dbt Analytics Engineering Certification dbt Labs Practice Exam

About the Exam

Prepare for the dbt Analytics Engineering Certification certification from dbt Labs with 63 practice questions, community discussion, and detailed explanations.

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Last updated November 3, 2026 at 4:41 PM

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QuestionQ1

Which two statements are true about version controlling code using Git? Choose 2 options.

Choose two

Explanation

Git preserves a history of committed project snapshots, allowing developers to inspect recorded changes and restore code from an earlier commit. However, Git does not automatically record every edit: changes must be staged and committed to become part of the history. (git-scm.com)

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QuestionQ2

Consider the following DAG:

app_data.detail_categories -&gt; stg_detail_categories -&gt; skills_with_details app_data.details -&gt; stg_details -&gt; lessons_with_details

What would help make this DAG more modular? Choose 1 option.

Explanation

Examining lessons_with_details and skills_with_details can identify complex or reusable transformations that could be separated into an intermediate model. This can make the downstream models easier to read, maintain, and test without assuming that the staging models should be combined. (docs.getdbt.com)

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QuestionQ3

Consider this dbt_project.yml:

name: "jaffle_shop"  
version: "1.0.0"  
config-version: 2  
profile: "snowflake"  
model-paths: ["models"]  
macro-paths: ["macros"]  
snapshot-paths: ["snapshots"]  
target-path: "target"  
clean-targets:  
  - "logs"  
  - "target"  
  - "dbt_modules"  
  - "dbt_packages"  
models:  
  jaffle_shop:  
    orders:  
      materialized: table

When you execute dbt run, your models build as views rather than tables:

19:36:14 Found 1 model, 0 tests, 0 snapshots, 0 analyses, 179 macros, 0 operations, 0 seed files, 0 sources, 0 exposures, 0 metrics  
19:36:16 Concurrency: 1 threads (target='default')  
19:36:17 Finished running 1 view model in 3.35s.  
19:36:17 Completed successfully  
19:36:17 Done. PASS=1 WARN=0 ERROR=0 SKIP=0 TOTAL=1

Could the following be a root cause of the model not being materialized as a table?

The target-path is incorrectly configured.

Explanation

The target-path controls where dbt writes artifacts; it does not determine whether a model is built as a table or a view. Materialization is controlled by model configuration, and models default to views when no applicable table configuration takes effect. (docs.getdbt.com)

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QuestionQ4

A developer imports a package named timeformat from a private repository for use in their project.

Which statement is correct? Choose 1 option.

Explanation

The root project can override a package’s model configuration in dbt_project.yml. Setting models: timeformat: +schema: timeseries configures the custom schema for models in the timeformat package. By default, dbt combines the target schema with the custom schema name. (docs.getdbt.com)

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QuestionQ5

You wrote the following test against your fct_orders model to verify that a parent model applied status filters properly:

\{\{  
config(  
enabled=true, severity='error'  
)  
\}\}  
select *  
from \{\{ ref('fct_orders') \}\}  
where status_code = 13

Which statement about this test is correct?

Explanation

A singular dbt data test returns records that violate its assertion. Here, the query returns rows with status_code = 13, so those rows cause the test to fail under the default failure threshold. The configured severity reports the failure as an error. (docs.getdbt.com)

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Add data tests to your DAG

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QuestionQ6

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QuestionQ15

Examine the model stg_customers_sales in the main branch:

select id as customer_id, name as customer_name from \{\{ source('my_data','my_source') \}\}

A developer creates a branch, feature_a, from main and modifies the model to:

select id as customer_id, name as customer_name, country as customer_country from \{\{ source('my_data','my_source') \}\}

A second developer also creates a branch, feature_b, from main and modifies the model to:

select id as customer_id, name as customer_name, address as customer_address from \{\{ source('my_data','my_source') \}\}

The first developer creates a PR and merges feature_a into main. The second developer then creates a PR and attempts to merge feature_b into main.

How will Git combine the code from feature_b with the code in main, which now also contains the changes from feature_a?

Statement:

“Because feature_a has already been approved and merged into main, the code for the model stg_customers_sales will remain as-is, and the changes from feature_b will not be added.”

Explanation

Both branches make different changes to the same line of the model, so a default Git merge produces a conflict that must be resolved. The earlier merge does not automatically cause Git to discard the later branch’s changes; the resolution can include both columns. (git-scm.com)

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