Quick Answer
The answer is data storage, data ingestion, and data transformation. These three components form the core of an end-to-end analytics solution on Azure because they represent the essential pipeline stages: first, raw data must be ingested from source systems using a service like Azure Data Factory (ADF), which orchestrates and automates movement via over 90 built-in connectors; second, that data must be stored durably and scalably, typically in Azure Data Lake Storage; and third, it must be transformed—often through Azure Databricks or Synapse Analytics—to prepare it for analysis. On the DP-900 exam, this question tests your understanding of the analytics lifecycle rather than memorizing every Azure service; a common trap is confusing visualization tools like Power BI as a core component when it is actually the final consumption layer. To remember, think of the pipeline as “Ingest, Store, Transform”—the three pillars that must exist before any reporting can happen.
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 part of an end-to-end analytics solution on Azure?
Data ingestion (Azure Data Factory)
Ingests data from various sources.
Azure Virtual Machines
Why wrong: General compute, not a core analytics component.
Data visualization (Power BI)
Presents insights from data.
Data storage (Azure Data Lake Storage)
Stores raw and processed data.
Microsoft Entra ID
Why wrong: Identity management, not part of analytics pipeline.
Answer choices
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)
Data ingestion using Azure Data Factory (ADF) is correct because ADF is the primary cloud-based ETL/ELT service for orchestrating and automating data movement from various sources into Azure storage or compute. It supports over 90 built-in connectors, scheduled triggers, and data flows, making it essential for the 'ingest' phase of an analytics pipeline.
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.
Data ingestion (Azure Data Factory)
Why this is correct
Ingests data from various sources.
Related concept
Read the scenario before looking for a memorised answer.
Azure Virtual Machines
Why it's wrong here
General compute, not a core analytics component.
Data visualization (Power BI)
Why this is correct
Presents insights from data.
Related concept
Read the scenario before looking for a memorised answer.
Data storage (Azure Data Lake Storage)
Why this is correct
Stores raw and processed data.
Related concept
Read the scenario before looking for a memorised answer.
Microsoft Entra ID
Why it's wrong here
Identity management, not part of analytics pipeline.
Common exam traps
The trap here is that candidates often 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.
Detailed technical explanation
An end-to-end analytics solution on Azure typically follows the 'modern data warehouse' pattern: ingest via Azure Data Factory or Event Hubs, store raw data in Azure Data Lake Storage (ADLS) Gen2, transform using Azure Databricks or Synapse Pipelines, and visualize with Power BI. ADLS Gen2 combines blob storage with a hierarchical namespace, enabling POSIX-like access control and high-throughput analytics. Power BI connects directly to ADLS via DirectQuery or imports data into its in-memory engine (VertiPaq) for fast interactive reports.
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: Data ingestion (Azure Data Factory) — Data ingestion using Azure Data Factory (ADF) is correct because ADF is the primary cloud-based ETL/ELT service for orchestrating and automating data movement from various sources into Azure storage or compute. It supports over 90 built-in connectors, scheduled triggers, and data flows, making it essential for the 'ingest' phase of an analytics pipeline.
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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