Free Practice Questions for dbt Labs dbt-Analytics-Engineering Exam

Drops and recreates a definition of a query which is executed at the time it's referenced

✅ view

Inserts or updates records of an existing object in the warehouse using a delta

✅ incremental

Code belonging to the model is inserted into the places the model is referenced

✅ ephemeral

Drops and recreates an object in the warehouse as stored results when using dbt run

✅ table

The correct answer is C: Model groups can limit references by other models which aren’t in the same group.

According to the dbt documentation, dbt provides a formal access-control mechanism for models through model groups and the access property. Models can be assigned to groups, and each model can be marked as public, protected, or private. The purpose of this system is to create clear boundaries within a project, making it possible to control which models are allowed to reference others. Specifically, dbt enforces rules where models in a group can restrict references from models outside that group, which helps maintain modularity and prevents accidental coupling of unrelated data layers. This is extremely valuable in large analytics engineering projects where teams manage different domains or data products.

Option A is incorrect because dbt does not use Git user groups or any form of identity-based access control for model usage. Option B is incorrect because dbt does not integrate with warehouse roles for controlling model references—access is handled strictly within the dbt project’s metadata. Option D is incorrect because user emails or platform identities have no role in determining which models can reference others. dbt enforces usage rules only through its metadata-driven grouping and access configuration system.

The dbt compilation error explicitly states that the model fct_tasks depends on a node named stg_tasks, but dbt cannot find any resource with that name in the project. dbt resolves ref('stg_tasks') at compile time by searching for a model, seed, or source named stg_tasks. When no such model exists, dbt raises the exact error shown: “depends on a node named 'stg_tasks' which was not found.”

This error occurs before execution and is unrelated to whether the table exists in the warehouse. For dbt, the existence of a warehouse table is irrelevant during compilation—only the presence of a declared dbt resource matters. Therefore, option C (no table in the warehouse) cannot cause this error.

Option A is incorrect because ephemeral models still count as dbt models, and dbt can resolve ref() to them without problem.

Option D is wrong because defining a model in schema.yml is optional and unrelated to dbt’s ability to find the model. Tests and documentation require YAML entries, but model definitions do not.

Thus, the only correct explanation is that no model file named stg_tasks.sql exists under /models, making Option B the correct choice.

In dbt, a singular test is any SQL file placed directly inside the tests/ directory—not inside the tests/generic/ folder. Singular tests contain a custom SQL query where returning any rows means failure. They are distinct from generic tests, which are YAML-defined and live inside the generic directory, often referencing macros.

Looking at the folder structure:

In tests/generic/

furniture_customers_test.sql → This is a generic test, not a singular test.

In the root tests/ directory

macro_stg_tpch_orders_assert_pos_price.sql → singular test

macro_stg_tpch_suppliers_assert_pos_acct_bal.sql → singular test

stg_tpch_orders_assert_positive_price.sql → singular test

When you run:

dbt test --select 'test_type:singular'

dbt selects only singular tests, meaning tests that are individually written SQL files in the top-level tests/ directory.

Therefore, dbt will run:

macro_stg_tpch_orders_assert_pos_price

macro_stg_tpch_suppliers_assert_pos_acct_bal

stg_tpch_orders_assert_positive_price

It will not run furniture_customers_test, because that file is inside tests/generic/, which is reserved for template-based generic tests and is not classified as singular.

Thus, the correct answer is Option C.

In dbt, the Directed Acyclic Graph (DAG) is created from model dependencies, which dbt identifies only through two mechanisms: the ref() function for referencing dbt models and the source() function for referencing declared sources. These functions are explicitly designed to tell dbt how models relate to each other so that dbt can track lineage, enforce build order, and generate documentation.

Option A, {{ source('source', 'table') }}, correctly references a declared source. Any model using source() establishes an upstream dependency on that source table. dbt will therefore draw a lineage line in the DAG from the source to the model.

Option B, {{ ref('stg_jaffle_shop__customers') }}, is the canonical way to reference another dbt model. ref() ensures dbt determines dependencies at compile time, and thus creates a DAG edge between this model and its parent.

The remaining options do not create lineage:

C directly references a database object, which dbt cannot interpret as a dependency.

D uses a Jinja variable unrelated to lineage.

