dbt Labs dbt-Analytics-Engineering試験復習、dbt-Analytics-Engineering模擬問題

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam 認定 dbt-Analytics-Engineering 試験問題 (Q318-Q323):

質問 # 318
(Multiple Select)

A. Implement a custom data test to specifically check the column for null values.
B. Switch the model materialization from 'view' to 'table'.
C. Examine the source data for the model to verify the presence of expected values.
D. Add intermediate CTEs within the model's SQL to inspect data at different transformation stages.

正解：C、D

解説：
Verifying source data and stepwise debugging are key. Tests help but come later. Changing the materialization is unlikely to directly address the null-value issue.

質問 # 319
Choose a correct command for each statement.

正解：

解説：

Explanation:
Will always point to the latest version of the source schema
Correct Match: # defer
Allows to use objects built in the target schema with any downstream tool Correct Match: # dbt clone
3## Allows to safely modify objects built in the target schema
Correct Match: # defer
4## Is a point-in-time operation
Correct Match: # dbt clone
defer and dbt clone serve very different purposes in dbt, and understanding their behavior is essential for choosing the right command.
The --defer flag tells dbt to use already-built objects from a previous environment (often production) instead of rebuilding them. Because it always references the existing target schema's most recent objects, it "always points to the latest version of the source schema." Since no objects are overwritten when using defer, it also
"allows safely modifying objects built in the target schema"-your development environment uses production objects without altering them.
By contrast, dbt clone creates a point-in-time copy of existing relations. This cloned schema is static; it does not auto-update when source data changes. Therefore, clone is classified as a "point-in-time operation." Since clone copies physical tables/views into a new schema, downstream tools (BI dashboards, ML pipelines) can safely query the cloned environment without affecting production, making "allows to use objects built in the target schema with any downstream tool" the correct match.
Thus, defer is used for logic substitution without copies, while clone is used for replicable, point-in-time snapshots.

質問 # 320
You're using dbt Cloud and want to tighten control over when production deployments can occur. What dbt Cloud feature, in conjunction with your CI/CD configuration, would allow you to enforce this?

A. Webhooks for triggering jobs by external events.
B. Deployment jobs and schedules.
C. Environment variables for sensitive credentials.
D. dbt Cloud's built-in source freshness checks.

正解：B

解説：
B enables you to define specific times or conditions for production deployments. Others are less directly related to controlling the timing.

質問 # 321
Your dbt project contains sensitive dat
a. You need to load seed data (e.g., lookup tables) while ensuring it's not accidentally committed to version control. How can you best achieve this?

A. Create separate seed files and add them to your .gitignore or equivalent.
B. Employ a secrets management tool and integrate it with your dbt deployment.
C. Utilize environment variables to store seed data values.
D. Write the seed data into log files, excluding them from version control.

正解：A

解説：
Seed files designed for local use should be kept out of version control. While the other options have merits, they don't directly prevent sensitive data from being committed.

質問 # 322
A project stakeholder requests access to an intermediate stage of a dbt transformation pipeline to validate specific calculations.
You need to weigh this request carefully because:

A. Exposing intermediate results can lead to premature analysis based on incomplete data.
B. All of the above-
C. Raw data, development data, and production data often have different levels of data quality, completeness, and timeliness.
D. Granting access to intermediate data may confuse stakeholders who are not familiar with the full transformation process-

正解：A

解説：
A: Intermediate data might be unrefined and not suitable for decision-making. B: Stakeholders might misinterpret the context of intermediary data. C: It's vital to guide stakeholders to final data outputs designed for analysis.

質問 # 323
......

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