DP-900 Describe an analytics workload on Azure Practice Question
Which THREE components are typically part of a modern data warehouse architecture on Azure? (Choose three.)
⚠ Common exam trap
Many exam-takers 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.
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 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.
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
Azure Data Factory is the cloud-backed ETL/ETL orchestration engine that connects to 90+ on-premises and cloud sources, moves data into Data Lake Storage Gen2, and triggers transformation activities on Azure Databricks or HDInsight. Its scheduled or event-driven pipelines are what make repeatable batch data processing possible. Without it, the modern data warehouse cannot automate the ingestion and transformation steps that feed the serving layer.
- ✓
Azure Synapse Analytics
Why this is correct
Azure Synapse Analytics is the converging warehouse and big data platform where data from the lake is loaded into dedicated SQL pools for relational storage and querying, or queried in place using serverless SQL. It provides the serving layer that business intelligence tools like Power BI connect to for dashboards and reports. Its distributed query engine and T-SQL compatibility make it the analytics cornerstone of the solution.
- ✗
Azure Stream Analytics
Why it's wrong here
Azure Stream Analytics is a real-time event-processing service that applies SQL-like queries to data streaming from IoT devices, clickstreams, or telemetry, with low latency and sliding windows. A modern data warehouse typically relies on batch or micro-batch ingestion from Azure Data Factory or Spark, not on continuous streaming. It is positioned for separate streaming analytics workloads, not as a required component of a relational warehouse.
- ✓
Azure Data Lake Storage Gen2
Why this is correct
Azure Data Lake Storage Gen2 is a blob storage substrate enhanced with a hierarchical namespace, offering POSIX-style ACLs, directory-level atomic rename, and indefinite scalability for petabytes of data. It serves as the landing zone for raw files (JSON, CSV, Avro) and as the store for curated Parquet tables that are queried by Synapse serverless SQL. Access policy and data partitioning in ADLS Gen2 make it the file-based backbone of the warehouse.
- ✗
Azure Cosmos DB
Why it's wrong here
Azure Cosmos DB is a globally distributed NoSQL database focused on millisecond-latency transaction processing across models like document, key-value, and graph, with a schema-agnostic API surface. The modern data warehouse analytics pipeline concentrates on structured fact and dimension tables and columnar file formats, not on operational record stores. Its design optimizes for online reads/writes, not for large columnar scans or BI reporting, so it falls outside the typical warehouse component set.
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Data Roles and Core Concepts
Key term
Azure Data Factory
Azure Data Factory is a cloud-based data integration service that lets you create, schedule, and orchestrate data pipelines to move and transform data from various sources to destinations.
Key term
Azure Databricks
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure that lets data teams prepare data, run machine learning models, and build data pipelines using a single workspace.
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