E is invalid, because source() requires two arguments: a source name and table name, not a dotted string.

Therefore, A and B are the only valid answers.

The correct answer is E: They are written using the {% macro %} wrapper.

Generic tests in dbt are implemented as macros, which means they must be defined using the {% macro %} Jinja syntax. These macros return a SQL query that evaluates to rows representing test failures. dbt executes these queries and marks the test as failed if any rows are returned. This requirement is explicitly documented: all generic tests are macros located in a test directory or within a package.

Option A is partially correct but not universal. Generic tests typically accept arguments such as model or column_name, but dbt does not require both or either. Custom tests can accept any parameters defined by the user. Option B is incorrect because tests do not require ref(); dbt injects the model relation for you when the test is executed. Option C is false because generic tests must be declared in YAML under a model or source for dbt to run them. Option D is incorrect because tests do not always need a column_name (e.g., relationship tests, row-count tests, table-level validations).

Therefore, the defining and universally required characteristic is that generic tests are written as macros, making E the correct answer.

Information Type

Retrieved From

Singular tests

C. .sql files

Column data types

A. Data platform information schema

Generic tests

B. .yml configuration

SQL code

C. .sql files

Column descriptions

B. .yml configuration

Model dependencies

C. .sql files

The dbt docs command compiles metadata about your project by gathering information from three primary sources: your warehouse’s information schema, your YAML configuration files, and your SQL model files. Understanding which metadata comes from which source is essential for debugging and for effective documentation practices.

Singular tests live inside .sql files within the /tests directory. Since dbt renders these tests directly from SQL files, their definitions appear in documentation sourced from that location.

Column data types come from the warehouse itself. dbt introspects the data platform information schema to retrieve actual types because dbt does not infer or define column types—only the warehouse does.

Generic tests (e.g., unique, not_null, accepted_values) are declared in .yml files. These YAML definitions contain test configurations, descriptions, and parameters, which dbt uses to document and execute these tests.

SQL code for models is naturally sourced from .sql files where the models are defined. This includes logic such as SELECT statements, CTEs, and transformations.

Column descriptions are written exclusively in .yml files. dbt never extracts descriptions from SQL comments—only from YAML.

Model dependencies come from the ref() and source() calls inside .sql model files, which dbt parses to build the DAG.

The correct answers are B: writing and using dbt macros and D: using dbt packages.

dbt strongly encourages DRY (Don't Repeat Yourself) principles, and two of the core mechanisms that support reusable logic are macros and packages. Macros allow you to write Jinja-powered reusable functions that can generate SQL statements dynamically, reducing duplication across models, tests, and project logic. Macros can encapsulate filters, joins, auditing logic, timestamps, and more—allowing developers to centralize logic in one place while referencing it across many models.

Packages extend this concept even further by allowing entire sets of macros, models, tests, and utilities to be imported into a project. Packages like dbt-utils contain widely used generic macros that help standardize transformations and testing. Using packages ensures consistent logic across teams and eliminates the need to rewrite common transformations.

Option A contradicts DRY principles because copy/pasting increases maintenance burden. Option C is not a mechanism for reusing logic; singular tests validate logic but do not reduce duplication. Option E simply changes a model’s materialization and does not support code reuse.

Thus, macros and packages are the only correct dbt mechanisms that provide reusable, modular, DRY logic.

The correct answer is A: Add model groups and set access to private.

dbt’s access control system is designed to restrict which models may reference others purely within the dbt project itself, independent of data warehouse permissions. This system uses model groups and an access property, which can be set to public, protected, or private. Setting a model to private means that only models within the same group are allowed to reference it. Any attempts to reference the model from outside the group will cause a compilation error, enforcing strong boundaries between domains or data products within a dbt project.

Option C (protected) is incorrect because protected allows references from other groups but requires explicit configuration—the intention is stewardship, not restriction. It does not fully restrict referencing.

Option B is incorrect because dbt model access control is not tied to warehouse permissions. Warehouse-level grants cannot control dependency references inside the dbt DAG.

Option D is irrelevant to access control. Versioning does not restrict references; it only manages updates to published models.

Therefore, the only correct way to restrict which models can reference another model is by placing it into a model group and marking it as private, which enforces strict internal usage boundaries.