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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Analytics Engineering Foundations	- Modern data stack concepts (ELT vs ETL) 1. Warehouse-centric transformation workflows 2. Role of dbt in analytics engineering - SQL proficiency for analytics 1. Joins, aggregations, and window functions 2. Data modeling in SQL
Testing and Data Quality	- Data validation strategies 1. CI-based validation workflows 2. Schema testing practices - Built-in and custom tests 1. Custom SQL tests 2. Generic tests (unique, not null, relationships)
dbt Core Concepts	- Models and materializations 1. Views, tables, incremental models 2. Ref and source functions - Project structure and configuration 1. Packages and dependencies 2. dbt_project.yml configuration
Deployment and Orchestration	- Environments and workflows 1. Development vs production environments 2. Version control with Git - Running dbt in production 1. CI/CD integration patterns 2. dbt Cloud and job scheduling
Documentation and Lineage	- dbt documentation system 1. Auto-generated docs site 2. Model descriptions and metadata - Data lineage understanding 1. Dependency tracking with ref() 2. Directed acyclic graph (DAG)

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dbt Labs dbt-Analytics-Engineering test cram - dbt Analytics Engineering Certification Exam

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q328-Q333):

NEW QUESTION # 328
A newly-hired analyst needs to update a downstream model that depends on a chain of intermediate models. To minimize the potential for disruption, how can modularity help them?

A. With clear dependencies, they can pinpoint which upstream models to test.
B. Incremental materializations will help verify that the changes are successful.
C. Snapshots ensure that historical data won't be impacted unintentionally.
D. By using macros, modifications are limited to a single location.

Answer: A

Explanation:
Modularity and well-defined dependencies make it easier to isolate the impact of a change, simplifying updates and testing.

NEW QUESTION # 329
You run the command:
dbt test --select 'test_type:singular'
What will the command run?
Options shown:

A. furniture_customers_test
macro_stg_tpch_orders_assert_pos_price
macro_stg_tpch_suppliers_assert_pos_acct_bal
stg_tpch_orders_assert_positive_price
B. furniture_customers_test
C. furniture_customers_test
stg_tpch_orders_assert_positive_price
Choose 1 option.
D. macro_stg_tpch_orders_assert_pos_price
macro_stg_tpch_suppliers_assert_pos_acct_bal
stg_tpch_orders_assert_positive_price

Answer: D

Explanation:
In dbt, singular tests are custom SQL tests that live as standalone .sql files inside the root /tests directory, not inside /tests/generic. A singular test returns rows that indicate failure, and dbt runs the SQL directly as written. Generic tests, on the other hand, live inside the /tests/generic folder and are YAML-based macro- driven tests.
From your test folder structure (shown in the original screenshot), the following SQL files exist:
Inside /tests/generic/
* furniture_customers_test.sqlThis file is a generic test, not singular.
Inside the root /tests directory:
* macro_stg_tpch_orders_assert_pos_price.sql
* macro_stg_tpch_suppliers_assert_pos_acct_bal.sql
* stg_tpch_orders_assert_positive_price.sql
These are singular tests, because they are standalone SQL files in the tests root folder.
When you run:
dbt test --select 'test_type:singular'
dbt filters for only tests classified as singular. It ignores generic tests entirely.
Therefore, only the following 3 tests will run:
* macro_stg_tpch_orders_assert_pos_price
* macro_stg_tpch_suppliers_assert_pos_acct_bal
* stg_tpch_orders_assert_positive_price
This matches Option C, making it the correct answer.

NEW QUESTION # 330
A developer imports a package from a private repository called timeformat for use within their project.
Which statement is correct? Choose 1 option.
Options:

A. "The package can be added with this configuration in the packages.yml file:" packages:
- local: /opt/dbt/timeformat
B. "The package can be installed by running the command dbt build."
C. "The package default schema can be overridden in the dbt_project.yml file as:" models:
timeformat:
+schema: timeseries
D. "Including the package version/revision in the packages.yml file, for private git packages will result in an error."

Answer: C

Explanation:
The only correct statement is Option C, because dbt allows you to override the schema for a package's models directly within your project's dbt_project.yml file. Packages behave just like subfolders within your own project, and therefore the standard configuration pattern applies:
models:
timeformat:
+schema: timeseries
This correctly overrides the package's default schema, allowing all models within the timeformat package to be built into the timeseries schema.
Why the other options are incorrect
Option A
Local packages can be added using a path reference, but that is not what a private Git repository refers to. A private repo must be added using git: plus SSH/HTTPS URL, not local: unless the developer literally cloned the repo into a local directory. The question specifically describes a private repository, so this is not the correct statement.
Option B
Packages are installed using dbt deps, not dbt build.
dbt build only runs models, tests, and snapshots; it does not install dependencies.
Option D
Including a version or revision is allowed for private Git-based packages. You can specify branches, tags, or commit hashes safely in packages.yml.

NEW QUESTION # 331
You're experimenting with new transformations. You want to iterate on a model without affecting data in the production warehouse. Which dbt command should you use along with appropriate flags?

A. dbt compile to ensure changes are valid without executing models.
B. dbt test with -defer to temporarily bypass upstream dependencies.
C. dbt run with -target to specify a development environment.
D. dbt seed with -select to load data into production.

Answer: C

Explanation:
The -target flag allows you to isolate development work within different environments preventing accidental changes to production data.

NEW QUESTION # 332
After adding several new models to your project, you want to execute only those models and their direct dependencies. Which command combination best achieves this?

A. dbt run followed by dbt test
B. dbt run -exclude model namel model name2
C. dbt run -models +model namel +model name2 ...
D. dbt build

Answer: C

Explanation:
The + syntax with dbt run allows you to specify models and their upstream dependencies. Dbt build encompasses run and test along with snapshot execution.

NEW QUESTION # 333
......

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