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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
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
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. Warehouse-centric transformation workflows 2. Role of dbt in analytics engineering
dbt Core Concepts	- Project structure and configuration 1. dbt_project.yml configuration 2. Packages and dependencies - Models and materializations 1. Ref and source functions 2. Views, tables, incremental models
Deployment and Orchestration	- Running dbt in production 1. CI/CD integration patterns 2. dbt Cloud and job scheduling - Environments and workflows 1. Version control with Git 2. Development vs production environments
Documentation and Lineage	- Data lineage understanding 1. Dependency tracking with ref() 2. Directed acyclic graph (DAG) - dbt documentation system 1. Auto-generated docs site 2. Model descriptions and metadata

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q321-Q326):

NEW QUESTION # 321
Is this materialization supported by Python models in dbt?
Ephemeral

A. Yes
B. No

Answer: B

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 # 322
A newly-hired analyst needs to update a downstream model that depends on a chain of intermediate models. To minimize the potential for disruption, how can modularity help them?

A. By using macros, modifications are limited to a single location.
B. Incremental materializations will help verify that the changes are successful.
C. With clear dependencies, they can pinpoint which upstream models to test.
D. Snapshots ensure that historical data won't be impacted unintentionally.

Answer: C

Explanation:
Modularity and well-defined dependencies make it easier to isolate the impact of a change, simplifying updates and testing.

NEW QUESTION # 323
A complex transformation in your dbt project pulls data from an external API. Due to rate limits, this API endpoint cannot be queried frequently in production. How could you handle development and testing of this transformation effectively?

A. Use environment variables to control API query frequency or switch between live and test data sources.
B. Utilize dbt snapshots to capture a static set of API data for use during development and testing.
C. Set up a development environment that utilizes a mock API to simulate external data.
D. All of the above could be part of a strategy.

Answer: D

Explanation:
A: Mocks enable testing without impacting the production API- B: Environment variables allow for flexible configuration. C: Snapshots provide static samples for testing in isolation.

NEW QUESTION # 324
(Multiple Select)

A. Using SELECT * whenever possible to avoid explicitly specifying needed columns.
B. Avoiding excessive use of UNION operations, especially within loops.
C. Leveraging appropriate indexes on columns used in joins and WHERE clauses.
D. Preferring correlated subqueries over joins when relationships are one-to-one.

Answer: B,C

Explanation:
UNION operations can be expensive. Indexing is essential for query optimization. SELECT * and correlated subqueries are generally less performant.

NEW QUESTION # 325
While onboarding new data sources to your dbt project, you discover some have unexpectedly high null rates in key columns. How would you integrate tests for this into your dbt workflow?

A. Designate some nulls as acceptable by extending the not_null test with the OR condition.
B. Create not_null tests on the raw source tables in a separate dbt project dedicated to source validation.
C. Include a data quality monitoring step in your upstream ETL process before data reaches dbt
D. Write tests within the initial staging models that calculate and check the null percentage.

Answer: C,D

Explanation:
B ensures data entering dbt is checked at the first opportunity. C pushes quality checks as far upstream as possible- A is valid, but less integrated if sources are managed outside dbt's purview D masks the problem rather than addresses it.

NEW QUESTION # 326
......

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