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| Section | Objectives |
|---|---|
| Topic 1: Deployment and Orchestration | - Running dbt in production
|
| Topic 2: Documentation and Lineage | - dbt documentation system
|
| Topic 3: Analytics Engineering Foundations | - Modern data stack concepts (ELT vs ETL)
|
| Topic 4: dbt Core Concepts | - Models and materializations
|
| Topic 5: Testing and Data Quality | - Built-in and custom tests
|
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NEW QUESTION # 356
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: D
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 # 357
you're working with a dbt project where a central model fct_orders is refactored. Several downstream models rely on this fact table. Which of the following is the MOST efficient way to determine which downstream models might be affected by this change?
Answer: D
Explanation:
While options B, D, and E can indirectly hint at broken models, they're not the most efficient methods. The dbt docs commands allow you to directly visualize and explore dependencies within your dbt project, making it the ideal choice for this scenario.
NEW QUESTION # 358
While onboarding new data sources to your dbt project, you discover some have unexpectedly high null rates in key columns. How would you integrate tests for this into your dbt workflow?
Answer: B,D
Explanation:
B ensures data entering dbt is checked at the first opportunity. C pushes quality checks as far upstream as possible- A is valid, but less integrated if sources are managed outside dbt's purview D masks the problem rather than addresses it.
NEW QUESTION # 359
Ignoring indentation, arrange these YAML code snippets in the correct order to generate descriptions on the source, table, and column:
Answer:
Explanation:
Explanation:
Here is the correct sequence:
Correct Order
* sources:
* - name: blue (source definition)
* tables:
* - name: yellow (table definition)
* columns:
* - name: red (column definition + tests)
In dbt, source documentation follows a strict YAML hierarchy. At the top level, the sources: key declares that you are defining source metadata. Inside this block, each source is listed as an item beginning with - name:.
Therefore, the snippet describing the source (blue) must come immediately after sources:.
Next, dbt requires a tables: block inside each source to define the raw tables belonging to that source. Thus, the snippet containing tables: must follow the source definition. The table description (yellow) is then placed directly under this key because it represents a table within that source.
Finally, dbt allows column-level metadata under a columns: block inside a table definition. Therefore, columns: appears next, followed by the snippet defining a specific column (red) which includes both a description and tests (unique, not_null).
Arranging the YAML in this hierarchical order ensures dbt can correctly assign documentation to the source object, the table object, and the individual column. Misordering these snippets would break the structure and prevent dbt from generating documentation properly.
NEW QUESTION # 360
You have a complex dbt model that is essential for production reporting. To minimize the risk of breaking this model during development, what strategy might you employ?
Answer: C,D
Explanation:
A provides stability for the critical part of production- C offers a staging-like area within production. B is good, but doesn't prevent the breakage upstream. D isnt generally how dbt models operate-
NEW QUESTION # 361
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