DBT Interview Questions

Welcome to Data Build Tool Interview Questions! The DBT has gained massive traction in the data engineering and analysis segment with commendable features. It is an open-source platform to help data engineers transform, model, and analyze data.

The demand for skilled DBT professionals is rising, and it is vital to understand concepts that are asked in any DBT interview. In this blog, we will discuss all the essential questions of DBT, along with detailed answers. Without wasting too much time, let's start our preparation.

Data Build Tool Interview Questions

1) What is a Data Build Tool (DBT)?

The Data build tool is an open-source platform designed to transform data warehouse data with SQL select statements. Using DBT, you can perform tasks like data transformation, modeling, testing & validation, etc. DBT simplifies the data engineer's tasks and prepares the data for analysis and reporting.

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2) What are the DBT uses?

DBT is widely used for performing the following tasks:

  • Data Transformation
  • Data Modeling
  • Schedule Jobs
  • Write test cases
  • Data Preparation for Analysis & Reporting
  • To build & Manage Pipelines

3) What is the difference between DBT & other ETL tools?

Most ETL platforms perform data transformation outside the data warehouse, whereas DBT performs data transformations within the data warehouse and is highly suitable for ELT workflows. Unlike other ETL tools, it offers version control and validation for data models. 

4) What is Jinja DB

Jinja is a templating language, and in DBT, we combine SQL with Jinja. Jinja offers a programmatic environment with more capabilities that are impossible alone with SQL.

5) What can you do with DBT Jinja?

In DBT, Jinja will allow developers to: 

  • Use Control Structures
  • Implement environmental variables in DBT projects
  • Convert snippet SQL into reusable macros, etc.

6) What are the DBT custom Jinja blocks?

Following are the custom Jinja blocks:

  • Docs blocks, 
  • Snapshots
  • Materializations

7)  How would you run DBT Commands?

To run DBT commands, you can select a path.

8) What is the best practice to execute bulk configure models?

It should be done at the directory level when performing bulk configurations, which is far simpler.

9) What is Meant by the Project in DBT?

A DBT project is a directory consisting of all the components needed to deal with data within DBT. A typical DBT project contains ingredients like project name, YAML configuration file,  data sources & destinations, transformation requirements, SQL queries, SQL templates, Snapshots, etc.

10) What is a YAML in DBT?

YAML stands for Yet Another Markup Language, a popular data serialization language. It is used in DBT to write configuration files. YAML is mandatory for every DBT project.

11) What do you know about DBT Models?

In DBT, a model is a logic used for data transformation or sometimes an intermediate step in the data transformation process. When you hit DBT “run,” you are running a model to transform data as per the specifications.

Generally, developers spend their time working with models that can be modified and scalable to maximize efficiency and meet growing requirements. 

12) What are Snapshots in DBT?

Snapshots are one of the core features available in dbt that help access historical data. This feature is handy while working with mutable tables in DBT and provides a Snapshot mechanism. DBT Snapshot records changes that occurred to a mutable table over some time.

13) Define Seeds Data Build Tool

Seeds are nothing but CSV files that are stored in a DBT project. The seed files can be loaded to a data warehouse using the dbt seed command.

14) What do you know about Data tests in DBT?

The DBT Test command helps you test your sources, seeds, and snapshots and give you the results.

15) Explain the usefulness of  Macros in DBT.

Macros are part of Jinja and are very helpful in eliminating the need to write the same code across DBT models. Macros are a code block that can be reusable across multiple projects and stored as a .sql file in the macros directory.

16) What is the use of Job Scheduler in DBT?

The Job Scheduler is the critical component for running DBT jobs in the cloud and simplifies the process of building data pipelines. It can handle both event-based and CORN-based schedules.

17) What are the APIs available in the DBT cloud?

Following are the DBT API’s:

  •  DBT Cloud Administrative API
  •  DBT Semantic Layer APIs
  •  DBT Cloud Discovery API

18) How do you Access the DBT APIs?

Following are the ways you can use to access DBT APIs:

  • Through User tokens
  • Through Service account tokens.

19) What is the Difference between DBT Cloud CLI & Cloud IDE?

The DBT cloud CLI allows developers to write code from a local command line interface or code environment. In contrast, DBT cloud IDE allows users to develop DBT projects directly in the browser.

20) How do you handle performance problems in DBT?

IN DBT, you will get the option called logs and diagnostics, which helps you identify and troubleshoot issues.

By Tech Solidity

Last updated on February 12, 2024