Quick Answer
The correct answer is to run OPTIMIZE on the Delta table to compact small files. This works because Delta Lake’s OPTIMIZE command uses bin-packing to merge numerous small files into larger ones, defaulting to a target size of 256 MB, which drastically reduces the number of files the query engine must scan. On the Microsoft Azure Data Engineer Associate DP-203 exam, this scenario tests your understanding of Delta Lake’s file management and the performance impact of daily partitioned ingestion, where each batch often creates tiny files. A common trap is to suggest ZORDER BY instead, but that optimizes data skipping for specific columns, not file count—so remember: small files = OPTIMIZE, not ZORDER. For a quick memory tip, think “OPTIMIZE compresses the mess, ZORDER sorts the address.”
This DP-203 practice question tests your understanding of monitor and optimize data storage and 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 designing a data processing solution using Azure Databricks with Delta Lake. The data is partitioned by date and ingested daily. You notice that the Delta table has many small files, causing slow read performance. Which strategy should you recommend to optimize the table for faster queries?
Run OPTIMIZE on the table to compact small files.
OPTIMIZE merges small files into larger ones.
Run ZORDER BY on the date column.
Why wrong: Z-ordering improves data skipping, not file compaction.
Run VACUUM to delete old files.
Why wrong: VACUUM removes unreferenced files, does not compact.
Increase the number of partitions by adding a new partition column.
Why wrong: More partitions lead to more small files.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Run OPTIMIZE on the table to compact small files.
Option A is correct because running OPTIMIZE on a Delta Lake table compacts many small files into larger ones, reducing the number of files that need to be read during queries. This directly addresses the slow read performance caused by the small file problem, which is common in daily partitioned ingestion. OPTIMIZE uses bin-packing to merge files up to a target size (default 256 MB), improving scan efficiency without changing the data.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Run OPTIMIZE on the table to compact small files.
Why this is correct
OPTIMIZE merges small files into larger ones.
Related concept
Read the scenario before looking for a memorised answer.
Run ZORDER BY on the date column.
Why it's wrong here
Z-ordering improves data skipping, not file compaction.
Run VACUUM to delete old files.
Why it's wrong here
VACUUM removes unreferenced files, does not compact.
Increase the number of partitions by adding a new partition column.
Why it's wrong here
More partitions lead to more small files.
Common exam traps
The trap here is that candidates may confuse ZORDER BY (which improves data skipping but not file count) with OPTIMIZE (which reduces file count), or mistakenly think VACUUM or adding partitions solves the small file problem, when in fact they either don't address it or make it worse.
Detailed technical explanation
Under the hood, Delta Lake's OPTIMIZE operation performs bin-packing by grouping small files into larger ones based on a target file size (configurable via spark.databricks.delta.optimize.maxFileSize). It also rewrites the transaction log to point to the new compacted files, ensuring ACID compliance. In real-world scenarios, daily ingestion without compaction can lead to thousands of tiny files (e.g., 1 MB each), causing high overhead in listing and opening files during queries, which OPTIMIZE resolves by merging them into fewer, larger files (e.g., 256 MB).
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Monitor and optimize data storage and processing — This question tests Monitor and optimize data storage and processing — Read the scenario before looking for a memorised answer..
The correct answer is: Run OPTIMIZE on the table to compact small files. — Option A is correct because running OPTIMIZE on a Delta Lake table compacts many small files into larger ones, reducing the number of files that need to be read during queries. This directly addresses the slow read performance caused by the small file problem, which is common in daily partitioned ingestion. OPTIMIZE uses bin-packing to merge files up to a target size (default 256 MB), improving scan efficiency without changing the data.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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