- A
Run OPTIMIZE on the table.
Why wrong: OPTIMIZE compacts small files into larger ones but does not optimize for selective filtering.
- B
Run ZORDER BY on the date column.
Z-ordering co-locates column data, improving data skipping for filters on that column.
- C
Run ANALYZE STATISTICS on the table.
Why wrong: ANALYZE STATISTICS collects metadata for the optimizer but does not physically reorganize data.
- D
Run VACUUM to clean up old versions.
Why wrong: VACUUM is for housekeeping, not read optimization.
Question 718 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.
You are working with a Delta Lake table in Azure Databricks. The table is updated frequently with new data and occasionally with updates to existing rows. You need to optimize read performance for queries that filter on a specific date column. The table is partitioned by date. Which optimization technique should you apply?
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 ZORDER BY on the date column.
Option B is correct because ZORDER BY on the date column co-locates related data within each partition, significantly reducing the amount of data scanned for queries that filter on that column. This is especially beneficial for a frequently updated Delta table where data is already partitioned by date, as ZORDER BY optimizes the layout within partitions without changing the partition scheme.
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 OPTIMIZE on the table.
Why it's wrong here
OPTIMIZE compacts small files into larger ones but does not optimize for selective filtering.
- ✓
Run ZORDER BY on the date column.
Why this is correct
Z-ordering co-locates column data, improving data skipping for filters on that column.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Run ANALYZE STATISTICS on the table.
Why it's wrong here
ANALYZE STATISTICS collects metadata for the optimizer but does not physically reorganize data.
- ✗
Run VACUUM to clean up old versions.
Why it's wrong here
VACUUM is for housekeeping, not read optimization.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse OPTIMIZE (file compaction) with ZORDER BY (data clustering), assuming any performance optimization technique will improve filter queries, but only ZORDER BY physically reorders data to enable efficient data skipping.
Detailed technical explanation
How to think about this question
ZORDER BY works by sorting data within each partition based on the specified column's values, creating a clustered index-like layout that allows Delta Lake's file-skipping mechanism to prune entire files during scans. In a real-world scenario with frequent updates, ZORDER BY also improves merge and update performance by reducing the number of files that need to be rewritten. The technique is most effective when the ZORDER column has high cardinality and is commonly used in filter predicates.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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: Run ZORDER BY on the date column. — Option B is correct because ZORDER BY on the date column co-locates related data within each partition, significantly reducing the amount of data scanned for queries that filter on that column. This is especially beneficial for a frequently updated Delta table where data is already partitioned by date, as ZORDER BY optimizes the layout within partitions without changing the partition scheme.
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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