This DP-900 practice question tests your understanding of describe an analytics workload on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which THREE components are essential for building a modern data warehouse architecture on Azure?
Azure Analysis Services
Why wrong: Optional semantic modeling layer.
Azure Cosmos DB
Why wrong: NoSQL database, not part of data warehouse.
Azure Databricks
For data transformation and processing.
Azure Data Lake Storage
Central storage for raw and curated data.
Azure Synapse Analytics
Serving layer for querying and analytics.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Azure Databricks
Azure Databricks is correct because it provides an Apache Spark-based analytics platform that enables data engineering, data science, and machine learning on large-scale data. In a modern data warehouse architecture, Azure Databricks is used for data transformation, preparation, and advanced analytics, often feeding into or complementing Azure Synapse Analytics for structured querying and reporting.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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
Optional semantic modeling layer.
Azure Cosmos DB
Why it's wrong here
NoSQL database, not part of data warehouse.
Azure Databricks
Why this is correct
For data transformation and processing.
Related concept
Read the scenario before looking for a memorised answer.
Azure Data Lake Storage
Why this is correct
Central storage for raw and curated data.
Related concept
Read the scenario before looking for a memorised answer.
Azure Synapse Analytics
Why this is correct
Serving layer for querying and analytics.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates may confuse Azure Analysis Services as a core component of the data warehouse architecture, when it is actually a downstream BI tool, or mistakenly think Azure Cosmos DB is suitable for analytical workloads, whereas it is optimized for transactional and real-time NoSQL scenarios.
Detailed technical explanation
A modern data warehouse on Azure typically follows a 'lakehouse' pattern, where Azure Data Lake Storage serves as the single source of truth for raw and curated data, Azure Databricks handles ETL/ELT and advanced analytics using Spark, and Azure Synapse Analytics provides a unified experience for data warehousing, big data analytics, and data integration. The synergy between these services allows for separation of compute and storage, enabling cost-effective scaling and diverse workload support.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
The correct answer is: Azure Databricks — Azure Databricks is correct because it provides an Apache Spark-based analytics platform that enables data engineering, data science, and machine learning on large-scale data. In a modern data warehouse architecture, Azure Databricks is used for data transformation, preparation, and advanced analytics, often feeding into or complementing Azure Synapse Analytics for structured querying and reporting.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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