dbt-Analytics-Engineering Testantworten & dbt-Analytics-Engineering Prüfungs-Guide

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: 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 2: 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
Topic 3: Documentation and Lineage	- dbt documentation system 1. Model descriptions and metadata 2. Auto-generated docs site - Data lineage understanding 1. Directed acyclic graph (DAG) 2. Dependency tracking with ref()
Topic 4: 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 5: 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. Role of dbt in analytics engineering 2. Warehouse-centric transformation workflows

>> dbt-Analytics-Engineering Testantworten <<

dbt-Analytics-Engineering Prüfungs-Guide - dbt-Analytics-Engineering Testing Engine

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dbt Labs dbt Analytics Engineering Certification Exam dbt-Analytics-Engineering Prüfungsfragen mit Lösungen (Q153-Q158):

153. Frage
Which two statements about Exposures are true?
Choose 2 options.

A. Exposures are defined in .sql files.
B. Models, sources, and metrics are downstream from Exposures.
C. You can run, test, and list resources that feed into your Exposure.
D. Exposures are materialized in the database.
E. Exposures describe a downstream use of your dbt project.

Antwort: C,E

Begründung:
The correct answers are C: Exposures describe a downstream use of your dbt project and E: You can run, test, and list resources that feed into your Exposure.
Exposures in dbt are documentation constructs that describe how the outputs of your dbt project are used downstream-such as dashboards, machine learning models, applications, reporting layers, or external tools.
They exist to provide visibility into the final layer of the analytics workflow, making it easier to track lineage from raw data # models # downstream consumers.
Option C is correct because the official dbt documentation explicitly states that exposures define the downstream use cases of dbt models. They create transparency about dependencies outside the dbt DAG itself.
Option E is also correct. Using commands like dbt ls --select +exposure:<name> or dbt run --select +exposure:
<name>, dbt enables users to run, test, and inspect the upstream resources feeding into the exposure. This is extremely valuable for impact analysis and CI workflows.
Option A is incorrect because exposures are downstream, not upstream-meaning models feed into exposures, not the other way around.
Option B is incorrect because exposures are not materialized in the warehouse; they are metadata stored in YAML.
Option D is incorrect because exposures are defined in YAML files, not SQL files.
Thus, C and E are the correct statements.

154. Frage
You're tasked with adding functionality to your dbt project for:

A. Auditing data quality with comprehensive anomaly detection tests.
B. Calculating complex financial metrics specific to your industry
C. Handling slowly changing dimensions.

Antwort: A

Begründung:
Data quality and SCD management are common needs addressed by many dbt packages. Financial metrics tend to be more domain-specific, and you might need custom logic.

155. Frage
You have a dbt project with models defined as views. After deploying to a production database, the view objects are present, but no data is displayed when querying them. What might be the root cause?

A. There's a bug in dbt's SQL generation that specifically affects views in certain environments.
B. Permissions granted in production don't allow reading data from the underlying tables.
C. Your dbt job didn't run successfully, so the views were created empty.
D. Dbt view materializations behave differently in production.

Antwort: B

Begründung:
B is a common security-related cause of 'missing' data. The others are less likely; views, logs, and dbt core logic tend to be consistent.

156. Frage
During development, you frequently used temporary print statements for debugging. Which of the following is the best practice before opening a pull request?

A. Comment out the print statements but leave them in the code.
B. Remove the print statements entirely from your commits-
C. Consolidate the print statements into a separate debugging function that can be toggled on and off
D. Leave the print statements for future reference, in case similar debugging is needed-

Antwort: B

Begründung:
Debugging code should generally not be part of committed changes, as it clutters the codebase and offers no value to the core functionality.

157. Frage
(Multiple Select)

A. Using dbt run -m to select models using a path pattern.
B. Configuring separate dbt projects for each subdirectory and running them independently.
C. Setting up tags strategically and using the -s flag when executing dbt commands.
D. Defining a custom source in sources.yml that selects tables from the desired subdirectory.

Antwort: A,C

Begründung:
A leverages path-based selection. B works if your organization is tag-based. C is unrelated, sources are about data, not model location. D is overly complex for this task.

158. Frage
......

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