- A
Convert the data to Avro format to reduce file size.
Why wrong: Avro may compress differently but does not address the small file count issue.
- B
Increase the maximum number of workers to 20 and use a larger instance type.
Why wrong: Scaling out may help but does not address the root cause of small file overhead.
- C
Modify the Stream Analytics job to output larger files (e.g., set the minimum file size to 100 MB) and use coalesce in Databricks to reduce the number of output partitions.
Larger input files reduce metadata overhead, and coalescing reduces output files, improving performance and reducing costs.
- D
Move the data to Azure Blob Storage Premium tier to reduce latency.
Why wrong: Premium tier increases cost and does not solve the small file problem.
DP-203 Practice Question: Monitor and optimize data storage and processing
This DP-203 practice question tests your understanding of monitor and optimize data storage and processing. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
You are a data engineer for a financial services company. You manage an Azure Data Lake Storage Gen2 account that stores real-time stock trade data ingested from Azure Event Hubs via Azure Stream Analytics. The data is partitioned by date and symbol. Each day, a downstream Azure Databricks job runs an ETL process to aggregate trades into 5-minute bars and writes the results to a separate container. The Databricks job runs on a cluster with 10 worker nodes (Standard_DS3_v2) using Auto-Scaling enabled (2-10 workers). Recently, the job has been taking longer than expected, and you observe that the cluster is often at 10 workers but still the job duration increased by 30%. The storage account shows high transaction costs. You suspect the issue is related to how data is read. What should you do to optimize the job's performance and reduce costs?
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
Modify the Stream Analytics job to output larger files (e.g., set the minimum file size to 100 MB) and use coalesce in Databricks to reduce the number of output partitions.
Option C is correct because the performance issue stems from reading many small files (small file problem) in Azure Data Lake Storage Gen2, which increases transaction costs and slows down Spark jobs. By configuring Stream Analytics to output larger files (e.g., minimum 100 MB) and using coalesce in Databricks to reduce output partitions, you minimize the number of files read/written, reducing overhead and transaction costs. This directly addresses the root cause—high transaction costs and cluster saturation at 10 workers—without unnecessary scaling or tier changes.
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.
- ✗
Convert the data to Avro format to reduce file size.
Why it's wrong here
Avro may compress differently but does not address the small file count issue.
- ✗
Increase the maximum number of workers to 20 and use a larger instance type.
Why it's wrong here
Scaling out may help but does not address the root cause of small file overhead.
- ✓
Modify the Stream Analytics job to output larger files (e.g., set the minimum file size to 100 MB) and use coalesce in Databricks to reduce the number of output partitions.
Why this is correct
Larger input files reduce metadata overhead, and coalescing reduces output files, improving performance and reducing costs.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Move the data to Azure Blob Storage Premium tier to reduce latency.
Why it's wrong here
Premium tier increases cost and does not solve the small file problem.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume performance issues require scaling up (more workers or larger instances) or changing storage tiers, when the real problem is inefficient data layout (small files) causing excessive I/O and transaction costs.
Detailed technical explanation
How to think about this question
The small file problem in Spark occurs because each file requires a separate task, and the overhead of opening/closing files (via Hadoop FileSystem APIs) dominates processing time, especially with thousands of tiny files. Stream Analytics outputs files based on event count or time windows; setting a minimum file size (e.g., 100 MB) forces larger, fewer files, reducing the number of partitions Spark must read. Coalesce in Databricks reduces the number of output partitions without a full shuffle, which is critical for avoiding data movement costs when writing aggregated results.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DP-203 question test?
Monitor and optimize data storage and processing — This question tests Monitor and optimize data storage and processing — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Modify the Stream Analytics job to output larger files (e.g., set the minimum file size to 100 MB) and use coalesce in Databricks to reduce the number of output partitions. — Option C is correct because the performance issue stems from reading many small files (small file problem) in Azure Data Lake Storage Gen2, which increases transaction costs and slows down Spark jobs. By configuring Stream Analytics to output larger files (e.g., minimum 100 MB) and using coalesce in Databricks to reduce output partitions, you minimize the number of files read/written, reducing overhead and transaction costs. This directly addresses the root cause—high transaction costs and cluster saturation at 10 workers—without unnecessary scaling or tier changes.
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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Last reviewed: Jun 11, 2026
This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.
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