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| Section | Weight | Objectives |
|---|---|---|
| External Dependencies | 10% | - Managing snapshots - External sources integration - Using packages |
| Managing Data Pipelines | 15% | - Pipeline orchestration - Deployment strategies - CI/CD integration |
| Leveraging dbt State | 5% | - State-aware operations - State management |
| Developing dbt Models | 20% | - Model design and structure
|
| Creating and Maintaining Documentation | 10% | - Descriptions and metadata - Documentation standards - Generating documentation |
| dbt Models Governance | 15% | - Project organization and structure - Version control integration - Naming conventions and standards |
| Debugging and Error Resolution | 15% | - Identifying modeling errors - Resolving data quality issues - Debugging techniques |
| Implementing dbt Tests | 10% | - Built-in tests - Test configuration and execution - Custom tests |
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NEW QUESTION # 301
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 # 302
(Multiple Select)
Answer: A,B
Explanation:
Pre-filtering at the source definition and incremental downstream models reduce processing. Timeouts and snapshots address different issues.
NEW QUESTION # 303
You've built a macro to implement a common custom test pattern. When using this macro in multiple models, you realize the tests are running much slower than expected. What might be the cause?
Answer: C,D
Explanation:
Inefficient macro logic would be amplified with wide reuse. Having numerous tests, even simple ones, can add up. C is unlikely unless the macro directly references downstream models.
NEW QUESTION # 304
You discover that an essential dbt macro behaves differently between your development environment and production due to a subtle database version discrepancy. Which debugging approach would be most efficient?
Answer: B,C,D
Explanation:
Each of these helps identify the root cause:A provides runtime insight into the issue. C lets you surface the relevant version for comparison. D allows for reproducible testing.
NEW QUESTION # 305
You encounter the following error when running dbt compile:Runtime Error in model customer _ churn (models/transformations/customer_churn.sql)
Answer: C
Explanation:
Explanation: This is a runtime error indicating incorrect usage of fct_orders. Options A and C could cause errors but of a different nature. Schema tests won't fix a referencing error.
NEW QUESTION # 306
......
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