- A
Disable automatic schema detection to reduce overhead.
Why wrong: Schema detection overhead is minimal and not a performance requirement.
- B
Use Delta Lake's OPTIMIZE command to compact small files.
Compacting small files improves read performance.
- C
Use Delta Lake Z-order optimization on frequently filtered columns.
Z-order improves data skipping.
- D
Cache the entire DataFrame in memory after reading.
Why wrong: Caching might not fit large datasets and is not required.
- E
Enable auto-compaction in Spark configuration.
Why wrong: Auto-compaction is a convenience, not a requirement.
Question 160 of 851
DP-203 Develop data processing Practice Question
This DP-203 practice question tests your understanding of develop data processing. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 designing a data processing solution using Azure Databricks. You need to read data from Azure Data Lake Storage Gen2, transform it using Spark SQL, and write to a Delta table. Which TWO configurations are required to ensure optimal performance for large datasets?
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
✓
Use Delta Lake's OPTIMIZE command to compact small files.
Option B is correct because the OPTIMIZE command in Delta Lake compacts small files into larger ones, reducing the number of files that Spark must read during subsequent queries and writes. This is critical for large datasets where many small files can cause significant overhead in file listing and task scheduling. Option C is correct because Z-order optimization on frequently filtered columns improves data skipping, allowing Delta Lake to prune irrelevant files during scans, which dramatically reduces I/O and speeds up query performance.
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.
- ✗
Disable automatic schema detection to reduce overhead.
Why it's wrong here
Schema detection overhead is minimal and not a performance requirement.
- ✓
Use Delta Lake's OPTIMIZE command to compact small files.
Why this is correct
Compacting small files improves read performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use Delta Lake Z-order optimization on frequently filtered columns.
Why this is correct
Z-order improves data skipping.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Cache the entire DataFrame in memory after reading.
Why it's wrong here
Caching might not fit large datasets and is not required.
- ✗
Enable auto-compaction in Spark configuration.
Why it's wrong here
Auto-compaction is a convenience, not a requirement.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse auto-compaction as a Spark configuration (Option E) when it is actually a Delta Lake table property, and they overlook that caching (Option D) is not beneficial for write-heavy pipelines with large datasets.
Detailed technical explanation
How to think about this question
Under the hood, Delta Lake's OPTIMIZE command uses bin-packing to merge small Parquet files into files of a configurable target size (default 256 MB), which reduces the metadata overhead in the Delta transaction log and improves read efficiency. Z-order optimization works by sorting data within files based on multiple columns, creating a multidimensional clustering that allows Delta Lake's data skipping to use min-max statistics to exclude entire files that do not match filter predicates. In real-world scenarios, combining OPTIMIZE with Z-order on high-cardinality columns like 'date' and 'customer_id' can reduce query times by over 90% for selective filters.
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.
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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: Use Delta Lake's OPTIMIZE command to compact small files. — Option B is correct because the OPTIMIZE command in Delta Lake compacts small files into larger ones, reducing the number of files that Spark must read during subsequent queries and writes. This is critical for large datasets where many small files can cause significant overhead in file listing and task scheduling. Option C is correct because Z-order optimization on frequently filtered columns improves data skipping, allowing Delta Lake to prune irrelevant files during scans, which dramatically reduces I/O and speeds up query performance.
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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