DP-203 Design and implement data storage Practice Question
Your company is building a real-time analytics solution for monitoring manufacturing equipment. Sensors send JSON data every second to an Azure Event Hubs instance. The data must be stored in Azure Data Lake Storage Gen2 in Parquet format, partitioned by date and hour. You use Azure Stream Analytics to read from Event Hubs and write to ADLS Gen2. Currently, the output is writing many small Parquet files (under 1 MB each), which is causing performance issues when reading the data. You need to optimize the output to produce fewer, larger files while maintaining low latency. What should you do?
⚠ Common exam trap
A common mix-up: candidates confuse partitioning granularity with file sizing, incorrectly assuming finer partitioning (Option A) or format changes (Option B) will solve the small-file problem, when the actual solution is to adjust the batching threshold in the output sink.
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
✓
Increase the 'Maximum events per batch' setting in the Stream Analytics output to ADLS Gen2
Increasing the 'Maximum events per batch' setting in the Stream Analytics output to ADLS Gen2 allows more events to be accumulated before writing a file, resulting in fewer, larger Parquet files. This directly addresses the small-file problem while maintaining low latency, as the batching is time-bound and does not introduce excessive delay.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Partition the output by minute instead of hour to distribute data more
Why it's wrong here
Finer partitioning would increase the number of files, not reduce it.
- ✗
Change the output format to Avro to improve compression
Why it's wrong here
Format change does not directly control file size; still may produce many small files.
- ✓
Increase the 'Maximum events per batch' setting in the Stream Analytics output to ADLS Gen2
Why this is correct
Buffering more events per batch produces larger files.
- ✗
Decrease the 'Maximum events per batch' setting to reduce latency
Why it's wrong here
Decreasing batch size would create even smaller files.
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