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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: dbt Core Concepts	- Project structure and configuration 1. dbt_project.yml configuration 2. Packages and dependencies - Models and materializations 1. Views, tables, incremental models 2. Ref and source functions
Topic 2: Testing and Data Quality	- Data validation strategies 1. CI-based validation workflows 2. Schema testing practices - Built-in and custom tests 1. Custom SQL tests 2. Generic tests (unique, not null, relationships)
Topic 3: Documentation and Lineage	- Data lineage understanding 1. Directed acyclic graph (DAG) 2. Dependency tracking with ref() - dbt documentation system 1. Auto-generated docs site 2. Model descriptions and metadata
Topic 4: Deployment and Orchestration	- Running dbt in production 1. CI/CD integration patterns 2. dbt Cloud and job scheduling - Environments and workflows 1. Development vs production environments 2. Version control with Git
Topic 5: Analytics Engineering Foundations	- SQL proficiency for analytics 1. Joins, aggregations, and window functions 2. Data modeling in SQL - Modern data stack concepts (ELT vs ETL) 1. Warehouse-centric transformation workflows 2. Role of dbt in analytics engineering

>> Hot dbt-Analytics-Engineering Questions <<

dbt-Analytics-Engineering Valid Exam Guide - dbt-Analytics-Engineering Valid Test Test

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q259-Q264):

NEW QUESTION # 259
Which of the following scenarios would typically warrant the use of production data in a development environment?

A. Testing changes to a critical business reporting model for accuracy before deployment.
B. Training new members of your team on writing SQL and basic dbt models.
C. Debugging a logic error that only manifests itself under the exact conditions of real-world production data.
D. Development environments should generally avoid using real production data to minimize risks.

Answer: D

Explanation:
While A and C might occasionally have valid use cases for production data in development, it usually should be avoided. Consider these for special circumstances:A Ideally, realistic test data or anonymized production samples should be used. C: Production data snapshots or masked/anonymized versions might be necessary in rare situations.

NEW QUESTION # 260
You've created a macro to display upstream dependencies differently from downstream dependencies in the DAG. However, after re-generating the documentation, the visualization doesn't seem to have changed. What should you investigate?

A. Ensure the macro is named correctly and located in the appropriate directory (macros folder) of your dbt project.
B. The built-in DAG functionality might have limitations preventing this type of customization.
C. Update the cache expiration settings of your web browser.
D. Verify that your web browser supports the CSS and JavaScript used for custom DAG styling.

Answer: A,B,D

Explanation:
B: dbt needs to be able to find your macro for it to be used. C: Browser compatibility can sometimes impact the rendering of custom visualizations. D: It's possible there are constraints on how far you can modify the built-in DAG.

NEW QUESTION # 261
You notice a pattern where warnings are generated for several models due to missing descriptions. How could you configure dbt_project.yml to fail these models instead when descriptions are absent?

A. Introduce a custom macro to validate model descriptions_
B. Globally change the default warning behavior using the warn-if: configuration
C. Add on-run-start and on-run-end hooks to perform custom checks
D. Set a project-level severity setting under the tests: section.

Answer: B

Explanation:
The warn-if: configuration lets you escalate specific warnings to errors at the project level.

NEW QUESTION # 262
Which two configurations can be applied to a dbt test?
Choose 2 options.

A. persist_docs
B. materialized
C. on_schema_change
D. enabled
E. tags

Answer: D,E

Explanation:
The correct answers are B: tags and C: enabled.
dbt tests-both generic and singular-support a limited but clearly defined set of configurations. Two of the supported configurations are tags, which allow grouping and selecting tests using dbt's selector syntax, and enabled, which can be set to true or false to control whether a test should run. These configurations are commonly used to manage large test suites, such as disabling slow tests in development or tagging tests for CI pipelines.
Option A (on_schema_change) is a model-only configuration and cannot be applied to tests.
Option D (materialized) applies only to models, not tests, since tests compile into queries that evaluate failures rather than physical objects.
Option E (persist_docs) applies to models and sources for documentation purposes. Tests do not support persisted documentation properties beyond what appears in test metadata.
dbt's documentation makes clear that tests inherit only a small subset of configurations compared to models- primarily enabled, tags, severity, and custom test arguments. Among the options provided, only tags and enabled are valid and supported.
Thus, the correct answers are B and C.

NEW QUESTION # 263
Your dbt_project.yml contains the following: YAML

A. No error: This is a valid configuration-
B. Runtime Error: Incorrectly specifying materialization within a nested YAML structure-
C. Compilation Error: Using "+" to indicate nested configuration is incorrect in -yml files.
D. Compilation Error: Invalid indentation causing a YAML parsing issue.

Answer: D

Explanation:
YAML relies on precise indentation. Materialization should be at the same level as the model name, not nested further.

NEW QUESTION # 264
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