dbt-Analytics-Engineeringトレーニング & dbt-Analytics-Engineering関連資料他の同様の教育プラットフォームとは異なり、dbt-Analytics-Engineeringクイズガイドは、分類なしのランダムな蓄積ではなく、マルチプレート配布用の資料を割り当てます。 dbt-Analytics-Engineering準備トレントは、さまざまな文化レベルのユーザーにより適したdbt-Analytics-Engineeringテスト資料を開発するために、従来の学習プラットフォームの利点に吸収され、その欠点を認識しています。そして、dbt-Analytics-Engineering試験材料は、プレートの多くの研究部分がユーザーの熱意を喚起するのに十分であり、ユーザーが集中力を維持できるようにします。 dbt Labs dbt Analytics Engineering Certification Exam 認定 dbt-Analytics-Engineering 試験問題 (Q42-Q47):質問 # 42 正解：
解説：
Explanation:
Drops and recreates a definition of a query which is executed at the time it's referenced
# view
Inserts or updates records of an existing object in the warehouse using a delta
# incremental
Code belonging to the model is inserted into the places the model is referenced
# ephemeral
Drops and recreates an object in the warehouse as stored results when using dbt run
# table
質問 # 43 正解：
解説：
Explanation:
Information Type
Retrieved From
Singular tests
.sql files
Column data types
Data platform information schema
Generic tests
.yml configuration
SQL code
C. .sql files
Column descriptions
.yml configuration
Model dependencies
.sql files
The dbt docs command compiles metadata about your project by gathering information from three primary sources: your warehouse's information schema, your YAML configuration files, and your SQL model files. Understanding which metadata comes from which source is essential for debugging and for effective documentation practices.
Singular tests live inside .sql files within the /tests directory. Since dbt renders these tests directly from SQL files, their definitions appear in documentation sourced from that location.
Column data types come from the warehouse itself. dbt introspects the data platform information schema to retrieve actual types because dbt does not infer or define column types-only the warehouse does.
Generic tests (e.g., unique, not_null, accepted_values) are declared in .yml files. These YAML definitions contain test configurations, descriptions, and parameters, which dbt uses to document and execute these tests.
SQL code for models is naturally sourced from .sql files where the models are defined. This includes logic such as SELECT statements, CTEs, and transformations.
Column descriptions are written exclusively in .yml files. dbt never extracts descriptions from SQL comments-only from YAML.
Model dependencies come from the ref() and source() calls inside .sql model files, which dbt parses to build the DAG.
質問 # 44
Which two configurations can be applied to a dbt test?
Choose 2 options.
A. enabled
B. materialized
C. tags
D. persist_docs
E. on_schema_change
正解：A、C
解説：
The correct answers are B: tags and C: enabled.
dbt tests-both generic and singular-support a limited but clearly defined set of configurations. Two of the supported configurations are tags, which allow grouping and selecting tests using dbt's selector syntax, and enabled, which can be set to true or false to control whether a test should run. These configurations are commonly used to manage large test suites, such as disabling slow tests in development or tagging tests for CI pipelines.
Option A (on_schema_change) is a model-only configuration and cannot be applied to tests.
Option D (materialized) applies only to models, not tests, since tests compile into queries that evaluate failures rather than physical objects.
Option E (persist_docs) applies to models and sources for documentation purposes. Tests do not support persisted documentation properties beyond what appears in test metadata.
dbt's documentation makes clear that tests inherit only a small subset of configurations compared to models- primarily enabled, tags, severity, and custom test arguments. Among the options provided, only tags and enabled are valid and supported.
Thus, the correct answers are B and C.
質問 # 45
You work at an e-commerce company and a vendor provides their inventory data via CSV file uploads to an S3 bucket.
How do you prep the data for dbt transformations?
Choose 1 option.
A. Create a dbt model with a view querying the external table directly.
B. Run a pre-hook to create a temporary table and query from it in a staging model.
C. Use dbt seed to stage the data in your data platform.
D. Declare the external table as a source using the external configuration.
正解：D
解説：
The correct answer is D: Declare the external table as a source using the external configuration.
When data arrives in an external storage system such as S3, many warehouses (like Snowflake, BigQuery, and Databricks) support external tables that reference data stored outside the warehouse. dbt fully supports external tables by allowing them to be declared as sources using the external: configuration block inside sources: in a YAML file. This tells dbt that the upstream object exists outside the warehouse and should not be materialized or transformed by dbt directly. It also enables dbt to include external data in lineage graphs, freshness checks, and downstream model dependencies.
Option A is not ideal because you still need to formally register the external table as a source; simply querying it in a model breaks the data lineage structure. Option B is unnecessary and operationally brittle- temporary tables are not required for external data ingestion. Option C is incorrect because dbt seed is intended for uploading local static CSV files, not files arriving via S3 or controlled by external vendors. Seeds do not support dynamic ingestion patterns.
Thus, the correct and documented method is to declare the external table as a source with the external configuration, allowing dbt to consume the data appropriately while maintaining proper governance and lineage.
質問 # 46
Which two configuration items can be defined under models: in your dbt_project.yml file?
Choose 2 options.
A. test
B. source
C. schema
D. target
E. tags
正解：C、E
解説：
The correct answers are A: schema and C: tags.
In dbt, the dbt_project.yml file is the central configuration file that defines model-level settings. Under the models: section, you can specify a wide range of model configurations such as schema, materialized, tags, alias, and custom meta fields. The schema configuration allows you to control which database schema a model should be built in, giving analytics engineers the flexibility to organize models by domain or environment. The tags configuration is also valid under models: and is widely used to group models logically for selection, documentation, or orchestration workflows.
Option B (source) is incorrect because sources are defined under YAML files in the sources: section, not under models: in dbt_project.yml. Option D (test) is incorrect because tests must be defined in model or source YAML files, not inside the project configuration. Option E (target) is not a configuration that applies to models; rather, it refers to dbt runtime environments and cannot be configured under the models: block.
dbt's project configuration system ensures that model-level behavior is managed centrally and consistently, and schema and tags are two of the officially supported configuration keys under models:.
