Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
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
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
A
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Azure Data Factory
Azure Data Factory is designed for orchestrating batch data movement and transformation, not for handling real-time streaming data from Event Hubs.
B
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Azure Databricks
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
Best answer
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
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Azure Synapse Pipelines
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 trap
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Technical deep dive
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
Related practice questions
Related DP-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
More questions from this exam
Keep practising from the same exam bank, or move into a focused topic page if this question exposed a weak area.
Question 1
A data engineer needs to process streaming data from IoT devices and store the results in Azure Data Lake Storage for long-term analytics. The data must be processed in near real-time to detect anomalies and trigger alerts. Which Azure service should the engineer use for stream processing?
Question 2
A data engineer needs to query data stored in CSV files in Azure Data Lake Storage Gen2 using T-SQL in Azure Synapse Analytics, without loading the data into the database. Which feature should they use?
Question 3
A data engineer needs to process raw clickstream data from multiple websites that is stored in Azure Blob Storage as JSON files. The processing must run automatically every hour, transform the data into a structured format for reporting, and handle schema changes in the source data without manual intervention. Which Azure service should be used?
Question 4
A data engineer is designing a data lake architecture in Azure. They plan to first ingest raw data from various sources into a landing zone in Azure Data Lake Storage Gen2. Then they will clean, validate, and deduplicate that data in a second zone. Finally, they will create aggregated, business-ready datasets in a third zone for analysts. This layered approach is known as which architecture?
Question 5
A data engineer needs to transform large datasets stored in Azure Data Lake Storage Gen2 using Python and Apache Spark. They want a serverless compute option that automatically scales and requires no cluster management. Which Azure service should they use?
Question 6
A company collects customer feedback forms. Each form contains always-present fields like CustomerID and SubmissionDate, but also a free-text Comments field and optional fields like Rating or ProductCategory that vary between forms. How should this data be classified?
FAQ
Questions learners often ask
What does this DP-900 question test?
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 a real-time event processing engine that can consume from Event Hubs, apply transformations (like filtering, aggregation, and windowing), and output to multiple sinks including Azure Data Lake Storage. Azure Data Factory is for orchestration and batch ETL, not real-time streaming. Azure Databricks could be used, but Stream Analytics is purpose-built for streaming transformations with minimal code. Azure Synapse Pipelines is similar to Data Factory and is not optimized for real-time processing.
What should I do if I get this DP-900 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
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