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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Analytics Engineering Foundations	- Modern data stack concepts (ELT vs ETL) 1. Role of dbt in analytics engineering 2. Warehouse-centric transformation workflows - SQL proficiency for analytics 1. Joins, aggregations, and window functions 2. Data modeling in SQL
dbt Core Concepts	- Models and materializations 1. Ref and source functions 2. Views, tables, incremental models - Project structure and configuration 1. dbt_project.yml configuration 2. Packages and dependencies
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
Testing and Data Quality	- Data validation strategies 1. Schema testing practices 2. CI-based validation workflows - Built-in and custom tests 1. Custom SQL tests 2. Generic tests (unique, not null, relationships)
Deployment and Orchestration	- Running dbt in production 1. dbt Cloud and job scheduling 2. CI/CD integration patterns - Environments and workflows 1. Development vs production environments 2. Version control with Git

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dbt Labs dbt-Analytics-Engineering Praxisprüfung - dbt-Analytics-Engineering Fragen Beantworten

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dbt Labs dbt Analytics Engineering Certification Exam dbt-Analytics-Engineering Prüfungsfragen mit Lösungen (Q269-Q274):

269. Frage
"Our dbt jobs take hours to run, and it's hard to know why!" says a frustrated colleague. What initial investigation steps would you suggest?

A. All of the above.
B. Check your dbt_project.yml configuration for any project-wide performance settings to optimize.
C. Look into dbt's profiling capabilities to pinpoint the slowest models and queries.
D. Review any 'on-run-start' or 'on-run-end' hooks that might add unexpected overhead.

Antwort: D

Begründung:
Each of these could provide valuable insights. Start with profiling to find the most performance-critical areas, but don't neglect hooks and potential config-level optimizations.

270. Frage
You've defined a complex custom singular test. While testing it initially on a development dataset, it times out. Which optimization techniques might be worth trying?

A. Disable the test in production environments and run it only in development.
B. Remove any redundant joins or subqueries from the test logic.
C. Ensure the test SQL uses appropriate indexes in the underlying database.
D. Rewrite the test using a dbt macro for better maintainability.

Antwort: B,C

Begründung:
A and C target improving query performance. B deals with code organization, not speed. D sacrifices data quality for speed

271. Frage
The dataset raw user activity is critical to several models. You've been informed that a major schema change is scheduled for this source. Select the best way to proactively mitigate the potential impact of this change on your dbt project.

A. Communicate with the relevant data team to create a temporary staging table mirroring the old schema.
B. Run dbt test after the schema changes and address any failures.
C. Create custom data tests that validate the critical columns in raw_user_activity.
D. Pause all dbt jobs until the schema change risk is fully assessed.

Antwort: C

Begründung:
Custom data tests proactively ensure that even if the schema changes, the critical data in raw_user_activity remains valid and consistent for your models. Other options are reactive or might introduce unnecessary downtime.

272. Frage
(Multiple Select)

A. Introduce a global persist_docs setting within your dbt_project.yml file to resolve schema inconsistencies.
B. If available, compare your schema.yml to a previous working version to spot problematic changes.
C. Utilize a YAML validator tool independently of dbt to check for general formatting errors.
D. Refer to the dbt documentation for a comprehensive list of valid properties within schema files.

Antwort: C,D

Begründung:
Understanding valid schema properties and general YAML linting are key. Version comparison can be useful, while persist_docs is less relevant to initial compilation errors.

273. Frage
Which command materializes my_model and only its first-degree parent model(s) in the data platform?
Choose 1 option.

A. dbt run --select 1+my_model
B. dbt compile --select +1 my_model
C. dbt run --select +1 my_model
D. dbt run --select +my_model
E. dbt compile --select +my_model

Antwort: C

Begründung:
The correct answer is D: dbt run --select +1 my_model.
In dbt's selection syntax, the + operator is used to include parents (upstream) and/or children (downstream) of a given node. When used as +my_model or my_model+, it means "include all ancestors" or "all descendants" respectively, with no limit on depth.
To limit how many levels of parents or children are included, dbt allows a numeric depth between the + and the resource name. +1 my_model (or my_model+1) means:
* Select my_model
* Plus only its first-degree parents (direct dependencies), and no further ancestors.
Because you want to materialize the models, you must use dbt run, not dbt compile. dbt compile only compiles SQL and does not create or update relations in the data platform.
So:
* Option A (dbt run --select +my_model) would include all upstream ancestors, not just first-degree parents.
* Options B and E use compile, so they don't materialize.
* Option C is just syntactically wrong.
Therefore, the only command that both runs models and limits selection to my_model plus its first-degree parents is dbt run --select +1 my_model.

274. Frage
......

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