DP-900 Describe an analytics workload on Azure Practice Question
A data analyst needs to run complex SQL queries against petabytes of historical sales data stored in Azure Data Lake Storage Gen2. The solution must be serverless with pay-per-query pricing. Which Azure service should they use?
⚠ Common exam trap
A common mix-up: candidates confuse Azure Synapse Analytics serverless SQL pool with Azure SQL Database or HDInsight, mistakenly thinking that any SQL-capable service can handle petabyte-scale serverless queries, while the key differentiator is the direct, pay-per-query access to Data Lake Storage without provisioning compute.
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 provides a serverless, pay-per-query engine that can directly query petabytes of data stored in Azure Data Lake Storage Gen2 using standard T-SQL. It eliminates infrastructure management and charges only for the data processed by each query, making it ideal for ad-hoc, complex SQL workloads on massive historical datasets.
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 the correct choice because it allows you to run T-SQL queries directly against data stored in Azure Data Lake Storage using a serverless, on-demand engine. You pay per query executed (per TB of data scanned) with no minimum compute or infrastructure to provision, making it ideal for ad-hoc, complex SQL analytics over petabyte-scale data files. Unlike provisioned SQL, it automatically scales and suspends, and it uses standard T-SQL, so the analyst can query with familiar syntax.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a fully managed, provisioned relational database service designed for line-of-business OLTP workloads with fixed compute capacity (DTUs or vCores), not for serverless on-demand SQL over data lake files. To query data in Azure Data Lake Storage, you would first have to load and store it in the database, incurring storage and compute costs, and the engine cannot directly reference external files as tables without additional tooling like PolyBase or External Tables, which are not its core competency. Therefore, it does not match the scenario's requirement for serverless, pay-per-query SQL over the data lake.
- ✗
Azure HDInsight with Spark
Why it's wrong here
Azure HDInsight with Spark requires you to provision and maintain a persistent cluster with a selected VM and node count, incurring hourly billing even when no jobs are running, so it is not a serverless, pay-per-query service. Additionally, the primary interface is Spark APIs (Scala, PySpark, or Spark SQL) submitted as jobs, and while you can run SQL queries, it involves cluster management and does not offer the same on-demand scalability as a serverless SQL pool. This makes it architecturally different and operationally heavier for answering interactive, ad-hoc SQL queries.
- ✗
Azure Analysis Services
Why it's wrong here
Azure Analysis Services is a platform for creating tabular data models and semantic layers, where data is cached in memory and accessed via MDX or DAX queries, not by running raw T-SQL against files in Azure Data Lake Storage. It requires building and deploying a model that pre-processes and aggregates data, so it is not a direct SQL query engine for the underlying data lake and would add significant design overhead. Consequently, it serves business intelligence reporting rather than the analyst's need for ad-hoc complex SQL directly on the data.
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 |
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Data Roles and Core Concepts
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.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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