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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q355-Q360):

NEW QUESTION # 355
You have created a model called stg_tasks and now you need to implement tests.
You provide this in schema.yml:
version: 2
models:
- name: stg_tasks
columns:
- name: completed_at
tests:
- not_null:
- config:
where: "state = 'completed'"
You receive this compilation error:
[WARNING]: Did not find matching node for patch with name 'stg_tasks' in the 'models' section of file
'models/example/schema.yml'
How can you change the configuration on the not_null test to fix this compiler error?

A. tests:
- not_null:
config:
where: "state = 'completed'"
B. tests:
- not_null:
config:
where: "state = 'completed'"
C. tests:
- not_null:
- config:
where: "state = 'completed'"
D. tests:
- not_null:
- config:
where: "state = 'completed'"
Choose 1 option.

Answer: A

Explanation:
In dbt, when you configure a generic test like not_null in YAML, the configuration for that test must be a mapping, not another list item. The correct structure is:
tests:
- not_null:
config:
where: "state = 'completed'"
In your original YAML, you wrote:
tests:
- not_null:
- config:
where: "state = 'completed'"
The extra dash (- config) makes config an element of a list rather than a key under the not_null test. This breaks the expected shape of the test definition. When dbt parses the schema.yml, it fails to correctly interpret the patch for the stg_tasks model, which leads to the warning: "Did not find matching node for patch with name 'stg_tasks'...".
By removing the extra dash and nesting config directly under not_null, dbt now reads this as a single generic test named not_null with a config block that passes the where argument. This allows dbt to correctly attach the test to the completed_at column of the stg_tasks model and eliminates the compiler warning.
Therefore, Option B is the only structurally valid configuration and is the correct answer.

NEW QUESTION # 356
A project stakeholder requests access to an intermediate stage of a dbt transformation pipeline to validate specific calculations.
You need to weigh this request carefully because:

A. Granting access to intermediate data may confuse stakeholders who are not familiar with the full transformation process-
B. Exposing intermediate results can lead to premature analysis based on incomplete data.
C. Raw data, development data, and production data often have different levels of data quality, completeness, and timeliness.
D. All of the above-

Answer: B

Explanation:
A: Intermediate data might be unrefined and not suitable for decision-making. B: Stakeholders might misinterpret the context of intermediary data. C: It's vital to guide stakeholders to final data outputs designed for analysis.

NEW QUESTION # 357
You're integrating a new API-based data source. Select the most suitable way to incorporate this into your dbt project.

A. Write a dbt macro to encapsulate the API interaction logic.
B. Create a seed file to load the data directly into a table.
C. Utilize a dbt package tailored for the specific API, if available.
D. Create a source with a loaded_at_field and write custom logic to snapshot API data.

Answer: C

Explanation:
Explanation: Pre-built packages streamline the integration of common APIs. Options A or B are workarounds, and D might be part of a custom solution but not the primary approach.

NEW QUESTION # 358
(Multiple Select)

A. The overall size and complexity of your dbt project
B. The technology used for your production data warehouse.
C. The need for feature parity (functions, syntax) with the production warehouse.
D. The cost-efficiency of the development environment solution.

Answer: B,C,D

Explanation:
A ensures compatibility and consistent developer experience. B avoids excessive development costs. C prevents surprises when porting code- D influences the scale of resources you need in development.

NEW QUESTION # 359
You're planning your production dbt deployment strategy. When deciding between completely separate development and deployment environments vs. using different schemas within a single data warehouse, what are I key factors to weigh?

A. Your data warehouse's pricing model and how it charges for resource usage.
B. The desired level of isolation and access control granularity.
C. The need to collaborate between developers with varying skill levels.
D. The complexity of your dbt project and the number of models.

Answer: A,B,C

Explanation:
B speaks to security and change management. C is important as the cost could differ considerably. D influences the need for stronger isolation. A is less important than the others here.

NEW QUESTION # 360
......

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