dbt Analytics Engineering Certification
Exam Pre Questions Already Solved
Correct 2025-2026 Update.
with Exposures you can - Answer run, test, and list resources that feed into your exposure
populate a dedicated page in the auto-generated documentation site with context relevant to
data consumers
Variables can be used to - Answer configure timezones, avoid hardcoding table names
vars can be scoped globally, or to a specific package imported in your project. - Answer name:
my_dbt_project
version: 1.0.0
config-version: 2
vars:
# The `start_date` variable will be accessible in all resources
start_date: '2016-06-01'
# The `platforms` variable is only accessible to resources in the my_dbt_project project
my_dbt_project:
platforms: ['web', 'mobile']
# The `app_ids` variable is only accessible to resources in the snowplow package
snowplow:
app_ids: ['marketing', 'app', 'landing-page']
models: ...
partial parsing - Answer In dbt Cloud, partial parsing of a project can lead to issues. If you find
that your dbt project is not compiling to the values you have set, deleting the target/partial
parse.msgpack file in your project can help. Doing so will force dbt to recompile your entire
project and may help resolve any issues caused by partial parsing.
just like SQL models, there are three ways to configure Python models: - Answer In
dbt_project.yml, where you can configure many models at once
,To enable tracking of hard deletes, the "invalidate_hard_deletes" option should be turned -
Answer ON in the dbt configuration file. This is done by setting "invalidate_hard_deletes:
true".
This will cause dbt to mark rows as invalid if they are deleted from the source query.
Include as many columns as possible in the snapshot (True or False) - Answer True. It is
recommended to include as many columns as possible in the snapshot, even if they do not
seem useful at the moment, as snapshots cannot be recreated. Reference:
https://docs.getdbt.com/docs/build/snapshots
Filter - Answer Some databases can have tables where a filter over certain columns are
required, in order prevent a full scan of the table, which could be costly. In order to do a
freshness check on such tables a filter argument can be added to the configuration, e.g. filter:
_etl_loaded_at >= date_sub(current_date(), interval 1 day). For the example above, the
resulting query would look like
- Answer By default, dbt will not quote the database, schema, or identifier for the source
tables that you've specified.
To force dbt to quote one of these values, use the quoting property.
https://docs.getdbt.com/docs/build/sources
version: 2
sources:
- name: jaffle_shop
database: raw
quoting:
database: true
schema: true
identifier: true
tables:
- name: order_items
- name: orders
# This overrides the `jaffle_shop` quoting config
, What are the arguments passed to the model() function when running a dbt project with dbt
run --select python_model? - Answer When running a dbt project with dbt run --select
python_model, dbt will prepare and pass in both arguments (dbt and session) to the model()
function.
- dbt: A class compiled by abt Core, unique to each model, enables you to run your Python code
in the context of your dbt project and DAG.
- session: A class representing your data platform's connection to the Python backend. The
session is needed to read in tables as DataFrames, and to write DataFrames back to tables. In
PySpark, by convention, the SparkSession is named spark, and available globally. For consistency
across platforms, we always pass it into the model function as an explicit argument called
session.
The order of precedence for variable declaration is as follows (highest priority first): - Answer
1. The variables defined on the command line with --vars.
1. The package-scoped variable declaration in the dbt_project.yml file
3. The global variable declaration in the dbt_project.yml file.
4. The variable's default argument (if one is provided).
The dbt source freshness command will output: - Answer 1. a pass/warning/error status for
each table selected in the freshness snapshot.
2. dbt will write the freshness results to a file in the target/ directory called sources.json by
default.
3. You can also override this destination, use the -o flag to the dbt source freshness command.
E.g dbt source freshness -o custom output directory/freshness report.json
What types of Incremental strategies does dbt Python models support? - Answer Incremental
dbt Python models support all the same incremental strategies as their SQL counterparts.
The specific strategies(merge or insert_overwrite) supported depend on the adapter or data
platform used.
