Databricks-DA-Assoc Managing Data Practice Question
A data analyst queries a partitioned Delta table `logs.events` that has a `event_date` column. The query filters `WHERE event_date = '2024-06-01'`. Performance is poor even though the table is partitioned on `event_date`. Which factor most likely explains the poor performance?
⚠ Common exam trap
The trap here is assuming that correct partition pruning guarantees fast reads, when heavy small-file fragmentation within the selected partition can still dominate query time.
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
✓
The table has thousands of small files within the matching partition, so pruning selects the right partition but still reads excessive files.
Partition pruning only eliminates partitions that cannot match; it does not fix fragmentation inside a matching partition. If the correct partition contains many small files, the query reads all of them, incurring high overhead. The likely remedy is compaction with OPTIMIZE on that partition, possibly combined with ZORDER, rather than changing the partition column or the predicate form.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The table has thousands of small files within the matching partition, so pruning selects the right partition but still reads excessive files.
Why this is correct
Partition pruning narrows the scan to the correct partition, but if that partition contains thousands of tiny files from frequent appends, the engine still pays heavy per-file overhead. Pruning and compaction solve different problems. This explanation is consistent with the facts: the partition column matches the predicate, yet the layout inside the partition remains fragmented and slow.
- ✗
The `event_date` column is stored as a string while the predicate compares to a string literal, forcing a full scan due to implicit casting.
Why it's wrong here
Comparing a string column to a string literal does not introduce casting that defeats partition pruning. Partition pruning works on the partition key values regardless of whether they are strings or dates, as long as the predicate is a simple equality or range on the partition column. This option invents a casting problem that does not exist in the described query.
- ✗
The table was partitioned by a high-cardinality column other than `event_date`, such as a unique event identifier.
Why it's wrong here
If the physical partitioning used a different column, the predicate on `event_date` could not prune files and performance would suffer. However, the scenario states the table is partitioned on `event_date`, so this explanation contradicts the given facts. It is plausible in general but does not match the described setup and therefore is not the best explanation here.
- ✗
The predicate is wrapped in a non-deterministic function such as `WHERE CAST(event_date AS DATE) = current_date()`, preventing partition pruning.
Why it's wrong here
Wrapping the partition column in a function can indeed prevent pruning, and using current_date would be non-deterministic. But the stem shows a plain equality against a literal date, not a function call, so this does not describe the actual query. It is a real pitfall in other situations but is inconsistent with the predicate given.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-DA-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-DA-Assoc exam.