dbt-Analytics-Engineering復習対策 & dbt-Analytics-Engineering PDF問題サンプル

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

>> dbt-Analytics-Engineering復習対策 <<

dbt Labs dbt Analytics Engineering Certification Exam 認定 dbt-Analytics-Engineering 試験問題 (Q164-Q169):

質問 # 164
(Multiple Select)

A. Long-term storage of the model's output isn't a primary requirement.
B. The model involves complex transformations that are computationally expensive.
C. The model's data needs to be frequently joined with other datasets for analysis.
D. You need to grant access to a model's results to users with limited permissions.

正解：A、D

解説：
Views are useful for access control and when the results don't necessarily require physical storage. They're not ideal for computationally heavy models as they are re-computed every time they're queried.

質問 # 165
You identify the need to refactor several models with similar transformations. Which of the following is the first step to increase modularity and promote the DRY philosophy?

A. Test individual models thoroughly to establish a baseline before making changes.
B. Analyze commonalities and differences in the transformations to determine refactoring potential.
C. Use a text editor's "find and replace" function to update repetitive code blocks.
D. Create CTEs to segment logical steps within the existing model SQL.

正解：B

解説：
Understanding the shared and unique aspects of the transformations is key to designing an effective modularization strategy.

質問 # 166
You suspect discrepancies in a critical model. To troubleshoot, you need to understand the order of model execution, but the DAG is complex. What would be the BEST strategy to get this information?

A. Visually inspect the manifest.json file.
B. Manually trace the dependencies in your model files.
C. Run dbt docs generate and inspect the generated documentation.
D. Use dbt Is -select

正解：C

解説：
While other options provide some information, dbt docs generate creates comprehensive documentation including a visual DAG to show execution flow clearly.

質問 # 167
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

正解：

解説：

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.

質問 # 168
You encounter the following error when running dbt compile:Runtime Error in model customer _ churn (models/transformations/customer_churn.sql)

A. The fct_orders source is not configured correctly in your dbt_project.yml file.
B. You need to add a schema test to validate the existence of the fct orders table.
C. There's a typo in how you're referencing the fct_orders object in your SQL.
D. The fct_orders model depends on other models that haven't compiled successfully.

正解：C

解説：
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.

質問 # 169
......

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dbt-Analytics-Engineering復習対策 & dbt-Analytics-Engineering PDF問題サンプル

dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

最新のdbt-Analytics-Engineering復習対策 & 資格試験におけるリーダーオファー & 唯一無二なdbt Labs dbt Analytics Engineering Certification Exam

dbt Labs dbt Analytics Engineering Certification Exam 認定 dbt-Analytics-Engineering 試験問題 (Q164-Q169):