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[Hardware] Test dbt-Analytics-Engineering Cram & dbt-Analytics-Engineering Valid Test C

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【Hardware】 Test dbt-Analytics-Engineering Cram & dbt-Analytics-Engineering Valid Test C

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q47-Q52):NEW QUESTION # 47
A developer has updated multiple models in their dbt project, materialized as tables and views.
They want to run and test all models upstream and downstream from the modified models that are materialized as views.
What command will achieve this? Choose 1 option.
  • A. dbt build --select +state:modified, +config.materialized:view+
  • B. dbt build --select @state:modified+, @config.materialized:view+
  • C. dbt build --select +state:modified +materialized:view+
  • D. dbt build --select +state:modified+
  • E. dbt build --select +state:modified, config.materialized:view+
Answer: A
Explanation:
The requirement is:
* Select all models that have been modified # this uses the state selector:state:modified
* But only those modified models that are materialized as views # this uses the config selector:config.
materialized:view
* Then include upstream and downstream dependencies of those models # this requires adding + around the selectors.
To combine both selectors, dbt uses a comma-separated list within --select, meaning both selectors must match simultaneously.
The correct syntax is:
dbt build --select +state:modified, +config.materialized:view+
This command:
* Finds models whose state is modified.
* Filters them to only those materialized as views.
* Includes upstream (+) and downstream (+) dependencies.
* Runs and tests the selected nodes because dbt build includes running models, tests, seeds, and snapshots.
Why the other options are wrong:
* A and C use invalid selector syntax (config.materialized:view+ must be prefixed with +).
* B only selects modified models but does not filter by materialization type.
* D uses materialized:view, which is invalid without the config. prefix.
Thus, only Option E satisfies all conditions and uses correct dbt selector syntax.

NEW QUESTION # 48
You are working with git to version control the dbt logic.
Order these steps to add or modify transformations to your dbt project.

Answer:
Explanation:

* Create a new branch in git and switch to itB. Update the dbt code according to the new logic
C. Create a Pull / Merge RequestD. Run some automated CI checks and/or manual review of the updated codeE. Merge the updated code to the main git branch
* Version control best practices in dbt follow the same engineering workflow used in modern software development. The first step is always to create a new git branch and switch into it, ensuring all work is isolated from production-ready code and allowing your team to develop safely without affecting others.
Once inside the feature branch, you update the dbt code according to the new logic, which may include modifying models, tests, macros, or documentation.
* Next, you create a Pull Request (PR), also known as a Merge Request, to propose integrating your changes into the main branch. This is important because dbt projects are collaborative, and PRs facilitate peer review, enforce project standards, and prevent regressions. Once the PR is created, automated CI pipelines-such as running dbt build, schema tests, data quality checks, and code-style checks-are executed. Reviewers may also manually inspect code for logic correctness, naming conventions, and modeling consistency.
* After all checks have passed and reviewers approve the PR, the final step is to merge the updated code into the main branch, making the new transformations part of the production dbt project. This workflow ensures governance, reliability, and auditable development practices, all of which are core principles in analytics engineering.

NEW QUESTION # 49
13. An analyst on your team has informed you that the business logic creating the is_active column of your stg_users model is incorrect.
You update the column logic to:
case
when state = 'Active'
then true
else false
end as is_active
Which test can you add on the state column to support your expectations of the source data? Choose 1 option.
  • A. - name: state
    tests:
    - accepted_values:
    values: ['active', 'churned', 'trial']
    - not_null
  • B. - name: is_active
    tests:
    - accepted_values:
    values: ['active', 'churned', 'trial']
    - not_null
  • C. - name: state
    tests:
    - not_null
    - unique
  • D. - name: is_active
    tests:
    - not_null
    - unique
Answer: A
Explanation:
The purpose of this question is to determine how to validate that the input values in the state column support the business logic that determines the is_active field. Since the logic checks whether state = 'Active', it is critical that the state column only contains values that the business process expects. In the example shown, acceptable states appear to be: 'active', 'churned', and 'trial'.
The correct way to enforce this expectation is to apply an accepted_values test on the state column. This ensures that any unexpected state (e.g., 'inactive', 'pending', 'deleted', or NULL) will cause the test to fail, alerting the team that the upstream system is producing unexpected or invalid values. Additionally, adding not_null ensures every user record contains a valid state.
Option A is the only configuration that applies the accepted values test to the correct column (state) and reflects the expected domain of values.
Options B and D incorrectly apply tests to the derived column is_active, not the source column that needs validation. Option C only checks nullability and uniqueness, which does not validate the range of allowed values and thus does not protect the business logic.
Therefore, Option A is the only correct answer.

