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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. CI/CD integration patterns 2. dbt Cloud and job scheduling
Topic 2: dbt Core Concepts	- Project structure and configuration 1. dbt_project.yml configuration 2. Packages and dependencies - Models and materializations 1. Views, tables, incremental models 2. Ref and source functions
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: 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
Topic 5: Testing and Data Quality	- Data validation strategies 1. CI-based validation workflows 2. Schema testing practices - Built-in and custom tests 1. Custom SQL tests 2. Generic tests (unique, not null, relationships)

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q68-Q73):

NEW QUESTION # 68
You encounter the following error when running dbt compile:Runtime Error in model customer _ churn (models/transformations/customer_churn.sql)

A. The fct_orders source is not configured correctly in your dbt_project.yml file.
B. You need to add a schema test to validate the existence of the fct orders table.
C. The fct_orders model depends on other models that haven't compiled successfully.
D. There's a typo in how you're referencing the fct_orders object in your SQL.

Answer: D

Explanation:
Explanation: This is a runtime error indicating incorrect usage of fct_orders. Options A and C could cause errors but of a different nature. Schema tests won't fix a referencing error.

NEW QUESTION # 69
You discover that dbt models run significantly slower in your production environment than in development. What potential causes should you investigate?

A. Inefficient use of snapshots or incremental models in your production dbt project_
B. Different database configurations or resource allocations (memory, CPU, etc.) between environments-
C. Production data volume being significantly larger than development data.
D. Network latency impactingressing access to remote data sources.

Answer: B,C,D

Explanation:
Each of these directly affects performance and could differ significantly between environments. C is relevant, but less likely the sole cause if development is performant.

NEW QUESTION # 70
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', 'customer_orders') }}
B. {{ source('jaffle_shop_raw_current', 'customer_orders') }}
C. {{ source('jaffle_shop_raw_current', 'orders') }}
D. {{ source('jaffle_shop', 'orders') }}

Answer: D

Explanation:
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.

NEW QUESTION # 71
You introduce a minor formatting change to a large SQL model. Afterwards, the results change unexpectedly. Which dbt-related issue might explain this, even if the SQL itself appears correct?

A. There's a version mismatch between the dbt library used during development and in production.
B. You've used a reserved keyword as a column alias without properly quoting it.
C. An upstream data issue only surfaces due to the formatting change making that part of the query execute differently.
D. A subtle indentation change in a CTE accidentally modifies the execution order within the query.

Answer: D

Explanation:
Whitespace matters in SQL, and CTE order is often significant even if the query might still be validly formatted.

NEW QUESTION # 72
You want to run and test the models, tests, and snapshots you have added or modified in development.
Which will you invoke? Choose 1 option.
Options:

A. dbt build --select state:modified --defer <path/to/artifacts>
B. dbt build --select state:modified --defer --state <path/to/artifacts>
C. dbt build --select state:modified --state <path/to/artifacts>
D. dbt run --select state:modified --defer --state <path/to/artifacts>
dbt test --select state:modified --defer --state <path/to/artifacts>
E. dbt run --select state:modified --state <path/to/artifacts>
dbt test --select state:modified --state <path/to/artifacts>

Answer: B

Explanation:
The goal in this scenario is:
* Select only models, tests, and snapshots that were modified in development, using:state:modified
* Use already-built production objects whenever possible, so development does not need to rebuild everything. This is done with:--defer
* Compare development code against production artifacts, which requires:--state <path/to/artifacts>
* Run everything (models + tests + snapshots) in a single command. This is exactly what dbt build does.
* dbt build = run models + run tests + run snapshots + run seeds.
Putting it all together, the correct command is:
dbt build --select state:modified --defer --state <path/to/artifacts>
This single command satisfies all requirements:
* It runs only modified resources.
* It uses the production state artifacts for comparison.
* It defers to production-built models where possible.
* It runs all resource types automatically.
Why the other options are incorrect:
* A # Missing --state, so state:modified cannot function.
* B # Splits run + test separately; question asks which single command you will invoke.
* D # Missing --defer, which defeats the purpose of using production objects.
* E # Also missing --defer.
Thus, Option C is the only correct answer.

NEW QUESTION # 73
......

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