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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q218-Q223):

NEW QUESTION # 218
Several models perform similar filtering and aggregation on a frequently used source table. What's the most effective way to streamline this process and improve maintainability?

A. Create an ephemeral model to prepare the data in a common format, referenced by downstream models.
B. Create a table materialization to hold the pre-processed data.
C. Replicate the filtering and aggregation logic directly within each model.
D. Create a snapshot to capture the pre-processed state of the source table.

Answer: A

Explanation:
Ephemeral models within CTEs allow for reusable intermediate steps in data transformation and are perfect for avoiding logic duplication across multiple models.

NEW QUESTION # 219
Consider these SQL and YAML files for the model model_a:
models/staging/model_a.sql
{{ config(
materialized = "view"
) }}
with customers as (
...
)
dbt_project.yml
models:
my_new_project:
+materialized: table
staging:
+materialized: ephemeral
Which is true about model_a? Choose 1 option.
Options:

A. Select statements made from the database on top of model_a and transformation processing within model_a will be quicker, but the data will only be as up to date as the last dbt run.
B. Select statements made from the database on top of model_a will be quicker, but the data will only be as up to date as the last dbt run.
(Note: A and D are duplicates - typical exam formatting.)
C. Select statements made from the database on top of model_a will be slower, but the data will always be up to date.
D. Select statements made from the database on top of model_a will result in an error.

Answer: C

Explanation:
model_a contains an in-model config explicitly setting:
{{ config(materialized = "view") }}
In dbt, in-model config overrides project-level config, including folder-level defaults. Even though the staging/ folder is configured as:
staging:
+materialized: ephemeral
...this does not apply because the in-file config has higher precedence.
So model_a is materialized as a view.
A view in dbt is not persisted as data; instead, it stores the SQL definition. When a downstream query selects from that view, the database executes the underlying SQL at query time. As a result:
* Queries run slower than selecting from a table, because the underlying computation happens each time.
* But the data is always up to date, because views read directly from the current underlying tables.
This matches Option C.
Why the other options are wrong
* A & D describe table behavior (faster queries, data only as fresh as last run). model_a is not materialized as a table.
* B is incorrect because views are fully queryable; no error occurs.

NEW QUESTION # 220
You're using the dbt-utils.get_column_values macro to list out the unique values from a specific source column in the documentation. However, the generated docs don't display any results. Which of the following reasons are most likely?

A. The macro itself might have a bug or an issue with how it's being used
B. There is a limit on how many unique values the macro can display within the documentation.

Answer: A,B

Explanation:
A: Some macros are designed specifically for model columns and may not function as expected with source columns. B: Macros might depend on data from a recent dbt run operation to generate their output. C: It's always possible that the macro has a bug or your implementation is not quite correct.

NEW QUESTION # 221
A dbt deployment pipeline fails on the production environment, yet everything works as intended in development and staging. Which areas would you investigate when troubleshooting?

A. Verify the credentials and connection details between production and other environments in your profiles-yml.
B. Ensure consistency of dependency versions across different environments-
C. Consider schema differences, if any between the production data warehouse and other environments.
D. Check if all required permissions are granted to the dbt user on the production database.

Answer: B

Explanation:
A: Connection errors lead to deployment failures. B: Mismatched schemas can break models. C: Lack of permissions prevents dbt from performing its job. D: Inconsistent dependency versions across environments can sometimes introduce subtle differences in behavior, leading to production-specific issues.

NEW QUESTION # 222
A model intermittently fails. Sometimes it runs without errors. Which category of issue should you suspect FIRST?

A. Errors within complex macros used by the model, triggered only under specific data conditions.
B. dbt-specific configuration errors causing unpredictable behavior at the engine level.
C. Data quality issues in the source that are not consistently present.
D. Race conditions due to models in your DAG running concurrently without proper synchronization.

Answer: C

Explanation:
Inconsistent failures often suggest data dependency problems. While the others could occur, start with potential data quality concerns.

NEW QUESTION # 223
......

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