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| Section | Weight | Objectives |
|---|---|---|
| Models | 25% | - Sources and references - Writing and managing SQL models - Materializations (table, view, ephemeral, incremental) - Snapshots - Seeds |
| Deployment and Orchestration | 15% | - Jobs and scheduling in dbt Cloud - Environments (dev, staging, prod) - Git version control integration - CI/CD with dbt Cloud |
| dbt Fundamentals | 15% | - dbt workflow and best practices - dbt project structure - dbt Core vs dbt Cloud |
| Testing and Documentation | 20% | - dbt docs and DAG visualization - Schema tests (unique, not_null, accepted_values, relationships) - Documentation generation - Custom data tests |
| Data Transformation Techniques | 25% | - Refactoring and incremental models - Jinja templating - Macros and packages - Common table expressions and subqueries |
>> dbt-Analytics-Engineering Reliable Test Topics <<
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NEW QUESTION # 204
A newly-hired analyst needs to update a downstream model that depends on a chain of intermediate models. To minimize the potential for disruption, how can modularity help them?
Answer: A
Explanation:
Modularity and well-defined dependencies make it easier to isolate the impact of a change, simplifying updates and testing.
NEW QUESTION # 205
(Multiple Select)
Answer: A,B
Explanation:
Pre-filtering at the source definition and incremental downstream models reduce processing. Timeouts and snapshots address different issues.
NEW QUESTION # 206 
Answer:
Explanation:
Explanation:
(branch)
(commit)
(pull request)
In dbt development workflows, version control using Git is essential for ensuring collaborative, safe, and trackable changes to analytics code. The correct first step when making updates-such as adding new models-is to create a new Git branch. This isolates development work from the production (main) branch, preventing incomplete or experimental logic from affecting deployed transformations. Branching supports dbt' s modular development approach and aligns with best practices for analytics engineering.
Once the branch is created, the developer modifies SQL models, tests, macros, or documentation as required.
To permanently record these modifications in Git, the developer must commit the changes. A commit serves as a snapshot of progress and creates an auditable history of transformations made to the project, enabling rollbacks, diffs, and peer review.
After development is complete, the developer submits a pull request (PR). The pull request triggers CI checks-often including dbt build, schema tests, and contract validations-to ensure code quality and identify impacts on downstream models. PRs allow team members to review and comment before changes merge into the main branch, enforcing governance, consistency, and reliability. This workflow embodies the engineering rigor dbt encourages: modular development, testing, versioning, and peer review.
NEW QUESTION # 207
Your team sometimes defines raw data sources directly in dbt models using .yml files within the models directory. When might this approach be problematic?
Answer: A
Explanation:
Defining raw sources within models leads to duplication and potential maintenance issues when multiple models share that dependency. A separate sources.yml file is generally preferred for shared datasets.
NEW QUESTION # 208
Which command materializes my_model and only its first-degree parent model(s) in the data platform?
Choose 1 option.
Answer: A
Explanation:
The correct answer is D: dbt run --select +1 my_model.
In dbt's selection syntax, the + operator is used to include parents (upstream) and/or children (downstream) of a given node. When used as +my_model or my_model+, it means "include all ancestors" or "all descendants" respectively, with no limit on depth.
To limit how many levels of parents or children are included, dbt allows a numeric depth between the + and the resource name. +1 my_model (or my_model+1) means:
* Select my_model
* Plus only its first-degree parents (direct dependencies), and no further ancestors.
Because you want to materialize the models, you must use dbt run, not dbt compile. dbt compile only compiles SQL and does not create or update relations in the data platform.
So:
* Option A (dbt run --select +my_model) would include all upstream ancestors, not just first-degree parents.
* Options B and E use compile, so they don't materialize.
* Option C is just syntactically wrong.
Therefore, the only command that both runs models and limits selection to my_model plus its first-degree parents is dbt run --select +1 my_model.
NEW QUESTION # 209
......
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