- A
Azure Data Factory
Why wrong: Azure Data Factory is designed for orchestrating batch data movement and transformation, not for handling real-time streaming data from Event Hubs.
- B
Azure Databricks
Why wrong: Azure Databricks is a powerful analytics platform that can process streaming data, but it requires more configuration and manual coding (e.g., using Structured Streaming) compared to a dedicated service like Stream Analytics for this specific use case of simple transformations and output to a data lake.
- C
Azure Stream Analytics
Azure Stream Analytics is a serverless real-time analytics service that can ingest data from Event Hubs, perform time-windowed aggregations, clean data, and output to Azure Data Lake Storage in the desired columnar format. It is the most straightforward and cost-effective choice for this streaming ETL scenario.
- D
Azure Synapse Pipelines
Why wrong: Azure Synapse Pipelines is a data integration and orchestration tool, similar to Azure Data Factory, and is not designed for real-time streaming processing from Event Hubs.
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.
A manufacturing company has a streaming data pipeline that ingests sensor data from factory equipment into Azure Event Hubs. The data must be prepared for reporting by cleaning invalid records, removing duplicates, and aggregating readings into 5-minute windows. The transformed data needs to be stored in a columnar format in a data lake to support efficient querying by data analysts using SQL. Which Azure service should perform the data transformation and loading?
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 Stream Analytics
Azure Stream Analytics is the correct choice because it is designed for real-time stream processing, directly consuming data from Azure Event Hubs, performing transformations like cleaning invalid records, removing duplicates, and aggregating over tumbling windows (e.g., 5-minute windows), and outputting the results in a columnar format (e.g., Parquet) to Azure Data Lake Storage. This aligns perfectly with the requirement for a low-latency, continuous transformation pipeline without needing additional orchestration or compute clusters.
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 it's wrong here
Azure Data Factory is designed for orchestrating batch data movement and transformation, not for handling real-time streaming data from Event Hubs.
- ✗
Azure Databricks
Why it's wrong here
Azure Databricks is a powerful analytics platform that can process streaming data, but it requires more configuration and manual coding (e.g., using Structured Streaming) compared to a dedicated service like Stream Analytics for this specific use case of simple transformations and output to a data lake.
- ✓
Azure Stream Analytics
Why this is correct
Azure Stream Analytics is a serverless real-time analytics service that can ingest data from Event Hubs, perform time-windowed aggregations, clean data, and output to Azure Data Lake Storage in the desired columnar format. It is the most straightforward and cost-effective choice for this streaming ETL scenario.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Azure Synapse Pipelines
Why it's wrong here
Azure Synapse Pipelines is a data integration and orchestration tool, similar to Azure Data Factory, and is not designed for real-time streaming processing from Event Hubs.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure Data Factory or Synapse Pipelines as suitable for streaming transformations because they see 'pipeline' or 'data movement' keywords, but these services are batch-oriented and cannot perform real-time windowed aggregations directly from Event Hubs.
Trap categories for this question
Similar concept trap
Azure Synapse Pipelines is a data integration and orchestration tool, similar to Azure Data Factory, and is not designed for real-time streaming processing from Event Hubs.
Command / output trap
Azure Databricks is a powerful analytics platform that can process streaming data, but it requires more configuration and manual coding (e.g., using Structured Streaming) compared to a dedicated service like Stream Analytics for this specific use case of simple transformations and output to a data lake.
Detailed technical explanation
How to think about this question
Under the hood, Azure Stream Analytics uses a SQL-like query language to define transformations, and its tumbling window function (e.g., TumblingWindow(second, 300)) automatically groups events into non-overlapping 5-minute intervals for aggregation. It also supports exactly-once semantics for output to Azure Data Lake Storage Gen2 in Parquet format, ensuring data consistency even during failures. A real-world scenario where this matters is when sensor data arrives out of order; Stream Analytics can handle late-arriving events via a configurable late arrival tolerance window, preventing duplicates in the aggregated output.
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.
What to study next
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FAQ
Questions learners often ask
What does this DP-900 question test?
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 Stream Analytics — Azure Stream Analytics is the correct choice because it is designed for real-time stream processing, directly consuming data from Azure Event Hubs, performing transformations like cleaning invalid records, removing duplicates, and aggregating over tumbling windows (e.g., 5-minute windows), and outputting the results in a columnar format (e.g., Parquet) to Azure Data Lake Storage. This aligns perfectly with the requirement for a low-latency, continuous transformation pipeline without needing additional orchestration or compute clusters.
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.
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Last reviewed: Jun 11, 2026
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