DP-203 Design and develop data processing Practice Question
This DP-203 practice question tests your understanding of design and develop data processing. 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.
A data engineer needs to process a large dataset stored in Azure Blob Storage using Azure Databricks. The dataset consists of millions of small CSV files. The processing job is slow due to the overhead of reading many small files. Which technique should be used to improve performance?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Coalesce the small files into larger files using a Databricks notebook
Option C is correct because coalescing the millions of small CSV files into larger files reduces the metadata overhead and I/O operations when reading from Azure Blob Storage. Databricks can then process fewer, larger files more efficiently, as each task handles a substantial data chunk rather than incurring the cost of opening and closing many small files.
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.
✗
Increase the number of worker nodes in the cluster
Why it's wrong here
More workers may not reduce the overhead of many small files.
✗
Convert the CSV files to Parquet format
Why it's wrong here
Parquet is efficient but doesn't reduce file count overhead.
✓
Coalesce the small files into larger files using a Databricks notebook
Why this is correct
Reduces file count and improves read performance.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Delta Lake caching to store the data in memory
Why it's wrong here
Caching helps after first read, but initial read still slow.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume performance issues are always solved by scaling out (Option A) or by switching formats (Option B), but the DP-203 exam specifically tests the understanding that small file overhead is a distinct problem requiring file consolidation.
Detailed technical explanation
How to think about this question
Under the hood, Azure Blob Storage has a per-file request overhead (e.g., listing, opening, reading metadata), and Spark’s task scheduler assigns one task per file partition. Coalescing files to a target size (e.g., 128 MB or 256 MB) aligns with HDFS block sizes and allows Spark to use fewer, more efficient tasks. In real-world scenarios, this technique is often combined with Auto Loader’s file notification mode to incrementally process new small files while periodically running a compaction job.
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.
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 develop data processing — This question tests Design and develop data processing — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Coalesce the small files into larger files using a Databricks notebook — Option C is correct because coalescing the millions of small CSV files into larger files reduces the metadata overhead and I/O operations when reading from Azure Blob Storage. Databricks can then process fewer, larger files more efficiently, as each task handles a substantial data chunk rather than incurring the cost of opening and closing many small files.
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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