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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Testing and Documentation | 20% | - Schema tests (unique, not_null, accepted_values, relationships) - dbt docs and DAG visualization - Custom data tests - Documentation generation |
| Topic 2: dbt Fundamentals | 15% | - dbt project structure - dbt Core vs dbt Cloud - dbt workflow and best practices |
| Topic 3: Data Transformation Techniques | 25% | - Jinja templating - Refactoring and incremental models - Macros and packages - Common table expressions and subqueries |
| Topic 4: Deployment and Orchestration | 15% | - CI/CD with dbt Cloud - Environments (dev, staging, prod) - Git version control integration - Jobs and scheduling in dbt Cloud |
| Topic 5: Models | 25% | - Sources and references - Materializations (table, view, ephemeral, incremental) - Snapshots - Writing and managing SQL models - Seeds |
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NEW QUESTION # 12
You discover that dbt models run significantly slower in your production environment than in development. What potential causes should you investigate?
Answer: A,C,D
Explanation:
Each of these directly affects performance and could differ significantly between environments. C is relevant, but less likely the sole cause if development is performant.
NEW QUESTION # 13
You're working in a highly collaborative environment. How could you manage contributions and updates to complex lineage macros to ensure maintainability and prevent conflicts?
Answer: B
Explanation:
A Version control is essential for tracking changes and collaboratiom B: Style guides and reviews improve code quality and reduce errors. C: Controlled access can be important, while still allowing for collaboration through review processes.
NEW QUESTION # 14
You have just executed dbt run on this model:
select * from {{ source("{{ env_var('input') }}", 'table_name') }}
and received this error:
Compilation Error in model my_model
expected token ':', got '}'
line 14
{{ source({{ env_var('input') }}, 'table_name') }}
How can you debug this?
Answer: C
Explanation:
This error is caused by invalid Jinja syntax, specifically by nesting {{ }} blocks inside another Jinja expression. The expression:
{{ source("{{ env_var('input') }}", 'table_name') }}
compiles to:
{{ source({{ env_var('input') }}, 'table_name') }}
Here, Jinja sees {{ inside another {{ ... }} block. Jinja does not allow nested print statements like this; instead, functions should be called directly inside a single pair of curly braces. The parser encounters an unexpected } where it expects part of a valid expression (hence "expected token ':', got '}'"), which is a classic symptom of mismatched or nested curly braces.
The correct usage is:
select * from {{ source(env_var('input'), 'table_name') }}
In this form, env_var('input') is evaluated first, and its result is passed as the first argument to source() within one Jinja expression.
Option C is therefore the correct debugging approach: inspect your Jinja and look for incorrectly nested curly brackets. Options A and D are generic and don't address the root cause, while B talks about quoting in SQL, which is not the problem-the error arises before SQL compilation, at Jinja parse time.
NEW QUESTION # 15
You've included some custom visualizations within your dbt project documentation (e.g., network graphs) but notice they don't render correctly when the site is generated. What might be the cause?
Answer: A,C
Explanation:
A: Custom visualizations often rely on external libraries that need to be included in the generated HTML for them to function. B: Some visualizations might require specific settings in your dbt_project.yml or model files for correct rendering.
NEW QUESTION # 16
Your stakeholders require robust data visualization and dashboarding capabilities beyond what dbt itself offers. Which of the following would be good pairings with dbt to address this need?
Answer: C
Explanation:
Modern Bl tools specialize in taking transformed data from dbt and creating rich, interactive visualizations and dashboards for end-users. The other options have their places in the data stack, but not for primary dashboarding.
NEW QUESTION # 17
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