Databricks-DE-Assoc Data Transformation and Modeling Practice Question
Which TWO of the following statements accurately describe the behavior of Delta Lake table constraints and enforcement?
⚠ Common exam trap
Candidates often assume that Delta table constraints are only enforced during read operations or background maintenance tasks, rather than actively rejecting invalid writes.
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
✓
CHECK constraints are enforced during write operations for both batch and streaming jobs.
Delta Lake supports NOT NULL constraints and CHECK constraints to enforce data quality at the write layer. These constraints are vital for maintaining schema integrity. Understanding their behavior is essential for engineers designing robust data pipelines, as they ensure that invalid data is rejected before it is committed to the transaction log, maintaining a single source of truth.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Delta Lake automatically enforces NOT NULL constraints on all columns by default.
Why it's wrong here
Delta Lake does not enforce NOT NULL constraints by default. To enforce these, a user must explicitly add the constraint to the table schema. If a user tries to insert a NULL value into a column that has not been explicitly defined as NOT NULL, the operation succeeds.
- ✓
CHECK constraints are enforced during write operations for both batch and streaming jobs.
Why this is correct
Delta Lake enforces CHECK constraints at the write time. Whenever a user or pipeline attempts to write data, the constraint is validated against the incoming records. If any row violates the condition, the write operation will fail, preventing the data from ever being committed to the table.
- ✗
Adding a CHECK constraint to a table will retroactively validate all existing data in that table.
Why it's wrong here
When a CHECK constraint is added, it only validates new data written to the table. Existing data that violates the constraint is not automatically flagged or cleaned; the constraint acts as a gatekeeper for future writes only, which is an important consideration for maintaining historical data lineage.
- ✓
Data engineers can define constraints using standard SQL during table creation or with ALTER TABLE statements.
Why this is correct
Delta Lake supports standard SQL syntax for defining constraints. This allows data engineers to integrate quality checks directly into their DDL scripts, ensuring that governance and business logic are baked into the table structure consistently across different environments and data engineering teams within the Databricks workspace.
- ✗
Constraints can only be defined on numeric data types within a Delta table.
Why it's wrong here
Constraints in Delta Lake are not restricted to numeric types; they can be applied to string, date, and other supported data types. For instance, a CHECK constraint can validate that a string column contains specific patterns or that a date column falls within a specific valid range.
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-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-DE-Assoc exam.