NEW QUESTION # 50
Match the macro to the appropriate hook so that the correct execution steps comply with these rules:
* macro_1() needs to be executed after every dbt run.
* macro_2() needs to be executed after a model runs.
* macro_3() needs to execute before every dbt run.
* macro_4() needs to be executed before a model runs.

Answer:
Explanation:

Explanation:
Hook 1
on-run-end: "{{ macro_x() }}"
The Answer:
macro_1
Hook 2
models:
<my_dbt_project>:
post-hook: "{{ macro_x() }}"
The Answer:
macro_2
Hook 3
on-run-start: "{{ macro_x() }}"
The Answer:
macro_3
Hook 4
{{
config(
pre-hook: "{{ macro_x() }}"
)
}}
macro_4
dbt supports run-level hooks and model-level hooks.
Run-level hooks fire once per invocation, while model- level hooks fire around each individual model.
on-run-end is a run-level after hook that executes once after the entire dbt command completes.
Because macro_1() must run after every dbt run, it correctly belongs here.
The post-hook configured under the models: section runs after each model in that scope finishes building.
This matches the requirement for macro_2() to execute after a model runs.
on-run-start is a run-level before hook and fires once before dbt begins executing any models for that command, making it the right place for macro_3() which must run before every dbt run.
Finally, the pre-hook specified inside a model's config() block runs before that specific model is built.
Since macro_4() must execute before a model runs, it belongs in the pre-hook configuration.
Thus the correct mapping is:
* on-run-end # macro_1
* model post-hook # macro_2
* on-run-start # macro_3
* model pre-hook # macro_4.

NEW QUESTION # 51
Examine the code:
select
left(customers.id, 12) as customer_id,
customers.name as customer_name,
case when employees.employee_id is not null then true else false end as is_employee, event_signups.event_name, event_signups.event_date, sum(case when visit_accomodations.type = 'hotel' then 1 end)::boolean as booked_hotel, sum(case when visit_accomodations.type = 'car' then 1 end)::boolean as booked_ride from customers
-- one customer can sign up for many events
left join event_signups
on left(customers.id, 12) = event_signups.customer_id
-- an event signup for a single customer can have many types of accommodations booked left join visit_accomodations on event_signups.signup_id = visit_accomodations.signup_id and left(customers.id, 12) = visit_accomodations.customer_id
-- an employee can be a customer
left join employees
on left(customers.id, 12) = employees.customer_id
group by 1, 2

Answer:
Explanation:

Explanation:
Operation
Correct Location
Aggregations (booked_hotel, booked_ride)
In a model dedicated to pre-aggregate data for reporting
Standardizing customer_id
In the first layer of models
Selecting customer_name
In the first layer of models
Joining employees
In a model between the first layer and final layer of models
In dbt's recommended modeling framework, transformations should be centralized according to their purpose and level of abstraction. Basic cleaning and column standardization-such as shortening customers.id using left(customers.id, 12)-belongs in the first layer of models, commonly known as staging models. This layer is responsible for producing clean, consistent, analytics-ready fields. Selecting raw descriptive fields like customers.name also belongs in this layer.
Joins that enrich a dataset by combining cleaned staging outputs across domains-such as joining customers with employees-belong in intermediate models. These models unify business logic that sits between the raw staging layer and the final aggregations used for reporting.
Finally, aggregations like counting types of accommodations (booked_hotel and booked_ride) belong in marts or reporting models, especially when they involve summarization that downstream tools (dashboards, BI reports) will consume. This keeps heavy business logic centralized and reusable.

NEW QUESTION # 52
......
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