Databricks-DA-Assoc Managing Data Practice Question
A data analyst manages a Delta table gold.orders that is updated by an upstream job every 15 minutes. Analysts run long-running dashboard queries against the table and occasionally see stale results, even seconds after an update. The analyst wants dashboard queries to reflect the latest committed data without restarting the SQL warehouse. Which action should the analyst take?
⚠ Common exam trap
The trap here is attributing stale query results to caching or file layout when the real cause is session-level snapshot reuse of the Delta table version.
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
✓
Configure the session to use a fresh snapshot by disabling snapshot reuse, for example by setting the appropriate Spark configuration for the session.
Snapshot reuse within a Spark or Databricks SQL session caches the table's state for performance, which can cause readers to miss recent commits. Disabling snapshot reuse forces the session to resolve the current Delta version on each query, so dashboards reflect the latest committed data. Cache tuning and OPTIMIZE address performance, not snapshot freshness, and converting formats sacrifices Delta guarantees.
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 gold.orders after every upstream commit so the table is compacted and readers pick up new files.
Why it's wrong here
OPTIMIZE compacts small files and can improve read performance, but it does not change how a reader session resolves the table's current version. A session reusing a stale snapshot will still see old data even after compaction. Scheduling OPTIMIZE is a maintenance practice, not a mechanism for forcing readers to observe the latest commit.
- ✗
Enable the Delta cache on the SQL warehouse and increase the cache size so recent files stay resident.
Why it's wrong here
The Delta cache accelerates repeated reads of the same Parquet files but does not automatically invalidate entries when the table is updated. Enabling or enlarging the cache can actually prolong staleness because cached file data may be reused. The staleness stems from snapshot reuse, not from insufficient cache capacity, so this does not address the root cause.
- ✗
Convert the table to a Parquet table so queries always read the latest files directly from cloud storage.
Why it's wrong here
Delta Lake provides ACID transactions and versioning; converting to plain Parquet removes those guarantees and does not inherently fix session-level snapshot reuse. Reading Parquet directly can also expose partially written files during concurrent updates. This changes the storage format rather than the reader's snapshot behavior, so it does not resolve the staleness.
- ✓
Configure the session to use a fresh snapshot by disabling snapshot reuse, for example by setting the appropriate Spark configuration for the session.
Why this is correct
Spark and Databricks SQL can reuse a cached table snapshot within a session for performance. When the underlying Delta table changes, a session that reuses the snapshot may return stale data. Disabling snapshot reuse forces each query to resolve the current table version, so dashboards see the latest committed data without restarting the warehouse.
Visual reference
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-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.