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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam 認定 dbt-Analytics-Engineering 試験問題 (Q346-Q351):

質問 # 346
(Multiple Select)

A. Write a custom Python script to parse dbt models.
B. Manually examine the compiled SQL files.
C. dbt docs
D. dbt test
E. Use the dbt debug command.
F. ref() and source() functions
G. Inspect the manifest.json file.
H. Jinja templating
I. Snapshots

正解：C、F、H

解説：
The manifest.json file, generated by dbt compile, contains comprehensive metadata about your project, including a detailed breakdown of model dependencies.

質問 # 347
Several models perform similar filtering and aggregation on a frequently used source table. What's the most effective way to streamline this process and improve maintainability?

A. Create a snapshot to capture the pre-processed state of the source table.
B. Create a table materialization to hold the pre-processed data.
C. Replicate the filtering and aggregation logic directly within each model.
D. Create an ephemeral model to prepare the data in a common format, referenced by downstream models.

正解：D

解説：
Ephemeral models within CTEs allow for reusable intermediate steps in data transformation and are perfect for avoiding logic duplication across multiple models.

質問 # 348
While onboarding new data sources to your dbt project, you discover some have unexpectedly high null rates in key columns. How would you integrate tests for this into your dbt workflow?

A. Designate some nulls as acceptable by extending the not_null test with the OR condition.
B. Write tests within the initial staging models that calculate and check the null percentage.
C. Create not_null tests on the raw source tables in a separate dbt project dedicated to source validation.
D. Include a data quality monitoring step in your upstream ETL process before data reaches dbt

正解：B、D

解説：
B ensures data entering dbt is checked at the first opportunity. C pushes quality checks as far upstream as possible- A is valid, but less integrated if sources are managed outside dbt's purview D masks the problem rather than addresses it.

質問 # 349
Which is true about writing generic tests?
Choose 1 option.

A. They are written using the {% macro %} wrapper.
B. They should accept one or both of these arguments: model and column_name.
C. They should always accept a column_name.
D. They must contain a {{ ref() }} snippet to a model.
E. They do not need to be specified under a model's YAML configurations.

正解：A

解説：
The correct answer is E: They are written using the {% macro %} wrapper.
Generic tests in dbt are implemented as macros, which means they must be defined using the {% macro %} Jinja syntax. These macros return a SQL query that evaluates to rows representing test failures. dbt executes these queries and marks the test as failed if any rows are returned. This requirement is explicitly documented:
all generic tests are macros located in a test directory or within a package.
Option A is partially correct but not universal. Generic tests typically accept arguments such as model or column_name, but dbt does not require both or either. Custom tests can accept any parameters defined by the user. Option B is incorrect because tests do not require ref(); dbt injects the model relation for you when the test is executed. Option C is false because generic tests must be declared in YAML under a model or source for dbt to run them. Option D is incorrect because tests do not always need a column_name (e.g., relationship tests, row-count tests, table-level validations).
Therefore, the defining and universally required characteristic is that generic tests are written as macros, making E the correct answer.

質問 # 350
(Multiple Select)

A. Run dbt docs generate and dbt docs serve to inspect the lineage graph.
B. Write custom macros to log intermediate data for each model.
C. IJse the -select flag with dbt run to execute models incrementally, starting with the most upstream dependency.
D. Add -full-refresh flag to dbt run to ensure all models are rebuilt from scratch.

正解：A、C

解説：
Visualization and incremental execution aid debugging. Custom logging might be necessary but is more time-consuming to implement. A full refresh isn't targeted for debugging.

質問 # 351
......

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