PDE Ingesting and Processing the Data Practice Question
A company wants to use dbt (data build tool) to transform data in BigQuery. They have a Cloud Storage bucket containing raw CSV files that are loaded daily into BigQuery via an external table. Which dbt feature should they use to modularize the transformation logic and handle dependencies between models?
⚠ Common exam trap
Candidates often confuse the purpose of dbt components: models with `ref()` manage transformation logic and dependencies, while tests handle data quality, snapshots track historical changes, and seeds load static data.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
dbt models with ref()
C is correct because dbt models with the `ref()` function allow you to modularize SQL transformation logic and automatically handle dependencies between models. When you use `ref('model_name')`, dbt builds a dependency graph, ensuring models are executed in the correct order based on their references. This is essential for transforming raw data from an external table into a structured, analytics-ready dataset in BigQuery.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
dbt tests
Why it's wrong here
Tests assert data quality expectations on existing models; they do not modularise transformation logic or manage dependencies. Models with ref() build the DAG. Tests are the correct choice when you need to validate uniqueness, not-null constraints, or accepted values before downstream consumption.
- ✗
dbt snapshots
Why it's wrong here
Snapshots capture slowly changing dimension history by recording row-level changes over time; they do not modularise transformations or wire dependencies. Models with ref() provide that. Snapshots are correct when you must preserve historical versions of mutable records, such as customer address changes.
- ✓
dbt models with ref()
Why this is correct
The ref() function creates a directed acyclic graph between dbt models, letting each model reference upstream ones by name rather than hard-coded table paths. This satisfies the modularisation and dependency-handling requirement, since dbt resolves build order automatically and materialises each model against the BigQuery external table.
- ✗
dbt seeds
Why it's wrong here
Seeds load static CSV files into the warehouse as tables; they do not modularise transformation logic or resolve dependencies. That role belongs to dbt models, which use ref() to build a dependency DAG. Seeds suit small, slowly changing lookup data such as country codes or mapping tables.
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