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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q72-Q77):

NEW QUESTION # 72
After configuring a dbt package, you notice its models depend on a source table absent from your warehouse. How do you typically address this?

A. File an issue or submit a pull request to the package's repository.
B. Override the package model's SQL query to reference an existing table in your warehouse.
C. Rewrite the package's model from scratch within your project to remove the dependency.
D. Update your source definitions to include the required table.

Answer: B

Explanation:
Start by fulfilling the package's dependencies. If the table is truly generic, then customizing the SQL (B) might be the next step.

NEW QUESTION # 73
A model depends heavily on geospatial calculations, which are computationally expensive. What optimization strategy might be worth exploring?

A. Convert the model to a snapshot to leverage historical data more effectively.
B. Pre-calculate common geospatial results as an incremental model.
C. Investigate whether the database offers specialized geospatial indexes or functions.
D. Ensure the underlying data adheres to DRY principles.

Answer: C

Explanation:
Optimized database functionality is crucial for complex calculations. While option A could be beneficial, it's important to first utilize database-level optimizations.

NEW QUESTION # 74
You introduce a custom macro into your project. This is a snippet of your dbt_project.yml:YAML

A. The macro_paths configuration in your dbt_project.yml might be incorrect or missing.
B. The .sql extension on the file within the macros directory is incorrect.
C. You must place the macros directory within the root of your dbt project.
D. The macro's name should only be 'trim', without the 'string_utils' prefix.

Answer: A

Explanation:
Check your macro file paths, as an incorrect configuration would prevent dbt from finding the macro. The others are likely not the issue but worth double-checking.

NEW QUESTION # 75
You've made several commits to your feature branch. Before merging into the main branch, you want to consolidate the changes into a single, clear commit. What Git technique would help achieve this?

A. Run git rebase main followed by git commit -amend.
B. Manually copy your files into a new branch, commit, and issue a pull request.
C. Use git rebase -i (interactive rebase) to squash commits together.
D. git cherry-pick specific commits from your branch and reapply them.

Answer: C

Explanation:
Interactive rebasing (git rebase -i) provides fine-grained control to squash, edit, or rearrange commits, creating a cleaner commit history

NEW QUESTION # 76
A developer has updated multiple models in their dbt project, materialized as tables and views.
They want to run and test all models upstream and downstream from the modified models that are materialized as views.
What command will achieve this? Choose 1 option.

A. dbt build --select +state:modified +materialized:view+
B. dbt build --select +state:modified, config.materialized:view+
C. dbt build --select @state:modified+, @config.materialized:view+
D. dbt build --select +state:modified+
E. dbt build --select +state:modified, +config.materialized:view+

Answer: E

Explanation:
The requirement is:
* Select all models that have been modified # this uses the state selector:state:modified
* But only those modified models that are materialized as views # this uses the config selector:config.
materialized:view
* Then include upstream and downstream dependencies of those models # this requires adding + around the selectors.
To combine both selectors, dbt uses a comma-separated list within --select, meaning both selectors must match simultaneously.
The correct syntax is:
dbt build --select +state:modified, +config.materialized:view+
This command:
* Finds models whose state is modified.
* Filters them to only those materialized as views.
* Includes upstream (+) and downstream (+) dependencies.
* Runs and tests the selected nodes because dbt build includes running models, tests, seeds, and snapshots.
Why the other options are wrong:
* A and C use invalid selector syntax (config.materialized:view+ must be prefixed with +).
* B only selects modified models but does not filter by materialization type.
* D uses materialized:view, which is invalid without the config. prefix.
Thus, only Option E satisfies all conditions and uses correct dbt selector syntax.

NEW QUESTION # 77
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