DP-203 Azure Stream Analytics Practice Question
A company ingests streaming data from multiple sources into Azure Event Hubs. The data must be stored in Azure Data Lake Storage Gen2 in Parquet format, partitioned by date and hour. The solution must minimize cost and processing latency. Which THREE actions should be taken?
⚠ Common exam trap
Candidates often confuse Event Hubs Capture's Avro output with the ability to write Parquet directly, or they mistakenly choose batch-oriented tools like Data Factory when a real-time streaming service (Stream Analytics) meets all requirements.
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
✓
Use Azure Stream Analytics to read from Event Hubs and write to Data Lake Storage in Parquet format.
Azure Stream Analytics (C) is ideal for this scenario because it processes streaming data from Event Hubs in real time and can write directly to Azure Data Lake Storage Gen2 in Parquet format, which provides efficient compression and columnar storage for analytics. Additionally, configuring the output partitioning by date and hour (D) ensures the data is organized in the required structure without post-processing. Azure Databricks (E) can also process the stream from Event Hubs and write Parquet with partitioning, offering flexibility for complex transformations while still meeting latency requirements, though it may incur slightly higher cost than Stream Analytics for simple pipelines. Options A and B are incorrect: Event Hubs Capture writes Avro, not Parquet, and Data Factory introduces batch latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable Event Hubs Capture to automatically write data to Data Lake Storage in Avro format.
Why it's wrong here
Event Hubs Capture writes data in Avro format, not Parquet, so it does not meet the format requirement.
- ✗
Use Azure Data Factory to copy data from Event Hubs to Data Lake Storage every 5 minutes.
Why it's wrong here
Azure Data Factory copies data in batches (e.g., every 5 minutes), introducing latency that may not satisfy real-time requirements.
- ✓
Use Azure Stream Analytics to read from Event Hubs and write to Data Lake Storage in Parquet format.
Why this is correct
Azure Stream Analytics provides real-time processing from Event Hubs and supports writing directly to Data Lake Storage in Parquet format with partitioning.
- ✓
Configure Stream Analytics output to partition by date and hour.
Why this is correct
Configuring Stream Analytics output to partition by date and hour ensures the data is stored in the required folder structure without additional processing.
- ✓
Use Azure Databricks to process the stream and write to Data Lake Storage.
Why this is correct
Azure Databricks can consume Event Hubs streams and write Parquet files with custom partitioning, supporting low-latency while enabling advanced transformations if needed.
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
Key term
Azure Stream Analytics
Azure Stream Analytics is a fully managed, real-time data processing service that analyzes and transforms high volumes of streaming data from various sources to deliver low-latency insights and trigger actions.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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