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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q184-Q189):

NEW QUESTION # 184
Your development environment uses a smaller, sampled dataset compared to production for efficiency. When a new performance-sensitive model is deployed, users report significant slowdowns. What might be some of the potential causes?

A. All of the above-
B. The smaller dataset in development didn't reveal performance issues that become apparent at the scale of production data
C. Your data warehouse might have different resource configurations or indexing in the production environment.
D. Performance optimizations made in development might not translate directly to the larger production data volumes.

Answer: A

Explanation:
A: Sampled data obscures performance problems related to data volume. B: Optimizations designed for small data might not be effective at scale. C: Differences between environments can drastically affect performance.

NEW QUESTION # 185
A dbt run failed with an error message.
Order these steps to fix your pipeline.

Answer:

Explanation:

Explanation:
Check your terminal or log file for the output from the most recent dbt run.
Look for specific error messages associated with the models that failed.
3## Isolate the problem by dbt run --select model_name to run a single model and confirm whether the issue is localized to that model.
4## Use dbt run --select model_name+ to run the model and its downstream dependencies, ensuring that your fix works across the DAG.
Brief Explanation
* First, you always inspect the latest run output (step 1).
* Then, identify the exact failing models and error messages (step 2).
* Next, you reproduce the issue on the individual model to be sure the fix works locally (step 3).
* Finally, you re-run the model plus its downstream dependencies to validate the fix across the DAG (step 4).

NEW QUESTION # 186
Is this materialization supported by Python models in dbt?
Ephemeral

A. No
B. Yes

Answer: A

Explanation:
dbt Python models support a limited set of materializations because they rely on execution within the data platform's Python compute engine (such as Snowpark for Snowflake, Dataproc for BigQuery, or Spark).
These engines require models to materialize into actual relations-tables or views-in order to persist the results of Python-based transformations.
The ephemeral materialization, however, is fundamentally incompatible with this behavior. Ephemeral models do not create relations in the warehouse; instead, dbt inlines their SQL logic directly into downstream models. Since Python models cannot be inlined (they execute Python code, not SQL), dbt does not allow ephemeral Python models. dbt requires Python model outputs to be materialized as either:
* table
* view
* incremental
Therefore, ephemeral is not supported for Python models, and attempting to configure a Python model as ephemeral will result in a compilation error.
The reason is straightforward: ephemeral logic depends on SQL compilation, while Python models depend on executing Python code in the data platform. Because these mechanisms are incompatible, dbt restricts Python models to relational materializations only.
Thus, the correct answer is No - ephemeral is not supported for Python models.

NEW QUESTION # 187
(Multiple Select)

A. Write custom macros to log intermediate data for each model.
B. IJse the -select flag with dbt run to execute models incrementally, starting with the most upstream dependency.
C. Add -full-refresh flag to dbt run to ensure all models are rebuilt from scratch.
D. Run dbt docs generate and dbt docs serve to inspect the lineage graph.

Answer: B,D

Explanation:
Visualization and incremental execution aid debugging. Custom logging might be necessary but is more time-consuming to implement. A full refresh isn't targeted for debugging.

NEW QUESTION # 188
You identify the need to refactor several models with similar transformations. Which of the following is the first step to increase modularity and promote the DRY philosophy?

A. Use a text editor's "find and replace" function to update repetitive code blocks.
B. Create CTEs to segment logical steps within the existing model SQL.
C. Analyze commonalities and differences in the transformations to determine refactoring potential.
D. Test individual models thoroughly to establish a baseline before making changes.

Answer: C

Explanation:
Understanding the shared and unique aspects of the transformations is key to designing an effective modularization strategy.

NEW QUESTION # 189
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