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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q54-Q59):

NEW QUESTION # 54
Your team sometimes defines raw data sources directly in dbt models using .yml files within the models directory. When might this approach be problematic?

A. If the raw dataset's schema is subject to change.
B. If the raw dataset requires complex transformations.
C. If multiple models utilize the same raw dataset.
D. If the raw dataset is accessed via a non-standard data warehouse connector.

Answer: C

Explanation:
Defining raw sources within models leads to duplication and potential maintenance issues when multiple models share that dependency. A separate sources.yml file is generally preferred for shared datasets.

NEW QUESTION # 55
A colleague has made updates to some model descriptions and wants you to quickly regenerate the docs to only include those changes. Which command variation would be the most efficient without a full recompilation?

A. dbt docs generate --models +[model_namel
B. It's not possible to partially regenerate dbt docs; you need to run the full dbt docs generate command.
C. Utilize dbt's incremental model feature along with dbt docs generate -no-compile.
D. dbt docs generate -select [model_name]

Answer: D

Explanation:
The -select flag limits the documentation generation to the specified model(s). However, for this to work smoothly, dependencies need to be considered. If the updated model depends on others, they might need to be included or recompiled for the docs to be correct.

NEW QUESTION # 56
You wrote this test against your fct_orders model to confirm status filters were properly applied by a parent model:
{{
config(
enabled=true,
severity='error'
)
}}
select *
from {{ ref('fct_orders') }}
where status_code = 13
Which statement about this test is true?

A. You must attach the test to the model in orders.yml for it to run.
B. Records with status_code = 13 will cause this test to fail.
C. The file must be saved in the tests/generic directory.
D. This test will warn instead of erroring when you use the --warn-error flag.

Answer: B

Explanation:
This test is a singular test, meaning it is defined as a standalone SQL file placed inside the /tests directory (not attached through YAML like generic tests). dbt executes singular tests by running the SQL query, and the test fails if the query returns any rows. In this case, the test selects all records from fct_orders where status_code = 13. That means if even one row in fct_orders has a status code of 13, the test will fail, because dbt interprets returned rows as evidence of a data-quality issue.
The configuration block at the top sets severity='error', meaning the test raises an error when it fails, rather than a warning. This setting overrides default severity behavior, so option B is incorrect. Singular tests do not need to be attached in YAML because they run automatically when placed under /tests # making option A incorrect. Meanwhile, generic tests live in /tests/generic, but singular tests should live directly under /tests (option C incorrect).
Therefore, the only correct statement is that records with status_code = 13 will cause the test to fail, because the test query is explicitly written to identify such rows.

NEW QUESTION # 57
(Multiple Select)

A. Preferring correlated subqueries over joins when relationships are one-to-one.
B. Leveraging appropriate indexes on columns used in joins and WHERE clauses.
C. Using SELECT * whenever possible to avoid explicitly specifying needed columns.
D. Avoiding excessive use of UNION operations, especially within loops.

Answer: B,D

Explanation:
UNION operations can be expensive. Indexing is essential for query optimization. SELECT * and correlated subqueries are generally less performant.

NEW QUESTION # 58
You define a source and later try to reference it. Compilation fails with: "Model depends on a node named 'my source' which was not found". What are some likely causes?

A. There's a typo in how you're referencing the source name using source().
B. The source definition is missing or incorrect in your dbt_project.yml or sources file.
C. Your dbt_project.yml configuration has the incorrect source-paths setting.
D. All of the above.

Answer: D

Explanation:
Dependency issues can manifest like this. Check your source configuration, the source() usage, and even your project's file paths.

NEW QUESTION # 59
......

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