The answer is to use coalesce() with a smaller number of partitions. This is correct because coalesce() reduces the number of partitions without triggering a full shuffle, meaning it minimizes the number of output files while preserving performance by keeping data movement local. In contrast, repartition() also reduces output files but forces a full shuffle, which can be slower for large DataFrames. On the DP-203 exam, this scenario tests your understanding of Spark optimization in Azure Synapse Analytics, specifically how to handle the common small-file problem that degrades write performance. A frequent trap is assuming repartition() is always better for reducing partitions, but coalesce() is the efficient choice when you are decreasing partitions, not increasing them. Remember the mnemonic: "Coalesce to compress, repartition to redistribute"—use coalesce when shrinking partitions to avoid an expensive shuffle.
DP-203 Develop data processing Practice Question
This DP-203 practice question tests your understanding of 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.
You are optimizing a Spark DataFrame transformation in Azure Synapse Analytics. The DataFrame has 20 columns and 100 million rows. You notice that the job is slow due to many small files being written to the output. Which two actions can you take to reduce the number of output files? (Choose two.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use coalesce() to reduce the number of partitions without a shuffle.
Option A is correct because `coalesce()` reduces the number of partitions without triggering a full shuffle, which minimizes the number of output files while preserving performance. Since the DataFrame already has 100 million rows and 20 columns, coalescing to fewer partitions directly reduces the number of files written, addressing the small-file problem efficiently.
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.
✓
Use coalesce() to reduce the number of partitions without a shuffle.
Why this is correct
Coalesce reduces partitions and thus output files, minimizing shuffle.
Related concept
Read the scenario before looking for a memorised answer.
✗
Enable caching on the DataFrame before writing.
Why it's wrong here
Caching does not reduce the number of output files.
✗
Apply bucketing on a column to group data.
Why it's wrong here
Bucketing organizes data into buckets but does not necessarily reduce file count; it can increase it.
✗
Increase the number of partitions using repartition() with a larger number.
Why it's wrong here
More partitions result in more output files.
✓
Use repartition() with a smaller number of partitions.
Why this is correct
Fewer partitions mean fewer files written.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse `coalesce()` with `repartition()`, assuming both cause a shuffle, or they mistakenly think increasing partitions (Option D) will improve performance when it actually exacerbates the small-file issue.
Trap categories for this question
Command / output trap
Caching does not reduce the number of output files.
Detailed technical explanation
How to think about this question
Under the hood, `coalesce()` works by merging existing partitions without a full shuffle, which is efficient for reducing partition count when the data is already distributed across executors. In contrast, `repartition()` triggers a full shuffle, which can be costly for large DataFrames. A real-world scenario is when writing to a data lake with many small files causes slow listing operations and high metadata overhead; reducing partitions to match the target file size (e.g., 128 MB per file) optimizes downstream reads.
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.
Develop data processing — This question tests Develop data processing — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use coalesce() to reduce the number of partitions without a shuffle. — Option A is correct because `coalesce()` reduces the number of partitions without triggering a full shuffle, which minimizes the number of output files while preserving performance. Since the DataFrame already has 100 million rows and 20 columns, coalescing to fewer partitions directly reduces the number of files written, addressing the small-file problem efficiently.
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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