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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q81-Q86):

NEW QUESTION # 81

Answer:

Explanation:

Explanation:
(new_project)
(models:)
(grants)
(require-dbt-version: '>= 1.0.0' )
The dbt_project.yml file acts as the configuration backbone for every dbt project. dbt requires that project names follow a strict naming convention, allowing only lowercase letters, numbers, and underscores.
Therefore, new_project is the only valid name among the choices. Names with spaces or hyphens, such as new project or new-project, violate dbt's project naming rules.
To configure how models behave-materializations, schemas, tags, or per-directory overrides-dbt uses the models: block inside dbt_project.yml. This block defines configuration inheritance rules and is different from the config() function, which is used inside SQL models, not in YAML files.
When granting permissions on database objects created by dbt, you use the grants configuration. This allows you to define access roles at the model level or project level for downstream BI or analytics tools, ensuring proper data governance and controlled data exposure.
Finally, compatibility with specific dbt versions is set using require-dbt-version. The correct YAML formatting uses quotes, such as '>= 1.0.0', ensuring dbt interprets the value as a string. This prevents accidental mis-parsing and enforces version requirements during dbt run or dbt build.

NEW QUESTION # 82
You have a dbt model that performs complex aggregations. During development, you notice some models take excessively long to run, but they ultimately complete without errors. Which of the following might be true?

A. This behavior automatically triggers an optimization feature within dbt.
B. Downstream models will inherit the performance issues.
C. The models are at risk of failing if the source data grows substantially.
D. These models likely have issues with joins or CTE structure impacting performance.

Answer: C,D

Explanation:
A and B are likely true. Models without errors can still signify potential scaling issues and inefficient logic. C is not necessarily true. Downstream models won't inherit performance issues unless they directly rely on the output of these slow models. D is false. dbt has optimization features, but they often require manual configuration, not automatic triggering.

NEW QUESTION # 83
You implement dbt tests to monitor data quality over time. Which of the following scenarios could these tests potentially help you detect?

A. An unexpected spike in web traffic leading to invalid data entries in your source system-
B. An accidental schema change by someone working outside of your dbt project-
C. Gradual degradation in source data completeness due to an API rate limit being reached
D. Performance issues in your data warehouse impacting the speed of dbt model updates.

Answer: A,B,C

Explanation:
A, C, and D relate to the changing quality of the data itself, which tests can surface. B (warehouse performance) is a separate issue.

NEW QUESTION # 84
You're working with a dataset where a column is expected to be updated frequently with newly arriving events. Which type of test would be MOST suitable to check that this process is working as intended?

A. A test comparing the row count of the source table between different dbt runs.
B. A custom test comparing timestamps on the column in your dbt model to the current time.
C. A generic 'not_null' test on the column.
D. A schema test on the column in the source table.

Answer: B

Explanation:
It focuses on freshness, which is the core concern in this scenario. The other options, while valid, don't target this specific assumption.

NEW QUESTION # 85
(Multiple Select)

A. Configuring tests to run only on models that have changed or have upstream changes.
B. Designing tests, where feasible, to examine subsets of data rather than full tables.
C. Running dbt tests in parallel with dbt model builds whenever possible-
D. Scheduling less critical tests to run during off-peak hours.

Answer: A,B,D

Explanation:
A targets tests to the most relevant areas, reducing workload. C leverages less busy periods to avoid resource contention. D reduces individual test runtimes through sampling. B is generally inadvisable, as testing and model building might compete for resources.

NEW QUESTION # 86
......

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