributed scans of petabytes of unstructured data. In this scenario, its in-memory/cube approach is the wrong fit because the data remains in the lake and is queried directly.
- ✓
Azure Synapse Serverless SQL pool
Why this is correct
Azure Synapse Serverless SQL pool is the correct choice because it lets you run complex T-SQL queries directly over data stored in Azure Data Lake Storage using built-in OPENROWSET options. It provisions compute automatically and scales transparently based on query needs, so you can query petabytes without managing cluster infrastructure. Because there is no dedicated compute to provision, you are billed only for the data processed, making it ideal for this petabyte-scale, lake-based workload.
- ✗
Azure HDInsight
Why it's wrong here
Azure HDInsight is a managed open-source analytics service that requires you to create and provision a cluster before you can run Spark, Hive, or Hadoop jobs, and you must actively manage node scaling, cluster lifecycles, and software updates. This operational overhead is unnecessary when the goal is simply to run ad-hoc complex queries against a data lake, where a serverless engine can scale instantly without cluster administration. The need for provisioning and continuous cluster management makes HDInsight a heavier, less direct option for this query scenario.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a relational database engine optimized for OLTP workloads, with database-level storage capacity limits and a schema-first design that requires data to be loaded before querying. Directly querying petabytes of semi-structured files in a data lake is outside its intended scope, and importing that data into tables would be impractical and costly. Thus, although it can run complex SQL, SQL Database is not built for petabyte-scale data lake analytics in this serverless, file-querying scenario.
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
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Data Roles and Core Concepts
Key term
Data lake
A data lake is a centralized storage repository that holds vast amounts of raw data in its native format until it is needed for analysis.
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
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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.