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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q68-Q73):

NEW QUESTION # 68
Match the macro to the appropriate hook so that the correct execution steps comply with these rules:
* macro_1() needs to be executed after every dbt run.
* macro_2() needs to be executed after a model runs.
* macro_3() needs to execute before every dbt run.
* macro_4() needs to be executed before a model runs.

Answer:

Explanation:

Explanation:
Hook 1
on-run-end: "{{ macro_x() }}"
The Answer:
macro_1
Hook 2
models:
<my_dbt_project>:
post-hook: "{{ macro_x() }}"
The Answer:
macro_2
Hook 3
on-run-start: "{{ macro_x() }}"
The Answer:
macro_3
Hook 4
{{
config(
pre-hook: "{{ macro_x() }}"
)
}}
macro_4
dbt supports run-level hooks and model-level hooks.
Run-level hooks fire once per invocation, while model- level hooks fire around each individual model.
on-run-end is a run-level after hook that executes once after the entire dbt command completes.
Because macro_1() must run after every dbt run, it correctly belongs here.
The post-hook configured under the models: section runs after each model in that scope finishes building.
This matches the requirement for macro_2() to execute after a model runs.
on-run-start is a run-level before hook and fires once before dbt begins executing any models for that command, making it the right place for macro_3() which must run before every dbt run.
Finally, the pre-hook specified inside a model's config() block runs before that specific model is built.
Since macro_4() must execute before a model runs, it belongs in the pre-hook configuration.
Thus the correct mapping is:
* on-run-end # macro_1
* model post-hook # macro_2
* on-run-start # macro_3
* model pre-hook # macro_4.

NEW QUESTION # 69
You typically use dbt run to execute your project. However, you want to trigger a refresh of only models that depend on newly updated source dat a. How could you achieve this?

A. Add dbt run to the post-hook of your upstream ETL process, so it triggers upon data arrival.
B. Configure your sources with the freshness property and run dbt source freshness.
C. Leverage dbt snapshots to identify changed sources and derive the dependent model list.
D. Manually determine dependent models, then use dbt run -m

Answer: B,C

Explanation:
B automates discovery of what's stale. D uses snapshots for the same purpose. A is tedious, C runs all models, not selectively

NEW QUESTION # 70
A new dbt model is created directly in the development environment without a corresponding raw data source. Which of these can be potential ramifications of this approach?

A. The model might be based on assumptions about data that dont hold true when applied in production.
B. There might be limited ability to reproduce or troubleshoot the model if the development data is modified or deleted.
C. All of the above-
D. You might lack a clear reference and lineage back to the original data source.

Answer: C

Explanation:
A: Direct development modeling risks bypassing important raw data checks. B: Data lineage becomes unclear and could lead to misinterpretations. C: Without a reliable source, fixing issues or adapting the model becomes harder

NEW QUESTION # 71
Your dbt_project.yml contains the following: YAML

A. No error: This is a valid configuration-
B. Compilation Error: Invalid indentation causing a YAML parsing issue.
C. Compilation Error: Using "+" to indicate nested configuration is incorrect in -yml files.
D. Runtime Error: Incorrectly specifying materialization within a nested YAML structure-

Answer: B

Explanation:
YAML relies on precise indentation. Materialization should be at the same level as the model name, not nested further.

NEW QUESTION # 72
A model intermittently fails. Sometimes it runs without errors. Which category of issue should you suspect FIRST?

A. Data quality issues in the source that are not consistently present.
B. dbt-specific configuration errors causing unpredictable behavior at the engine level.
C. Race conditions due to models in your DAG running concurrently without proper synchronization.
D. Errors within complex macros used by the model, triggered only under specific data conditions.

Answer: A

Explanation:
Inconsistent failures often suggest data dependency problems. While the others could occur, start with potential data quality concerns.

NEW QUESTION # 73
......

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