The answer is Azure Data Factory, Azure Data Lake Storage Gen2, and Azure Synapse Analytics. These three components form the core of a modern data warehouse architecture on Azure because they address the full data lifecycle: Data Factory handles cloud-based ETL orchestration and automated data movement, Data Lake Storage Gen2 provides a scalable, hierarchical data lake for raw and transformed data, and Synapse Analytics serves as the unified analytics platform for querying and reporting. On the DP-900 exam, this question tests your understanding of how these services work together in a medallion architecture (bronze, silver, gold layers), often appearing as a multi-select scenario where you must distinguish between storage, compute, and integration services. A common trap is confusing Azure SQL Database or Azure Databricks as mandatory components—while they can be used, they are not the three core pillars. Memory tip: think of the “pipeline, lake, and warehouse” trio—Data Factory moves it, Data Lake stores it, Synapse analyzes it.
DP-900 Describe an analytics workload on Azure Practice Question
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 typically part of a modern data warehouse architecture on Azure? (Choose three.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Data Factory
Azure Data Factory is correct because it serves as the cloud-based ETL (Extract, Transform, Load) service that orchestrates and automates data movement and transformation across various sources and destinations. In a modern data warehouse architecture, Data Factory is used to ingest raw data from on-premises or cloud sources, transform it using mapping data flows or external compute (e.g., Azure Databricks), and load it into the data warehouse or data lake for analytics. It provides a code-free visual interface or SDK-based control for scheduling and monitoring pipelines, making it essential for the ingestion and preparation layer.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 Data Factory
Why this is correct
Orchestrates data ingestion and transformation pipelines.
Related concept
Read the scenario before looking for a memorised answer.
✓
Azure Synapse Analytics
Why this is correct
Provides the data warehousing and analytics serving layer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Stream Analytics
Why it's wrong here
Real-time stream processing, not a core component of batch data warehouse.
✓
Azure Data Lake Storage Gen2
Why this is correct
Serves as the data lake for raw and transformed data.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Cosmos DB
Why it's wrong here
NoSQL database for operational workloads, not typical for data warehousing.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure Stream Analytics (a real-time processing service) with a batch data warehouse component, or mistakenly think Azure Cosmos DB can serve as an analytical data store due to its multi-model capabilities, but it lacks the columnar storage and MPP architecture required for modern data warehousing.
Detailed technical explanation
How to think about this question
Under the hood, Azure Synapse Analytics (formerly SQL Data Warehouse) uses a distributed query engine with control and compute nodes, leveraging PolyBase to query data directly from Azure Data Lake Storage Gen2 without moving it. Data Lake Storage Gen2 combines a hierarchical namespace with blob storage, enabling POSIX-compliant access and fine-grained ACLs, which is critical for managing large-scale analytical datasets. In a real-world scenario, a retail company might use Data Factory to ingest daily sales data into Data Lake Storage Gen2, then use Synapse SQL pools to run complex aggregations across billions of rows, all while maintaining separation of storage and compute for cost efficiency.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure Data Factory — Azure Data Factory is correct because it serves as the cloud-based ETL (Extract, Transform, Load) service that orchestrates and automates data movement and transformation across various sources and destinations. In a modern data warehouse architecture, Data Factory is used to ingest raw data from on-premises or cloud sources, transform it using mapping data flows or external compute (e.g., Azure Databricks), and load it into the data warehouse or data lake for analytics. It provides a code-free visual interface or SDK-based control for scheduling and monitoring pipelines, making it essential for the ingestion and preparation layer.
What should I do if I get this DP-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.