Databricks-DE-Pro Data Sharing and Federation Practice Question
A data engineer is using Lakehouse Federation to query a PostgreSQL database. The engineer notices that a query filtering on a column with a high cardinality is performing poorly, even though the remote database has an index on that column. What is the most likely reason for the poor performance?
⚠ Common exam trap
The trap here is blaming the remote database's statistics or configuration, when the issue is that the filter expression prevents predicate pushdown from Databricks to PostgreSQL.
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 filter condition uses a function or expression that cannot be pushed down to PostgreSQL, causing a full table scan.
Poor performance on a filtered query against a federated PostgreSQL database often indicates that the filter was not pushed down. If the filter uses an expression that PostgreSQL cannot evaluate, Databricks retrieves all data and filters locally, bypassing the remote index. The engineer should simplify the filter or use pushdown-compatible expressions to leverage the index and improve performance.
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 PostgreSQL database is not configured with the correct statistics, causing the query planner to choose a sequential scan.
Why it's wrong here
While outdated statistics can lead to poor query plans on the PostgreSQL side, the issue here is likely on the Databricks side. If the filter is pushed down, PostgreSQL's planner would use the index if statistics are up to date. But if the filter is not pushed down, PostgreSQL never sees the filter, so its planner is irrelevant. The scenario implies that the filter is not being pushed down, so statistics are not the primary cause.
- ✗
The foreign catalog is using a JDBC connection with a small fetch size, causing many round trips to the database.
Why it's wrong here
A small fetch size can degrade performance by increasing network round trips, but it would affect all queries, not just those with high-cardinality filters. The scenario specifically mentions a filter on a high-cardinality column, which points to a pushdown issue. Fetch size is a tuning parameter that can be adjusted, but it is not the most likely cause of poor performance for this particular query pattern.
- ✗
The foreign catalog is not configured to allow predicate pushdown for the PostgreSQL database.
Why it's wrong here
Predicate pushdown is enabled by default for PostgreSQL in Lakehouse Federation. While it is possible to disable it, the default configuration supports pushdown. If pushdown were disabled, the query would fetch all data and filter locally, causing poor performance. However, the scenario states that the remote database has an index, which suggests that pushdown might be working but not effectively. Therefore, this is not the most likely cause.
- ✓
The filter condition uses a function or expression that cannot be pushed down to PostgreSQL, causing a full table scan.
Why this is correct
Lakehouse Federation pushes down simple predicates like equality and range filters. However, if the filter uses a function or expression that PostgreSQL cannot evaluate, such as a complex UDF or a non-deterministic function, the pushdown fails. Databricks then retrieves all rows and applies the filter locally, ignoring the remote index. This results in a full table scan and poor performance. The engineer should rewrite the query to use pushdown-compatible expressions.
About these practice questions
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JA
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-DE-Pro 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-DE-Pro exam.