Model naming conventions in dbt - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding dbt Model Naming Patterns

In dbt, model names often follow specific patterns to indicate their purpose. Which of the following model names best represents a staging model for raw customer data?

A int_customers

B fct_customers

C dim_customers

D stg_customers_raw

💡 Hint

Staging models usually start with stg_ and represent raw or lightly transformed data.

In dbt, stg_ prefix is used for staging models that prepare raw data for further transformations. int_ is for intermediate models, dim_ for dimension tables, and fct_ for fact tables.

❓ Predict Output

intermediate

Output of dbt Model Naming Convention Check

Given the following list of dbt model names, how many are correctly following the common naming conventions for dimension models?

models = ['dim_users', 'fct_sales', 'dim_products', 'stg_orders', 'dim_customers']

dbt

models = ['dim_users', 'fct_sales', 'dim_products', 'stg_orders', 'dim_customers']
dim_models = [m for m in models if m.startswith('dim_')]
print(len(dim_models))

A 1

B 2

C 3

D 4

💡 Hint

Count how many model names start with dim_.

Models starting with dim_ are dimension models. Here, 'dim_users', 'dim_products', and 'dim_customers' match, totaling 3.

❓ data_output

advanced

Resulting DataFrame from Model Naming Filter

Consider a pandas DataFrame listing dbt models and their types. Which rows remain after filtering for models with names starting with fct_?

dbt

import pandas as pd
models_df = pd.DataFrame({
  'model_name': ['fct_sales', 'dim_customers', 'stg_orders', 'fct_revenue'],
  'description': ['sales facts', 'customer dimensions', 'order staging', 'revenue facts']
})
fact_models = models_df[models_df['model_name'].str.startswith('fct_')]
fact_models

A [{'model_name': 'fct_sales', 'description': 'sales facts'}, {'model_name': 'fct_revenue', 'description': 'revenue facts'}]

B [{'model_name': 'dim_customers', 'description': 'customer dimensions'}]

C Empty DataFrame

D [{'model_name': 'stg_orders', 'description': 'order staging'}]

💡 Hint

Filter rows where model_name starts with fct_.

Only 'fct_sales' and 'fct_revenue' start with 'fct_', so only those rows remain after filtering.

🔧 Debug

advanced

Identify the Error in Model Naming Convention Code

What error does the following Python code produce when checking if model names start with stg_?

models = ['stg_orders', 'dim_customers', 'stg_payments']
filtered = [m for m in models if m.startswith('stg_') == True]
print(filtered)

A No error; outputs ['stg_orders', 'stg_payments']

B TypeError: 'bool' object is not callable

C SyntaxError: invalid syntax

D Outputs an empty list []

💡 Hint

Check if startswith returns a boolean and how it is used.

The code correctly filters model names starting with 'stg_'. The comparison to True is redundant but valid. The output is the list of matching models.

🚀 Application

expert

Choosing the Correct Model Name for a New Intermediate Model

You are creating a new intermediate model in dbt that combines staging data for orders and payments. Which model name best follows dbt naming conventions?

A stg_orders_payments

B int_orders_payments

C fct_orders_payments

D dim_orders_payments

💡 Hint

Intermediate models often use the prefix int_.

The int_ prefix is used for intermediate models that combine or transform staging data before final models. stg_ is for raw staging, fct_ for fact tables, and dim_ for dimension tables.