DP-203 Design and implement data storage Practice Question
This DP-203 practice question tests your understanding of design and implement data storage. 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.
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?
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
Option C is correct because 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.
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.
✗
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
Decrease the 'Maximum events per batch' setting to reduce latency
Why it's wrong here
Decreasing batch size would create even smaller files.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may 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.
Detailed technical explanation
How to think about this question
The 'Maximum events per batch' setting in Azure Stream Analytics controls the number of events accumulated before a write operation to ADLS Gen2 is triggered. Under the hood, Stream Analytics uses a time-based window (e.g., 1 minute) and a count-based threshold; increasing the batch size allows more data to be buffered, producing larger files (e.g., 64 MB or more) that are optimal for Parquet columnar storage and efficient for downstream analytics engines like Azure Synapse or Spark. In real-world scenarios, this tuning is critical for streaming pipelines where small files can cause excessive metadata operations and slow query performance.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
Design and implement data storage — This question tests Design and implement data storage — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the 'Maximum events per batch' setting in the Stream Analytics output to ADLS Gen2 — Option C is correct because 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.
What should I do if I get this DP-203 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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Question Discussion
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