DP-900 Describe an analytics workload on Azure Practice Question
Your company is migrating an on-premises SQL Server data warehouse to Azure. The solution must support both historical analytics and real-time reporting. Which Azure service should you recommend as the primary data store?
⚠ Common exam trap
Many exam-takers confuse Azure SQL Database (an OLTP service) with a data warehouse solution, overlooking that Synapse Analytics is the dedicated Azure service for hybrid transactional/analytical processing (HTAP) and large-scale analytics workloads.
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
Azure Synapse Analytics is the correct choice because it is a cloud-native analytics service that unifies big data and data warehousing, supporting both historical analytics (via dedicated SQL pools for large-scale relational data warehousing) and real-time reporting (via serverless SQL pools or Apache Spark pools for streaming and interactive queries). It is designed to handle the migration of an on-premises SQL Server data warehouse while providing integrated capabilities for batch and real-time 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 Analysis Services
Why it's wrong here
Azure Analysis Services is a semantic modeling and OLAP engine that compresses data into an in-memory tabular model for fast, interactive BI queries. It does not store a durable, authoritative copy of data; it imports or DirectQueries from an existing relational source such as Azure SQL Database or SQL Server. Migrating a data warehouse directly into it would force you to redesign your dimensional schema into tabular/cube objects, and it lacks critical data warehouse features like MPP distribution, PolyBase over data lakes, and native ingestion pipelines.
- ✗
Azure Data Lake Storage Gen2
Why it's wrong here
Azure Data Lake Storage Gen2 is an ultra-scalable hierarchical file store built on Blob Storage that can hold petabytes of raw, semi-structured, and structured files at low cost. It provides no query engine or SQL execution itself; you must layer compute such as Azure Synapse Serverless, Databricks, or HDInsight on top to analyze the files. While it is an excellent staging area or data lake landing zone during migration, it does not offer managed T-SQL endpoints, index structures, or transactional workload management, so it cannot replace a SQL Server data warehouse without an additional engine.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a fully managed relational database service optimized for OLTP workloads, with rowstore indexes, fine-grained security, and high-frequency point lookups or small transactions. It can implement columnstore indexes, but it is a single-node SMP engine with limited scalability for massive scans compared to Azure Synapse's distributed MPP architecture. A large on-premises data warehouse with huge fact tables would require significant sharding or scaling work in Azure SQL Database and would not benefit from Synapse's optimized distribution, workload isolation, and integrated data orchestration.
- ✓
Azure Synapse Analytics
Why this is correct
Azure Synapse Analytics is the purpose-built cloud data warehouse service that uses a massively parallel processing (MPP) engine across multiple compute nodes, automatically distributing tables and using clustered columnstore indexes for high compression and scan performance. It provides full T-SQL support, PolyBase connectors to Azure Data Lake Storage Gen2 and other sources, and integrations with Azure Data Factory and Synapse Pipelines for end-to-end data movement. Synapse Link also enables real-time analytics on operational data, making it the closest technical equivalent to replacing an on-premises SQL Server data warehouse.
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
Big data
Big data refers to extremely large and complex datasets that traditional data processing tools cannot handle efficiently, requiring specialized technologies to store, process, and analyze them.
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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