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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Testing and Data Quality	- Built-in and custom tests 1. Generic tests (unique, not null, relationships) 2. Custom SQL tests - Data validation strategies 1. Schema testing practices 2. CI-based validation workflows
Deployment and Orchestration	- Environments and workflows 1. Development vs production environments 2. Version control with Git - Running dbt in production 1. dbt Cloud and job scheduling 2. CI/CD integration patterns
Analytics Engineering Foundations	- SQL proficiency for analytics 1. Data modeling in SQL 2. Joins, aggregations, and window functions - Modern data stack concepts (ELT vs ETL) 1. Role of dbt in analytics engineering 2. Warehouse-centric transformation workflows
Documentation and Lineage	- dbt documentation system 1. Model descriptions and metadata 2. Auto-generated docs site - Data lineage understanding 1. Directed acyclic graph (DAG) 2. Dependency tracking with ref()
dbt Core Concepts	- Models and materializations 1. Ref and source functions 2. Views, tables, incremental models - Project structure and configuration 1. dbt_project.yml configuration 2. Packages and dependencies

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q354-Q359):

NEW QUESTION # 354
A model passes its tests during development, but later fails when deployed on a less powerful production database. Which of the following might be the reason for this discrepancy?

A. The test was accidentally disabled in the production dbt project.
B. Source data distributions differ significantly between environments.
C. There's a bug in the version of dbt that runs in production.
D. Performance-related test thresholds are too lenient in development.

Answer: B,D

Explanation:
A and B could lead to tests passing in a forgiving environment but not catching issues in a more resource- constrained one. C would likely be caught earlier, and D is less likely

NEW QUESTION # 355
(Multiple Select)

A. Availability of specific dbt packages used in your project.
B. Credentials used to authenticate to the data warehouse.
C. The specific SQL dialect used when generating model code.
D. The target database schema where models are materialized.

Answer: B,C,D

Explanation:
These often differ between development, test, and production environments. D, while relevant, is project- wide, not environment-specific.

NEW QUESTION # 356
A project is migrating from a staging environment to a production environment within the same data warehouse. However, the production data has more strict access controls. Which approaches could you take to manage this transition?

A. All of the above.
B. Store connection credentials as environment variables and reference them within the profiles.yml using Jinja templating.
C. Use separate profiles.yml files for staging and production, each with their respective credentials.
D. Utilize a secrets management solution and integrate it into your dbt deployment process.

Answer: A

Explanation:
A: Separate profiles allow storing environment-specific credentials. B: Environment variables keep sensitive data out of source control and allow for dynamic configuration of the profiles. yml file. C: Secrets managers offer more robust security for sensitive credentials.

NEW QUESTION # 357
You have an urgent hotfix that needs to be deployed while a larger feature branch is still under review. Which strategies could you consider?

A. Merge the hotfix into the main branch and then rebase the feature branch on top of the new main branch.
B. Temporarily revert the feature branch changes, deploy the hotfix, and then restore the feature branch.
C. Merge the hotfix into the feature branch and wait for the entire feature to be approved.
D. Create a dedicated branch from the main branch specifically for the hotfix.

Answer: A,D

Explanation:
B: A dedicated hotfix branch isolates changes and provides the quickest path to deployment. D: Rebasing the feature branch after the hotfix ensures it includes the necessary fix.

NEW QUESTION # 358
You get the following error when compiling a model:ln 'models/marts/sales_report.sql':

A. Delete the existing model and create a new one with the materialization set to table.
B. Examine your model's SQL and ensure you've defined a column or combination of columns to serve as a unique identifier.
C. Add a config.get("unique_key") entry within your model configuration in dbt_project.yml.
D. Introduce a unit test to assert that the model's output has no duplicate rows.

Answer: B

Explanation:
Incremental models must have a unique key You must address this in the model's logic itself. Changing the materialization or testing wont solve the root cause.

NEW QUESTION # 359
......

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