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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
dbt Fundamentals	15%	- dbt workflow and best practices - dbt project structure - dbt Core vs dbt Cloud
Testing and Documentation	20%	- Custom data tests - Documentation generation - dbt docs and DAG visualization - Schema tests (unique, not_null, accepted_values, relationships)
Data Transformation Techniques	25%	- Jinja templating - Macros and packages - Common table expressions and subqueries - Refactoring and incremental models
Models	25%	- Snapshots - Sources and references - Writing and managing SQL models - Seeds - Materializations (table, view, ephemeral, incremental)
Deployment and Orchestration	15%	- CI/CD with dbt Cloud - Jobs and scheduling in dbt Cloud - Git version control integration - Environments (dev, staging, prod)

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dbt Labs dbt Analytics Engineering Certification Exam dbt-Analytics-Engineering Prüfungsfragen mit Lösungen (Q336-Q341):

336. Frage
Given this dbt_project.yml:
name: "jaffle_shop"
version: "1.0.0"
config-version: 2
profile: "snowflake"
model-paths: ["models"]
macro-paths: ["macros"]
snapshot-paths: ["snapshots"]
target-path: "target"
clean-targets:
- "logs"
- "target"
- "dbt_modules"
- "dbt_packages"
models:
jaffle-shop:
+materialized: table
...and this warning when compiling your project:
[WARNING]: Configuration paths exist in your dbt_project.yml file which do not apply to any resources.
There are 1 unused configuration paths:
- models.jaffle-shop
What is the root cause?
A run hook in the jaffle_shop project was defined with an incorrect regular expression.

A. No
B. Yes

Antwort: A

Begründung:
The true root cause is not related to run hooks or regular expressions. The warning clearly indicates that dbt found a configuration path that does not match any actual model path inside the project. In this case, the config block uses the key models.jaffle-shop, which is incorrect because dbt model paths must match the folder structure and cannot contain hyphens unless the folder itself contains a hyphen. Since the project folder is named jaffle_shop (with an underscore), dbt cannot map the configuration to any model.
dbt evaluates configuration paths based on directory names under the models/ folder. A mismatch between the folder name and the configuration key causes dbt to ignore the configuration entirely. This results in the warning:
"Configuration paths exist in your dbt_project.yml file which do not apply to any resources." This warning is specifically triggered when dbt identifies unused configuration paths due to typos, incorrect nesting, or invalid identifiers. dbt does not interpret hyphens (-) as valid Python identifiers for resource names, so a config path like models.jaffle-shop will never be applied.
Therefore, the assertion that the cause is a "run hook with an incorrect regular expression" is incorrect. The actual root cause is that the configuration path uses the wrong name (jaffle-shop instead of jaffle_shop).

337. Frage
Unexpected Results

A. Implement targeted profiling to measure the execution time of specific query sections within the model.
B. Revert the changes in your version control system to restore the model to its previous working state.
C. Use diff tools or version control history to compare the refactored logic against the previous version.

Antwort: B

Begründung:
Start by gaining situational awareness (logs), pinpointing bottlenecks (profiling), and then analyzing the code change itself (diff). Revert if needed but only after some investigation.

338. Frage
Your dbt project contains sensitive dat
a. You need to load seed data (e.g., lookup tables) while ensuring it's not accidentally committed to version control. How can you best achieve this?

A. Write the seed data into log files, excluding them from version control.
B. Employ a secrets management tool and integrate it with your dbt deployment.
C. Utilize environment variables to store seed data values.
D. Create separate seed files and add them to your .gitignore or equivalent.

Antwort: D

Begründung:
Seed files designed for local use should be kept out of version control. While the other options have merits, they don't directly prevent sensitive data from being committed.

339. Frage
Which two code snippets result in a lineage line being shown in the DAG? Choose 2 options.

A. {{ target.schema }}
B. {{ source('jaffle_shop.customers') }}
C. {{ ref('stg_jaffle_shop__customers') }}
D. {{ source('source', 'table') }}
E. dbt_jsmith.stg_jaffle_shop__customers

Antwort: C,D

Begründung:
In dbt, the Directed Acyclic Graph (DAG) is created from model dependencies, which dbt identifies only through two mechanisms: the ref() function for referencing dbt models and the source() function for referencing declared sources. These functions are explicitly designed to tell dbt how models relate to each other so that dbt can track lineage, enforce build order, and generate documentation.
Option A, {{ source('source', 'table') }}, correctly references a declared source. Any model using source() establishes an upstream dependency on that source table. dbt will therefore draw a lineage line in the DAG from the source to the model.
Option B, {{ ref('stg_jaffle_shop__customers') }}, is the canonical way to reference another dbt model. ref() ensures dbt determines dependencies at compile time, and thus creates a DAG edge between this model and its parent.
The remaining options do not create lineage:
* C directly references a database object, which dbt cannot interpret as a dependency.
* D uses a Jinja variable unrelated to lineage.
* E is invalid, because source() requires two arguments: a source name and table name, not a dotted string.
Therefore, A and B are the only valid answers.

340. Frage
You discover an intermittent issue where your models sometimes produce incorrect results. After investigation, the problem seems to occur when the source data contains a particular combination of unexpected values. How would you best address this?

A. Raise an issue with the team responsible for the upstream data source to fix the error at its origin.
B. Modify the model to ignore the rows causing the issue.
C. Write a custom dbt test to explicitly identify the bad data scenario.
D. Add pre-processing logic within the model to clean or filter out the problematic data.

Antwort: A,C

Begründung:
B helps reliably prevent bad data from affecting output. C seeks a long-term solution by fixing the root cause. A might be a temporary workaround, but not ideal. D is risky and hides the issue.

341. Frage
......

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