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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Testing and Documentation	20%	- Schema tests (unique, not_null, accepted_values, relationships) - Custom data tests - dbt docs and DAG visualization - Documentation generation
Topic 2: Data Transformation Techniques	25%	- Macros and packages - Common table expressions and subqueries - Jinja templating - Refactoring and incremental models
Topic 3: Deployment and Orchestration	15%	- Environments (dev, staging, prod) - CI/CD with dbt Cloud - Jobs and scheduling in dbt Cloud - Git version control integration
Topic 4: Models	25%	- Materializations (table, view, ephemeral, incremental) - Sources and references - Writing and managing SQL models - Seeds - Snapshots
Topic 5: dbt Fundamentals	15%	- dbt project structure - dbt Core vs dbt Cloud - dbt workflow and best practices

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q106-Q111):

NEW QUESTION # 106
Which is true about writing generic tests?
Choose 1 option.

A. They must contain a {{ ref() }} snippet to a model.
B. They do not need to be specified under a model's YAML configurations.
C. They are written using the {% macro %} wrapper.
D. They should accept one or both of these arguments: model and column_name.
E. They should always accept a column_name.

Answer: C

Explanation:
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.

NEW QUESTION # 107
Collaborative Fix

A. Add clear commit messages that concisely summarize the purpose of the fix.
B. Open a pull request with detailed comments explaining the rationale for the changes and any potential side-effects.
C. Include updated model documentation (dbt docs) outlining the logic and reasons for adjustment.
D. All of the above.

Answer: D

Explanation:
Changes need context! I-Jse comments, updated docs, and informative commit messages.

NEW QUESTION # 108
You've introduced a new series of dbt models and want to run them in isolation to verify results before integrating them into the main build process. Which dbt command options and flags are likely to be useful?

A. dbt test --select source name to validate the new models' source data.
B. dbt run -s to target models with a specific tag
C. dbt build -exclude model_name to temporarily skip certain models.
D. dbt run -m +model_path to explicitly select a model by its file path.

Answer: B,D

Explanation:
A and C provide precise selection mechanisms. B is for exclusion, not focused execution- D is about tests, not model runs-

NEW QUESTION # 109
When merging code on a remote repository hosting platform (like GitHub), you notice options like "squash and merge" and "rebase and merge." Which factors would influence your choice between these options?

A. The size and complexity of the changes within the pull request.
B. Your team's established conventions and preferences for maintaining the project history.
C. Whether you want to preserve the feature branch commit history in detail.
D. All of the above.

Answer: D

Explanation:
A: Squash and merge simplifies history; rebase and merge preserve details. B: Large pull requests might benefit from squashing for a cleaner main branch view. C: Teams should have consistent ways to manage history for ease of collaboration.

NEW QUESTION # 110
Consider these SQL and YAML files for the model model_a:
models/staging/model_a.sql
{{ config(
materialized = "view"
) }}
with customers as (
...
)
dbt_project.yml
models:
my_new_project:
+materialized: table
staging:
+materialized: ephemeral
Which is true about model_a? Choose 1 option.
Options:

A. Select statements made from the database on top of model_a will be quicker, but the data will only be as up to date as the last dbt run.
(Note: A and D are duplicates - typical exam formatting.)
B. Select statements made from the database on top of model_a will be slower, but the data will always be up to date.
C. Select statements made from the database on top of model_a will result in an error.
D. Select statements made from the database on top of model_a and transformation processing within model_a will be quicker, but the data will only be as up to date as the last dbt run.

Answer: B

Explanation:
model_a contains an in-model config explicitly setting:
{{ config(materialized = "view") }}
In dbt, in-model config overrides project-level config, including folder-level defaults. Even though the staging/ folder is configured as:
staging:
+materialized: ephemeral
...this does not apply because the in-file config has higher precedence.
So model_a is materialized as a view.
A view in dbt is not persisted as data; instead, it stores the SQL definition. When a downstream query selects from that view, the database executes the underlying SQL at query time. As a result:
* Queries run slower than selecting from a table, because the underlying computation happens each time.
* But the data is always up to date, because views read directly from the current underlying tables.
This matches Option C.
Why the other options are wrong
* A & D describe table behavior (faster queries, data only as fresh as last run). model_a is not materialized as a table.
* B is incorrect because views are fully queryable; no error occurs.

NEW QUESTION # 111
......

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