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| Section | Objectives |
|---|
| Topic 1: dbt Core Concepts | - Models and materializations
- 1. Views, tables, incremental models
- 2. Ref and source functions
- Project structure and configuration
- 1. dbt_project.yml configuration
- 2. Packages and dependencies
|
| Topic 2: Documentation and Lineage | - Data lineage understanding
- 1. Directed acyclic graph (DAG)
- 2. Dependency tracking with ref()
- dbt documentation system
- 1. Model descriptions and metadata
- 2. Auto-generated docs site
|
| Topic 3: 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
|
| Topic 4: Deployment and Orchestration | - Environments and workflows
- 1. Version control with Git
- 2. Development vs production environments
- Running dbt in production
- 1. dbt Cloud and job scheduling
- 2. CI/CD integration patterns
|
| 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. Role of dbt in analytics engineering
- 2. Warehouse-centric transformation workflows
|
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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q83-Q88):
NEW QUESTION # 83
Which explanation describes how dbt infers dependencies between models?
Choose 1 option.
- A. .yml configurations for sources and refs are parsed for dependency information.
- B. Information is gathered from the use of source and ref macros.
- C. The underlying SQL code is parsed and relationships are created from explicit table references.
- D. All source and ref macros are resolved to database objects and dbt queries them for dependencies.
Answer: B
Explanation:
The correct answer is A: Information is gathered from the use of source and ref macros.
dbt determines the dependency graph - the DAG - by analyzing calls to ref() and source() inside model SQL files. These macros explicitly declare relationships between models. When a developer writes ref ('orders'), dbt interprets this as: "the current model depends on the orders model." Similarly, source() indicates dependencies on upstream raw data sources. This declarative approach allows dbt to build a structured and deterministic DAG without scanning SQL for implicit table references.
Option B is incorrect because dbt does not query database objects to infer dependencies; it resolves dependencies at compile time through metadata generated from model files. Option C is incorrect because dbt intentionally does not parse SQL to detect table names-this would be brittle and error-prone across warehouses. Instead, dbt requires explicit references to maintain reliability. Option D is incorrect because YAML files define metadata about models and sources but do not create dependency relationships between them.
Thus, the dependency graph is built exclusively by reading ref() and source() macro calls, which ensures clarity, correctness, and maintainability within the analytics engineering workflow.
NEW QUESTION # 84
Which of the following scenarios might warrant a git rebase instead of a standard git merge when integrating your changes into the main branch?
- A. You need to resolve complex merge conflicts that arise during a standard merge.
- B. Your branch has diverged significantly from the main branch with unrelated changes.
- C. You want to avoid a merge commit and keep a linear project history.
- D. You only want to include a subset of your feature branch commits in the main branch.
Answer: B,C
Explanation:
A Rebasing creates a more linear history, making it easier to trace changes over time. D. Rebasing allows for a cleaner integration when a feature branch contains intermingled changes that shouldn't all be merged.
NEW QUESTION # 85
A pull request includes changes to both dbt models and configuration files in your dbt_project.yml file. During the merge, conflicts arise in the configuration file. How would you approach resolving these conflicts?
- A. Ask the author of the pull request to rebase their changes and resolve the configuration conflict in their branch.
- B. Utilize Git's merge conflict markers to carefully combine changes in the configuration file.
- C. Give priority to the model changes and manually edit the configuration file after.
- D. Request a separate, smaller pull request dedicated to the configuration changes to isolate the conflict.
Answer: A,B,D
Explanation:
B: Git conflict markers are essential to identify and carefully resolve the conflict. C: Asking the PR author to resolve the conflict keeps changes consolidated on their branch. D: Isolating configuration changes can simplify conflict resolution, especially if they involve complex adjustments.
NEW QUESTION # 86
You receive a compilation error stating:Model depends on a node named 'ref data' which was not found
- A. Check the configuration of the ref_data data source.
- B. Review downstream models that might be referencing ref_data incorrectly.
- C. Search for a model named ref_data within your project or any included packages.
- D. Examine the SQL within the model where the error occurs for typos or incorrect ref() usage.
Answer: C
Explanation:
Compilation errors often mean missing dependencies. Start by ensuring ref_data actually exists as a model.
NEW QUESTION # 87
Type Mismatch
- A. Handle the type conversion with a custom macro and apply it selectively within your dbt project.
- B. Modify the source table directly in the database to adjust the data type, ensuring backward compatibility.
- C. Use a CAST operation within the affected model's SQL, leaving the underlying source unchanged.
- D. Introduce a staging model specifically to perform the type conversion, creating a clean separation.
Answer: D
Explanation:
A staging model offers the most control and flexibility. Direct source modification risks disrupting other systems. In-model CASTs work but clutter the core logic. Macros are powerful but add complexity.
NEW QUESTION # 88
......
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