Databricks-DE-Pro Developing Code (Python/SQL) Practice Question
Exhibit
Error: Py4JJavaError: An error occurred while calling o68.save. : org.apache.spark.sql.AnalysisException: Cannot write incompatible data to table 'sales_data': Column 'price' (decimal(10,2)) cannot be cast to 'price' (decimal(8,2)).
Refer to the exhibit. You are appending data to an existing Delta table. What is the most likely cause of this error, and how should you resolve it?
⚠ Common exam trap
Candidates often assume the error is due to a missing column. They overlook that Delta Lake enforces strict schema types and precision, and incoming data must match the defined target schema.
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 incoming data's 'price' column has a higher precision than the table schema.
This error occurs because of a data type mismatch between the incoming DataFrame and the existing table schema, specifically a change in precision or scale. Databricks enforces schema safety to prevent data corruption. Resolving this requires either casting the incoming data to match the target schema or using the 'mergeSchema' option if the goal is to allow evolution, provided the change is safe and intended for the application.
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 is locked by another process.
Why it's wrong here
Locking issues manifest as concurrency errors or timeouts, not as an AnalysisException related to data type incompatibility. The error clearly indicates a schema validation failure, which happens during the initial write process, confirming that the issue is related to the data structure, not the concurrency state of the table.
- ✓
The incoming data's 'price' column has a higher precision than the table schema.
Why this is correct
The error explicitly states an incompatibility between the two decimal types. Appending data requires the incoming schema to be compatible with the target. If the incoming 'price' requires more precision than the existing column allows, the append operation is rejected to preserve the integrity of the existing data stored.
- ✗
The table schema is corrupted and needs to be repaired.
Why it's wrong here
Schema corruption errors are much rarer and typically involve the transaction log itself. This error is a clear semantic mismatch between the source and target schema definitions. The system is functioning correctly by preventing the insertion of data that does not fit the defined schema of the target table.
- ✗
The user does not have write access to the table.
Why it's wrong here
Insufficient permissions would result in an AccessDenied or Authorization error. The error message is a technical validation error from the Spark SQL engine, indicating that the schema enforcement process has identified an issue with the data being submitted, which is completely separate from the user's authorization level.
Visual reference
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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.