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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: dbt Core Concepts	- Models and materializations 1. Views, tables, incremental models 2. Ref and source functions - Project structure and configuration 1. Packages and dependencies 2. dbt_project.yml configuration
Topic 2: 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 3: 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
Topic 4: Deployment and Orchestration	- Environments and workflows 1. Version control with Git 2. Development vs production environments - Running dbt in production 1. dbt Cloud and job scheduling 2. CI/CD integration patterns
Topic 5: Testing and Data Quality	- Data validation strategies 1. CI-based validation workflows 2. Schema testing practices - Built-in and custom tests 1. Generic tests (unique, not null, relationships) 2. Custom SQL tests

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dbt Labs dbt Analytics Engineering Certification Exam dbt-Analytics-Engineering Prüfungsfragen mit Lösungen (Q337-Q342):

337. Frage
(Multiple Select)

A. The error_after freshness check syntax.
B. Using an 's' at the end of tables.
C. Misspelling "orders" as "orderes".
D. Indenting freshness incorrectly under the customers table.

Antwort: C,D

Begründung:
YAML cares about spelling and indentation. The others are valid parts of source definition syntax.

338. Frage
Examine the configuration for the source:
sources:
- name: jaffle_shop
schema: jaffle_shop_raw_current
tables:
- name: orders
identifier: customer_orders
Which reference to the source is correct?

A. {{ source('jaffle_shop', 'orders') }}
B. {{ source('jaffle_shop_raw_current', 'orders') }}
C. {{ source('jaffle_shop', 'customer_orders') }}
D. {{ source('jaffle_shop_raw_current', 'customer_orders') }}

Antwort: A

Begründung:
In dbt, the source() function resolves a source by its declared source name and table name, not by the physical schema or identifier in the warehouse. The YAML block defines a source named jaffle_shop, and under that source, a table named orders. The identifier: customer_orders field tells dbt that although the logical table name is orders, the actual physical object in the warehouse is named customer_orders.
dbt always expects the syntax:
{{ source(source_name, table_name) }}
Here, the correct reference uses jaffle_shop as the source name and orders as the table name because these are the logical names assigned in the YAML. dbt internally resolves the physical table name via the identifier field, so the model should not reference customer_orders directly.
Option A and B are incorrect because the first argument is not the schema; dbt does not use schemas in the source() call. Option D is incorrect because customer_orders is the warehouse identifier, not the logical table name recognized by dbt.
Therefore, the correct reference is:
{{ source('jaffle_shop', 'orders') }}
This ensures consistent modeling, dependency tracking, and accurate documentation.

339. Frage
Your development environment uses a smaller-scale version of the production warehouse. You often debug issues that depend on data volume, requiring a way to temporarily "scale up" your development environment. Which strategies are feasible?

A. Run a separate dbt project against production data for testing, but target a staging schema.
B. Selectively snapshot a subset of production data into your development environment.
C. Write dbt macros that use temporary tables to simulate larger datasets during development
D. Dynamically increase resource allocation (CPU, memory) within your data warehouse, if supported.

Antwort: A,B,D

Begründung:
A directly addresses the resource limitation, if possible. C offers a controlled way to bring in real data. D creates a sandboxed environment within production. B is clever, but unlikely to be truly representative.

340. Frage
You're evaluating whether to adopt an external tool that offers a visual, drag-and-drop interface for building data models. When considering this alongside dbt, what's a KEY factor to weigh?

A. The ease of migrating existing dbt projects into the visual tool.
B. Whether the tool can handle the full complexity of your data transformations.
C. If the visual tool has advanced machine learning capabilities for data cleaning.
D. The ability of the tool to generate dbt-compatible SQL and integrate into your version control workflow.

Antwort: B,D

Begründung:
A and B touch on the crucial issues of how such a tool fits into your established workflow with dbt as the orchestrator, and whether it really matches the sophistication of your needs.

341. Frage
You need to run a dbt job that performs a long-running data cleansing operation. The job occasionally times out. Which of the following are potential solutions?

A. Schedule the job to run concurrently with other dbt models to maximize resource usage-
B. Use a dbt macro to retry the cleansing operation with a backoff strategy within the model itself-
C. Increase the timeout setting in your CI/CD tool or dbt job configuration.
D. Break the cleansing logic into multiple models, chaining them with dependencies-

Antwort: B,C,D

Begründung:
Each tackles the timeout issue differently:A reduces the work done in a single step. B provides more time directly . C adds retry resilience within the logic. D would likely worsen performance due to resource contention.

342. Frage
......

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