Reference: https://docs.getdbt.com/docs/build/python-models
The Python model in dbt has the capability to incorporate additional functions: - Answer
either through importing external functions or by defining its own. This allows for the creation
of non-dbt functions within the same Python model file for use in the model. However, it's
currently not possible to import and reuse Python functions defined in one dbt model in other
Exam Pre Questions Already Solved
Correct 2025-2026 Update.
with Exposures you can - Answer run, test, and list resources that feed into your exposure
populate a dedicated page in the auto-generated documentation site with context relevant to
data consumers
Variables can be used to - Answer configure timezones, avoid hardcoding table names
vars can be scoped globally, or to a specific package imported in your project. - Answer name:
my_dbt_project
version: 1.0.0
config-version: 2
vars:
# The `start_date` variable will be accessible in all resources
start_date: '2016-06-01'
# The `platforms` variable is only accessible to resources in the my_dbt_project project
my_dbt_project:
platforms: ['web', 'mobile']
# The `app_ids` variable is only accessible to resources in the snowplow package
snowplow:
app_ids: ['marketing', 'app', 'landing-page']
models: ...
partial parsing - Answer In dbt Cloud, partial parsing of a project can lead to issues. If you find
that your dbt project is not compiling to the values you have set, deleting the target/partial
parse.msgpack file in your project can help. Doing so will force dbt to recompile your entire
project and may help resolve any issues caused by partial parsing.
just like SQL models, there are three ways to configure Python models: - Answer In
dbt_project.yml, where you can configure many models at once
,To enable tracking of hard deletes, the "invalidate_hard_deletes" option should be turned -
Answer ON in the dbt configuration file. This is done by setting "invalidate_hard_deletes:
true".
This will cause dbt to mark rows as invalid if they are deleted from the source query.
Include as many columns as possible in the snapshot (True or False) - Answer True. It is
recommended to include as many columns as possible in the snapshot, even if they do not
seem useful at the moment, as snapshots cannot be recreated. Reference:
https://docs.getdbt.com/docs/build/snapshots
Filter - Answer Some databases can have tables where a filter over certain columns are
required, in order prevent a full scan of the table, which could be costly. In order to do a
freshness check on such tables a filter argument can be added to the configuration, e.g. filter:
_etl_loaded_at >= date_sub(current_date(), interval 1 day). For the example above, the
resulting query would look like
- Answer By default, dbt will not quote the database, schema, or identifier for the source
tables that you've specified.
To force dbt to quote one of these values, use the quoting property.
https://docs.getdbt.com/docs/build/sources
version: 2
sources:
- name: jaffle_shop
database: raw
quoting:
database: true
schema: true
identifier: true
tables:
- name: order_items
- name: orders
# This overrides the `jaffle_shop` quoting config
, What are the arguments passed to the model() function when running a dbt project with dbt
run --select python_model? - Answer When running a dbt project with dbt run --select
python_model, dbt will prepare and pass in both arguments (dbt and session) to the model()
function.
- dbt: A class compiled by abt Core, unique to each model, enables you to run your Python code
in the context of your dbt project and DAG.
- session: A class representing your data platform's connection to the Python backend. The
session is needed to read in tables as DataFrames, and to write DataFrames back to tables. In
PySpark, by convention, the SparkSession is named spark, and available globally. For consistency
across platforms, we always pass it into the model function as an explicit argument called
session.
The order of precedence for variable declaration is as follows (highest priority first): - Answer
1. The variables defined on the command line with --vars.
1. The package-scoped variable declaration in the dbt_project.yml file
3. The global variable declaration in the dbt_project.yml file.
4. The variable's default argument (if one is provided).
The dbt source freshness command will output: - Answer 1. a pass/warning/error status for
each table selected in the freshness snapshot.
2. dbt will write the freshness results to a file in the target/ directory called sources.json by
default.
3. You can also override this destination, use the -o flag to the dbt source freshness command.
E.g dbt source freshness -o custom output directory/freshness report.json
What types of Incremental strategies does dbt Python models support? - Answer Incremental
dbt Python models support all the same incremental strategies as their SQL counterparts.
The specific strategies(merge or insert_overwrite) supported depend on the adapter or data
platform used.
Reference: https://docs.getdbt.com/docs/build/python-models
The Python model in dbt has the capability to incorporate additional functions: - Answer
either through importing external functions or by defining its own. This allows for the creation
of non-dbt functions within the same Python model file for use in the model. However, it's
currently not possible to import and reuse Python functions defined in one dbt model in other
