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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Testing and Data Quality	- Built-in and custom tests 1. Custom SQL tests 2. Generic tests (unique, not null, relationships) - Data validation strategies 1. CI-based validation workflows 2. Schema testing practices
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
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
Analytics Engineering Foundations	- SQL proficiency for analytics 1. Data modeling in SQL 2. Joins, aggregations, and window functions - Modern data stack concepts (ELT vs ETL) 1. Warehouse-centric transformation workflows 2. Role of dbt in analytics engineering
Documentation and Lineage	- Data lineage understanding 1. Dependency tracking with ref() 2. Directed acyclic graph (DAG) - dbt documentation system 1. Auto-generated docs site 2. Model descriptions and metadata

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dbt Labs dbt Analytics Engineering Certification Exam dbt-Analytics-Engineering Prüfungsfragen mit Lösungen (Q172-Q177):

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

Antwort: A

Begründung:
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.

173. Frage
(Multiple Select)

A. Confirming that the total count of orders in a day matches a rollup value in a pre-calculated summary table.
B. Verifying that a paid_at timestamp column always occurs after a signup_at timestamp column.
C. Ensuring a primary key column has no null values.
D. Checking if values in a specific column fall within a valid range based on business logic.

Antwort: B,C

Begründung:
Generic tests (not_null and unique) handle basic structural checks. Options C and D involve domain specific logic or external comparisons better suited for custom tests.

174. Frage
After implementing thorough dbt tests, you confidently deploy changes. Yet, downstream Bl dashboards break, and stakeholders complain about inaccurate dat a. What could have gone wrong?

A. Critical assumptions were made about the data in the Bl layer, independent of dbt models.
B. There are logic errors in your dbt models that the tests didn't catch.
C. The tests you wrote were too strict, causing false positives.
D. Your tests were correct, but a bug in dbt prevented them from running successfully.

Antwort: A,B

Begründung:
This emphasizes that models can be internally correct yet still produce unusable output if the source data changes. Tests only cover what they're designed for; the Bl layer may have its own data quality requirements.

175. Frage
You've defined a unique test on a key column within a model. When running dbt test, you encounter a surprising failure. Which of the following might be the cause?

A. An incorrect join in your model is inadvertently creating duplicate rows.
B. There are truly duplicate values in the column.
C. Upstream data was updated unexpectedly, leading to previously non-existent duplicates.
D. The unique test is too strict and doesn't align with the business definition of uniqueness.

Antwort: A,B,C,D

Begründung:
All of these could cause a previously passing test to fail, highlighting the interplay between data, model logic, and test definitions.

176. Frage
Which two mechanisms allow dbt to write DRY code by reusing logic, preventing writing the same code multiple times?
Choose 2 options.

A. Using dbt packages
B. Changing a model materialization from view to ephemeral
C. Writing and using dbt macros
D. Copy/pasting folders containing multiple models
E. Creating singular tests

Antwort: A,C

Begründung:
The correct answers are B: writing and using dbt macros and D: using dbt packages.
dbt strongly encourages DRY (Don't Repeat Yourself) principles, and two of the core mechanisms that support reusable logic are macros and packages. Macros allow you to write Jinja-powered reusable functions that can generate SQL statements dynamically, reducing duplication across models, tests, and project logic.
Macros can encapsulate filters, joins, auditing logic, timestamps, and more-allowing developers to centralize logic in one place while referencing it across many models.
Packages extend this concept even further by allowing entire sets of macros, models, tests, and utilities to be imported into a project. Packages like dbt-utils contain widely used generic macros that help standardize transformations and testing. Using packages ensures consistent logic across teams and eliminates the need to rewrite common transformations.
Option A contradicts DRY principles because copy/pasting increases maintenance burden. Option C is not a mechanism for reusing logic; singular tests validate logic but do not reduce duplication. Option E simply changes a model's materialization and does not support code reuse.
Thus, macros and packages are the only correct dbt mechanisms that provide reusable, modular, DRY logic.

177. Frage
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