Databricks-DA-Assoc Understanding the Databricks Platform Practice Question
A data analyst is working in a notebook and notices that the query results are inconsistent compared to an earlier run, despite no code changes. What is the most likely cause?
⚠ Common exam trap
Candidates often blame the notebook environment or caching, ignoring that concurrent writes to the underlying data source are the most frequent cause of inconsistent results.
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 underlying data files are being modified by a concurrent process.
Inconsistent results often stem from the underlying data being updated concurrently without proper versioning or isolation. Databricks provides ACID transactions via Delta Lake. If the analyst is querying raw files instead of Delta tables, or if the table is being updated by another process, the results might vary. Understanding how Delta Lake handles snapshot isolation is crucial for ensuring that analytical reports remain consistent and reproducible over time.
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 notebook is not using a SQL Warehouse.
Why it's wrong here
While SQL Warehouses provide optimized query execution, they are not the primary reason for data inconsistency. Consistency is a property of the storage layer. Whether you use a notebook or a warehouse, if the underlying data files are not managed with Delta Lake, queries will reflect the current state of files.
- ✓
The underlying data files are being modified by a concurrent process.
Why this is correct
If the data is not stored as a Delta table, or if the process bypasses transaction logs, concurrent writes will cause inconsistent reads. Delta Lake solves this by providing snapshot isolation, which guarantees that once a query starts, it reads a consistent version of the data, regardless of concurrent modifications.
- ✗
The cluster has automatically terminated and restarted.
Why it's wrong here
Cluster termination affects compute availability, but it does not change the content of the data files themselves. Restarting a cluster does not cause data inconsistency; the data remains in object storage. If results change after a restart, it indicates that the underlying data has changed, not the compute environment.
- ✗
The notebook needs more memory to process the dataset.
Why it's wrong here
Memory constraints would lead to performance degradation or out-of-memory errors, not inconsistent query results. If a query succeeds, the result should be deterministic unless the underlying data is shifting. Insufficient memory will crash the process, making it an availability issue rather than a data integrity or consistency problem.
About these practice questions
Courseiva writes every Databricks-DA-Assoc question from scratch — 291 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.