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[General] dbt-Analytics-Engineering Exam Consultant, dbt-Analytics-Engineering Paper

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【General】 dbt-Analytics-Engineering Exam Consultant, dbt-Analytics-Engineering Paper

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q39-Q44):NEW QUESTION # 39
Which is true about writing generic tests?
Choose 1 option.
  • A. They must contain a {{ ref() }} snippet to a model.
  • B. They are written using the {% macro %} wrapper.
  • C. They should always accept a column_name.
  • D. They do not need to be specified under a model's YAML configurations.
  • E. They should accept one or both of these arguments: model and column_name.
Answer: B
Explanation:
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.

NEW QUESTION # 40
Choose whether these scenarios describe a test or a contract:

Answer:
Explanation:

Explanation:
1. Can only be defined on SQL models
The Answer:
contract
2. Errors are returned before the model is built
The Answer:
contract
3. May be configured to customize severity
The Answer:
test
4. May be run on ephemeral models
The Answer:
test
dbt tests and contracts serve different purposes in ensuring data quality and model correctness.
Tests evaluate data after it is produced, while contracts validate the structure of a model before dbt attempts to build it.
A model contract is schema-level enforcement that describes required columns, data types, and constraints.
Contracts can only be applied to SQL models, not Python or ephemeral models. Because contracts validate the model's schema before executing any SQL, dbt surfaces those errors before the model is built, preventing invalid schemas from being deployed.
In contrast, tests evaluate the data after dbt builds a model. Tests can be written generically (unique, not_null, relationships, accepted_values) or as custom SQL tests.
They run after the model is materialized. Tests also allow severity configuration, enabling warnings instead of failures for less critical issues-something contracts do not support.
Tests also run on ephemeral models, because dbt expands ephemeral SQL inline within downstream models, allowing tests to still execute logically against the resulting compiled SQL. Contracts, however, do not apply because ephemeral models never materialize into database objects.
Thus:
* "SQL-only" and "errors before build" # contract
* "custom severity" and "run on ephemeral models" # test
If you want the next question formatted the same way, send it!

NEW QUESTION # 41
What must happen before you can build models in dbt?
Choose 1 option.
  • A. Raw data must be cleaned.
  • B. Underlying data must be accessible on your data platform.
  • C. You must have created a service account in your data platform.
  • D. Sources must have been defined in your dbt project.
Answer: B
Explanation:
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.

NEW QUESTION # 42

Answer:
Explanation:

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.

NEW QUESTION # 43
You have just executed dbt run on this model:
select * from {{ source("{{ env_var('input') }}", 'table_name') }}
and received this error:
Compilation Error in model my_model
expected token ':', got '}'
line 14
{{ source({{ env_var('input') }}, 'table_name') }}
How can you debug this?
  • A. Take a look at the compiled code.
  • B. Check your SQL to see if you quoted something incorrectly.
  • C. Check your Jinja and see if you nested your curly brackets.
  • D. Incorporate a log function into your macro.
Answer: C
Explanation:
This error is caused by invalid Jinja syntax, specifically by nesting {{ }} blocks inside another Jinja expression. The expression:
{{ source("{{ env_var('input') }}", 'table_name') }}
compiles to:
{{ source({{ env_var('input') }}, 'table_name') }}
Here, Jinja sees {{ inside another {{ ... }} block. Jinja does not allow nested print statements like this; instead, functions should be called directly inside a single pair of curly braces. The parser encounters an unexpected } where it expects part of a valid expression (hence "expected token ':', got '}'"), which is a classic symptom of mismatched or nested curly braces.
The correct usage is:
select * from {{ source(env_var('input'), 'table_name') }}
In this form, env_var('input') is evaluated first, and its result is passed as the first argument to source() within one Jinja expression.
Option C is therefore the correct debugging approach: inspect your Jinja and look for incorrectly nested curly brackets. Options A and D are generic and don't address the root cause, while B talks about quoting in SQL, which is not the problem-the error arises before SQL compilation, at Jinja parse time.

NEW QUESTION # 44
......
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