dbt-Analytics-Engineering試験資料 & dbt-Analytics-Engineering過去問

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

>> dbt-Analytics-Engineering試験資料 <<

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dbt Labs dbt Analytics Engineering Certification Exam 認定 dbt-Analytics-Engineering 試験問題 (Q154-Q159):

質問 # 154
dbt Model Snippet:SQL

A. The nested subquery to calculate MAX(created_at).
B. The Jinja conditional logic used for incremental logic.
C. The literal use of 'analytics' as the target_database.
D. The source() function referencing data from the 'analytics' schema_

正解：A

解説：
Subqueries can be performance bottlenecks- The other parts, while potentially optimizable, are less likely to cause a dramatic slowdown.

質問 # 155
You're building tests for a dbt model that performs a join across multiple large datasets. You notice performance issues - tests are taking a very long time to run. What might be optimization strategies?

A. Accept that tests on complex models will always be slow.
B. Focus on tests that can validate a subset of the data rather than the entire dataset.
C. Create snapshots of the source tables and test on those snapshots instead.
D. Refactor model SQL to use incremental logic to avoid full table scans.

正解：B、D

解説：
B and C target reducing the test's workload with sampling or optimizing the model itself. A might help under specific circumstances, but not always. D is a defeatist approach!

質問 # 156
You're integrating a new API-based data source. Select the most suitable way to incorporate this into your dbt project.

A. Write a dbt macro to encapsulate the API interaction logic.
B. Utilize a dbt package tailored for the specific API, if available.
C. Create a source with a loaded_at_field and write custom logic to snapshot API data.
D. Create a seed file to load the data directly into a table.

正解：B

解説：
Explanation: Pre-built packages streamline the integration of common APIs. Options A or B are workarounds, and D might be part of a custom solution but not the primary approach.

質問 # 157
Which of the following is NOT a direct benefit of thorough source, table, and column descriptions in dbt?

A. Increased discoverability for other team members working on the project
B. Improved lineage and data traceability within the documentation.
C. Faster dbt model compilation and execution times.
D. Better data quality due to the automated enforcement of column descriptions

正解：C

解説：
Explanation: While good descriptions have numerous benefits, they don't inherently speed up model compilation and execution within dbt-

質問 # 158
(Multiple Select)

A. Additional records are appended with new data.
B. A column is renamed
C. The column ordering within the table is changed.
D. A small percentage of records have null values introduced in a field.

正解：B、D

解説：
Renaming or introducing nulls can directly break downstream SQL that references that column. B (adding data) is generally less disruptive. Column order (D) is usually irrelevant for analytics.

質問 # 159
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無料でクラウドストレージから最新のXhs1991 dbt-Analytics-Engineering PDFダンプをダウンロードする：https://drive.google.com/open?id=1DRLpNmUza1-Rw1_XMbecie_Um1J3GPUk