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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: Documentation and Lineage	- Data lineage understanding 1. Dependency tracking with ref() 2. Directed acyclic graph (DAG) - dbt documentation system 1. Model descriptions and metadata 2. Auto-generated docs site
Topic 2: dbt Core Concepts	- Project structure and configuration 1. Packages and dependencies 2. dbt_project.yml configuration - Models and materializations 1. Ref and source functions 2. Views, tables, incremental models
Topic 3: Testing and Data Quality	- Built-in and custom tests 1. Generic tests (unique, not null, relationships) 2. Custom SQL tests - Data validation strategies 1. CI-based validation workflows 2. Schema testing practices
Topic 4: 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 5: 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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q232-Q237):

NEW QUESTION # 232
You're debugging a runtime error. Analyzing the compiled SQL alone doesn't provide enough clues to find the root cause. What's your next debugging step?

A. Modify the compiled SQL directly and re-run the query against your warehouse.
B. Compare compiled SQL from a previous successful run to the current failing version-
C. Disable custom macros to see if one of them is interfering-
D. Examine the dbt logs for more context about the model's execution and dependencies.

Answer: D

Explanation:
Logs often provide additional error messages and context Comparing SQL versions can be helpful but might not illuminate the root cause. Never modify compiled SQL directly

NEW QUESTION # 233
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 --state <path/to/artifacts>
B. dbt run --select state:modified --defer --state <path/to/artifacts>
dbt test --select state:modified --defer --state <path/to/artifacts>
C. dbt build --select state:modified --defer --state <path/to/artifacts>
D. dbt run --select state:modified --state <path/to/artifacts>
dbt test --select state:modified --state <path/to/artifacts>
E. dbt build --select state:modified --defer <path/to/artifacts>

Answer: C

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 # 234
(Multiple Select)

A. Using clear and consistent naming conventions across models and macros.
B. Preferring long, monolithic SQL files with minimal CTEs.
C. Minimizing explicit model dependencies to create loosely coupled components.
D. Encapsulating complex logic in well-named macros with descriptive comments.

Answer: A,D

Explanation:
Well-documented macros and consistent naming boost readability. Large monolithic SQL files hinder maintainability.

NEW QUESTION # 235
You've configured your project to include both schema and data tests in the generated documentation. A user reports the tests are visible but don't show any execution results (pass/fail status). Why might this be happening?

A. Your data pipeline hasn't been executed recently, so there are no recent test results.
B. The type of test you're using is not supported for displaying results in the documentation.
C. The documentation generator has a known limitation on how test results are displayed.
D. The project settings are explicitly configured to hide test results in the documentation.

Answer: A,D

Explanation:
A Test results depend on recent executions; if your data pipeline is not updated, there won't be fresh results. B: Its possible to have configurations that display the tests themselves but not their execution results

NEW QUESTION # 236
You write a macro to dynamically create a filter string and apply it within a model as follows:SQL

A. You cannot dynamically apply filters using Jinja; they must be hardcoded in your model's SQL.
B. The database doesn't support comparison of the data type of the region column with a string value.
C. The generate_filter macro should be placed within a separate macros directory, not inline in the model.
D. The macro templating incorrectly nests single and double quotes.

Answer: D

Explanation:
Nesting quotes within Jinja can be tricky. Review the macro's output for unintended formatting. The others are unlikely to cause this specific error.

NEW QUESTION # 237
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