- A
Run VACUUM to remove old versions and compact small files
Compaction reduces file count.
- B
Disable auto-optimize to reduce overhead
Why wrong: Auto-optimize helps performance.
- C
Run OPTIMIZE with ZORDER BY on frequently filtered columns
Improves data skipping.
- D
Increase the number of shuffle partitions
Why wrong: Not specific to Delta.
- E
Partition the table by high-cardinality columns
Enables partition pruning.
Question 515 of 851
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.
Which THREE actions improve the performance of a Delta table in Azure Databricks? (Choose three.)
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
✓
Run VACUUM to remove old versions and compact small files
Option A is correct because running VACUUM on a Delta table removes old file versions that are no longer referenced by the Delta transaction log, freeing up storage and reducing the number of small files that must be read during queries. This compaction of small files improves I/O efficiency and query performance, especially in tables with frequent updates or deletes.
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.
- ✓
Run VACUUM to remove old versions and compact small files
Why this is correct
Compaction reduces file count.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Disable auto-optimize to reduce overhead
Why it's wrong here
Auto-optimize helps performance.
- ✓
Run OPTIMIZE with ZORDER BY on frequently filtered columns
Why this is correct
Improves data skipping.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of shuffle partitions
Why it's wrong here
Not specific to Delta.
- ✓
Partition the table by high-cardinality columns
Why this is correct
Enables partition pruning.
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 'increasing shuffle partitions' (a Spark tuning parameter) with a direct performance improvement for Delta tables, when in fact it can worsen performance and is not a recommended action for Delta-specific optimization.
Detailed technical explanation
How to think about this question
Under the hood, Delta Lake uses a transaction log to track file versions, and VACUUM removes files older than a retention threshold (default 7 days) after ensuring no concurrent transactions reference them. OPTIMIZE with ZORDER BY reorganizes data within files based on column values, creating a clustered index-like structure that allows data skipping to prune files during queries with filters on those columns, drastically reducing scan time. Partitioning by high-cardinality columns (e.g., user_id) can create too many small directories, causing metadata overhead and poor file pruning; low- to medium-cardinality columns (e.g., date, region) are preferred for partitioning.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Develop data processing — study guide chapter
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FAQ
Questions learners often ask
What does this DP-203 question test?
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: Run VACUUM to remove old versions and compact small files — Option A is correct because running VACUUM on a Delta table removes old file versions that are no longer referenced by the Delta transaction log, freeing up storage and reducing the number of small files that must be read during queries. This compaction of small files improves I/O efficiency and query performance, especially in tables with frequent updates or deletes.
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: Jul 4, 2026
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