dbt Analytics Engineering Certification Preparation Details
This dbt Analytics Engineering Certification study guide covers every exam domain and objective with official dbt documentation links. Use it to prepare for the 65-question, two-hour dbt Core 1.11 exam efficiently and confidently. You can also explore more dbt certification study guides on the dbt Certification category to keep building your skills.
dbt Analytics Engineering Certification Materials
01 Developing and optimizing dbt models
Identifying and verifying any raw object dependencies
Understanding core dbt materializations
Materializations best practices
Conceptualizing modularity and how to incorporate DRY principles
How we structure our dbt projects
Using commands such as build, run, test, docs, show, snapshot, and seed
Creating a logical flow of models and building clean DAGs
How we structure our dbt projects
Defining configurations in dbt_project.yml
Using dbt Packages
Creating Python Models
Providing access to users to models with the grants config
Creating snapshots in YAML
Selecting the optimal incremental strategy based on a dataset’s characteristics
About microbatch incremental models
Materializations best practices
Validating model logic and schema definitions in dry-runs using the –empty flag
Running models in sample mode using the –sample flag
Understanding advanced dbt materializations such as microbatch
About microbatch incremental models
02 Managing dbt models governance
Adding contracts to models to ensure the shape of models
Creating different versions of our models and deprecating the old ones
Defining constraints in YAML to enforce data integrity at the platform level
03 Debugging data modeling errors
Understanding logged error messages
How can I see the SQL that dbt is running?
Troubleshooting using compiled code
How can I see the SQL that dbt is running?
Troubleshooting .yml compilation errors
Developing and implementing a fix and testing it prior to merging
Managing dbt behavior with flags
04 Troubleshooting and optimizing dbt pipelines
Troubleshooting and managing failure points in the DAG
severity, error_if, and warn_if
Using dbt clone
Clone incremental models as the first step of your CI job
05 Implementing dbt tests
Using generic, singular, custom, custom generic, and unit tests on a wide variety of models and sources
Writing custom generic data tests
Testing assumptions for dbt models and sources
severity, error_if, and warn_if
Implementing various testing steps in the workflow
06 Implementing and maintaining external dependencies
Implementing dbt exposures
Implementing source freshness
07 Leveraging the dbt state
Understanding state and state selection
Using dbt retry
Wrapping Up dbt Analytics Engineering Certification
Working through every objective in this dbt Analytics Engineering Certification study guide, alongside hands-on practice in a real dbt project, prepares you for all seven exam domains. Stay consistent and good luck on exam day. You can also explore more dbt certification study guides on the dbt Certification category to keep building your skills. Have a question or tip? Leave a comment below.
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