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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Transformation Techniques	25%	- Common table expressions and subqueries - Jinja templating - Refactoring and incremental models - Macros and packages
Topic 2: Deployment and Orchestration	15%	- Jobs and scheduling in dbt Cloud - CI/CD with dbt Cloud - Environments (dev, staging, prod) - Git version control integration
Topic 3: Testing and Documentation	20%	- Documentation generation - dbt docs and DAG visualization - Schema tests (unique, not_null, accepted_values, relationships) - Custom data tests
Topic 4: Models	25%	- Writing and managing SQL models - Seeds - Sources and references - Materializations (table, view, ephemeral, incremental) - Snapshots
Topic 5: dbt Fundamentals	15%	- dbt workflow and best practices - dbt Core vs dbt Cloud - dbt project structure

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q348-Q353):

NEW QUESTION # 348
You get a warning during dbt run:WARNlNG: Found 100 rows in 'stg_customers' that will become duplicates in the 'dim_customers' target.

A. Update the stg_customers source definition to include filtering logic to remove duplicate rows.
B. Investigate your dim_customers modeling logic to ensure a unique key is correctly used to prevent duplicates.
C. It's safe to ignore this warning, as de-duplication is handled automatically by dbt.
D. Increase the memory allocated for the database to handle the large number of duplicates.

Answer: B

Explanation:
This typically indicates a logic flaw in your model creating the target table. Modifying the source or increasing memory won't resolve the core issue.

NEW QUESTION # 349
Consider this DAG:
app_data.detail_categories -> stg_detail_categories -> skills_with_details app_data.details -> stg_details -> lessons_with_details What will support making this DAG more modular? Choose 1 option.

A. Union stg_detail_categories and stg_details in the staging layer to reduce the need for downstream joins.
B. Combine lessons_with_details and skills_with_details into one wide table called curriculum_with_details.
C. Consolidate the two staging models into one model and then use this downstream for both skills_with_details and curriculum_with_details.
D. Join stg_detail_categories and stg_details in the staging layer to reduce the need for downstream joins.
E. Examine the SQL of lessons_with_details and skills_with_details to see if there is a candidate for an intermediate model.

Answer: E

Explanation:
Modularity in dbt means breaking transformations into small, reusable steps that are easy to reason about, test, and reuse. In this DAG, two staging models (stg_detail_categories and stg_details) feed into two mart- level models (skills_with_details and lessons_with_details). If there is repeated logic in those two mart models-such as common joins, filters, or derived fields-that logic is a good candidate to be extracted into an intermediate model.
Option E follows dbt best practices: inspect the SQL of lessons_with_details and skills_with_details and identify shared logic to move into a new intermediate model (for example, int_details_with_categories). Both downstream models can then reference this intermediate model using ref(), centralizing logic and improving maintainability.
Options A and B push joins/unions into the staging layer, which should focus on clean, 1:1 representations of sources-not combining multiple sources. Option C collapses distinct staging models into one, reducing clarity and breaking the source-to-staging contract. Option D creates an overly wide, less reusable final table instead of focusing on shared reusable logic.
Therefore, the action that truly increases modularity is Option E: creating an intermediate model based on common SQL patterns.

NEW QUESTION # 350
(Multiple Select)

A. Submit an issue to the package's GitHub repository if its open-source.
B. Temporarily override the buggy macro or model within your dbt project.
C. Update the packages_yml file to point to your forked version of the package.
D. Switch to a different dbt package offering similar functionality.

Answer: A,B

Explanation:
Contributing to open-source is great, but you likely need an immediate fix. Overriding gives you control while addressing the issue upstream. Forking is feasible, but the maintenance burden might outweigh the benefits.

NEW QUESTION # 351
A performance-critical model depends on multiple complex transformations that would benefit from being pre-computed. Direct querying of this model should be as fast as possible. Which materialization is likely the best choice?

A. View
B. Ephemeral
C. Table
D. Incremental

Answer: C

Explanation:
Explanation: Tables store materialized results on disk. When pre-calculated, they offer the fastest query response time, making them suitable for performance-sensitive models.

NEW QUESTION # 352
You need to test for consistency between a raw source table and its corresponding dbt model after transformation. This requires comparing many columns for null counts and matching value distributions. Which approach would be most efficient?

A. Using dbt's built-in source freshness tests for a high-level check.
B. Manually writing SQL queries to separately calculate and compare these metrics.
C. Relying entirely on generic not_null and unique tests.
D. Creating a custom dbt test that leverages statistical comparison techniques.

Answer: D

Explanation:
A is tedious, B is too coarse-grained, and D is insufficient. A custom test designed for this comparison provides automation and the needed specificity.

NEW QUESTION # 353
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