Databricks-DE-Assoc Implementing CI/CD Practice Question
A team is implementing a CI/CD process for their Delta Live Tables (DLT) pipelines. Which THREE of the following practices are recommended to ensure reliable deployment?
⚠ Common exam trap
Candidates often assume that manual testing in the UI is sufficient, overlooking the requirement for automated, repeatable tests and declarative infrastructure definitions that characterize robust CI/CD pipelines.
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
✓
Use Git branches to manage feature development and production code.
Reliable DLT deployments depend on version control, automated testing, and environment abstraction. By defining DLT pipelines as code via DABs or Terraform, teams ensure consistent environments. Testing code before deployment prevents runtime errors, and using Git-based workflows allows for code reviews, which are essential for maintaining high-quality pipelines. These practices collectively ensure that DLT pipeline changes are predictable, verifiable, and safe to deploy into production environments without disrupting existing operations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Keep all DLT configuration values hardcoded within the pipeline notebook.
Why it's wrong here
Hardcoding configuration prevents the reuse of pipelines across different environments, such as development and production. It forces developers to modify the core logic just to change settings, violating the principle of configuration-as-code and increasing the likelihood of introducing bugs during simple environment-specific adjustments.
- ✓
Use Git branches to manage feature development and production code.
Why this is correct
Branching allows developers to isolate their changes and perform testing without affecting the stable production version. Merging through pull requests ensures that all code changes undergo peer review, which is a critical gatekeeping mechanism for maintaining pipeline stability and preventing unauthorized or faulty code deployments.
- ✓
Implement automated tests that run against the pipeline before production deployment.
Why this is correct
Automated testing verifies that the DLT pipeline logic functions correctly before it is promoted to production. This approach catches integration issues early in the pipeline development cycle, reducing the risk of production failures and ensuring that data quality expectations are met consistently every time a new version is deployed.
- ✗
Manually update the pipeline source code in the production workspace.
Why it's wrong here
Manual updates create a 'black box' environment where changes are not tracked in version control. This approach lacks an audit trail, prevents rollback capabilities, and makes it impossible to guarantee that the production environment is in the expected state, significantly increasing the risk of operational failures.
- ✓
Define pipeline infrastructure using declarative files like JSON or YAML.
Why this is correct
Declarative files provide a 'source of truth' for the pipeline's infrastructure. By versioning these files, teams can track the evolution of their pipeline's configuration over time. This infrastructure-as-code pattern simplifies the deployment process by enabling automated tools to apply the exact desired state to any target Databricks workspace.
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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.