DP-900 Describe an analytics workload on Azure Practice Question
Which THREE components are part of an end-to-end analytics solution on Azure?
⚠ Common exam trap
Test-takers frequently confuse supporting services (like identity management or generic compute) with the core analytics pipeline components, leading them to select Azure Virtual Machines or Microsoft Entra ID as part of the end-to-end solution when they are not directly involved in the data flow.
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
✓
Data ingestion (Azure Data Factory)
Option A (Data ingestion via Azure Data Factory) is correct because an end-to-end analytics solution must first bring data from disparate sources into Azure, and Azure Data Factory is the cloud ETL/orchestration service that performs this ingestion and pipeline movement. Option D (Data storage via Azure Data Lake Storage) is correct because ingested data must land in a scalable, durable repository, and ADLS Gen2 provides hierarchical namespace storage optimized for big-data analytics workloads. Option C (Data visualization via Power BI) is correct because the final stage of an end-to-end analytics solution is delivering insights to users, which Power BI accomplishes through interactive reports and dashboards. Option B (Azure Virtual Machines) is not part of the core analytics pipeline; VMs are generic IaaS compute and are not a required analytics component. Option E (Microsoft Entra ID) is an identity and access management service, not an analytics pipeline stage, so it does not belong among the three components.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Data ingestion (Azure Data Factory)
Why this is correct
Data ingestion is the initial phase in an analytics solution, and Azure Data Factory is the primary orchestration service that creates pipelines to extract data from on-premises and cloud sources, transform it, and load it into storage. It is a core component because without ingesting data, none of the downstream analytics processes could begin, so it directly drives the entire pipeline.
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Azure Virtual Machines
Why it's wrong here
Azure Virtual Machines provide general-purpose infrastructure-as-a-service compute, meaning you can install any software on them, but they are not a dedicated analytics component. While you could manually set up SQL Server or Apache Spark on a VM, the question asks for built-in parts of an end-to-end analytics solution, and VMs are unmanaged building blocks rather than first-party analytics services like Data Factory or Power BI.
- ✓
Data visualization (Power BI)
Why this is correct
Data visualization is the final stage where processed data is turned into actionable insight, and Power BI is the Azure analytics service that connects to data sources, builds interactive reports, and supports real-time dashboards. This makes it a correct choice because it provides the business-facing presentation layer that is essential for communicating findings to decision-makers.
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Data storage (Azure Data Lake Storage)
Why this is correct
Azure Data Lake Storage (ADLS) is a hyperscale, cost-effective repository that stores raw and transformed data as files, supporting both structured and unstructured data, and it integrates with processing tools like Databricks and Data Factory. As a core component, it serves as the central data lake that holds the organization's analytics data for downstream consumption.
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Microsoft Entra ID
Why it's wrong here
Microsoft Entra ID is an identity and access management service, not a data processing or analytics tool. While it can secure Azure Data Factory, ADLS, and Power BI by enforcing authentication and role-based access control, it does not itself ingest, store, transform, or visualize data, so it is not one of the three correct analytics components.
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Related to this question
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Real-Time Analytics on Azure
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
Data ingestion
Data ingestion is the process of moving data from various sources into a storage system where it can be accessed, analyzed, and used.
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