최신dbt-Analytics-Engineering시험대비덤프공부자료인증공부문제

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: Documentation and Lineage	- dbt documentation system 1. Auto-generated docs site 2. Model descriptions and metadata - Data lineage understanding 1. Directed acyclic graph (DAG) 2. Dependency tracking with ref()
Topic 2: dbt Core Concepts	- Models and materializations 1. Views, tables, incremental models 2. Ref and source functions - Project structure and configuration 1. Packages and dependencies 2. dbt_project.yml configuration
Topic 3: Analytics Engineering Foundations	- Modern data stack concepts (ELT vs ETL) 1. Warehouse-centric transformation workflows 2. Role of dbt in analytics engineering - SQL proficiency for analytics 1. Joins, aggregations, and window functions 2. Data modeling in SQL
Topic 4: Deployment and Orchestration	- Environments and workflows 1. Version control with Git 2. Development vs production environments - Running dbt in production 1. CI/CD integration patterns 2. dbt Cloud and job scheduling
Topic 5: Testing and Data Quality	- Data validation strategies 1. CI-based validation workflows 2. Schema testing practices - Built-in and custom tests 1. Custom SQL tests 2. Generic tests (unique, not null, relationships)

>> dbt-Analytics-Engineering시험대비 덤프공부자료 <<

dbt-Analytics-Engineering덤프데모문제 & dbt-Analytics-Engineering시험자료

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최신 Analytics Engineers dbt-Analytics-Engineering 무료샘플문제 (Q331-Q336):

질문 # 331
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.

정답：

설명：

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.

질문 # 332
A pull request targeting your main branch has been approved in code review but requires changes after automated tests fail. To address this, you should:

A. Disable the automated tests temporarily, merge, and re-enable tests later.
B. Merge the pull request despite the failing tests, resolving the issues in a subsequent fix.
C. Ask the branch author to add new commits fixing the issues. Then merge when tests pass.
D. Directly modify the branch by pushing commits to fix the tests.

정답：C

설명：
The branch author should update their pull request with fixes. Merging should typically only happen once tests pass, indicating code stability.

질문 # 333
What must happen before you can build models in dbt?
Choose 1 option.

A. Sources must have been defined in your dbt project.
B. Underlying data must be accessible on your data platform.
C. Raw data must be cleaned.
D. You must have created a service account in your data platform.

정답：B

설명：
The correct answer is C: Underlying data must be accessible on your data platform.
dbt does not perform data ingestion or data loading. Instead, dbt operates after raw data is already available in your warehouse. This means that before dbt can build any models-whether staging, intermediate, or mart- layer models-the underlying source data must already exist and be accessible in the connected data platform (Snowflake, BigQuery, Redshift, Databricks, etc.). dbt uses SQL to transform existing relations; therefore, if the data platform cannot access the underlying tables or external sources, model execution will fail.
Option A is incorrect because sources do not need to be defined before building models. Models can be built without using sources at all. Source definitions are optional metadata and lineage declarations, not prerequisites.
Option B is incorrect because service accounts are not required; dbt can connect through any credential mechanism supported by the warehouse (OAuth, user accounts, tokens, etc.).
Option D is incorrect because dbt itself performs transformations on raw data-cleaning raw data beforehand is not required; in fact, that is one of dbt's main responsibilities.
Thus, the only true prerequisite is that the warehouse must contain accessible underlying data.

질문 # 334
You discover that a heavily used model is taking significantly longer to materialize. After investigation, you determine that the complexity of the model's logic is a contributing factor. Which of the following actions is LEAST likely to help improve performance?

A. Investigate potential optimizations in the underlying SQL code.
B. Break down the model into smaller, intermediate models using CTEs.
C. Change the materialization from a table to a view.
D. Consider applying an incremental materialization strategy if suitable.

정답：C

설명：
Switching from a table to a view alone doesn't address the core performance issue of complex logic. Other options prioritize breaking down the transformations or directly optimizing the SQL itself.

질문 # 335

정답：

설명：

Explanation:
For models not stored under finance:
# reporter and bi
For models inside the finance folder:
# finance, reporter, bi, and public
In dbt, grants configured at the root level apply to all models unless overridden by a more specific folder- or model-level configuration.
The root-level grant is:
+grants:
+select: ['reporter', 'bi']
This means all models by default are selectable by:
* reporter
* bi
Now the finance folder contains its own override:
finance:
+grants:
+select: ['finance']
When dbt merges grants, overrides do not replace the entire list-they add onto inherited grants unless explicitly cleared. Therefore, models inside the finance folder inherit the parent grants and add the finance grant.
So models under finance are accessible to:
* finance
* reporter
* bi
* and public (implicit default in most warehouses unless denied)
Models not inside finance use only the root grants:
* reporter
* bi
Thus the correct dropdown answers are:
* reporter and bi
* finance, reporter, bi, and public

질문 # 336
......

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