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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q357-Q362):

NEW QUESTION # 357
You've created a pull request, and a discussion in the code review reveals issues with the approach you used in your feature branch. Rather than continuously updating the original branch, what's a strategy to address the feedback while keeping a clean pull request history?

A. Directly modify the commits on your existing feature branch using git rebase -i.
B. Add the changes as a new commit to your feature branch, explaining the revisions in the pull request comments.
C. Close the original pull request and create a new branch with the revised implementation.
D. Request that your collaborators edit the code directly within the pull request interface.

Answer: C

Explanation:
A: A new branch often provides the cleanest history for the reworked feature. B: Altering existing commits should be used judiciously, as it 'rewrites' history and can cause problems for collaborators.

NEW QUESTION # 358
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 result in an error.
D. Select statements made from the database on top of model_a will be slower, but the data will always be up to date.

Answer: D

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 # 359
(Multiple Select)

A. Instruct dbt to rename columns rather than dropping and recreating them during updates.
B. Avoid refactoring dbt models that are directly queried by external tools.
C. Establish a naming convention in dbt that external tools can rely upon.
D. Create views on top of dbt model tables, providing a stable abstraction layer for the Bl tools.

Answer: D

Explanation:
B decouples the tools from the underlying model. A is database dependent, C is fragile, D inhibits improvement

NEW QUESTION # 360
Which two configuration items can be defined under models: in your dbt_project.yml file?
Choose 2 options.

A. source
B. tags
C. target
D. schema
E. test

Answer: B,D

Explanation:
The correct answers are A: schema and C: tags.
In dbt, the dbt_project.yml file is the central configuration file that defines model-level settings. Under the models: section, you can specify a wide range of model configurations such as schema, materialized, tags, alias, and custom meta fields. The schema configuration allows you to control which database schema a model should be built in, giving analytics engineers the flexibility to organize models by domain or environment. The tags configuration is also valid under models: and is widely used to group models logically for selection, documentation, or orchestration workflows.
Option B (source) is incorrect because sources are defined under YAML files in the sources: section, not under models: in dbt_project.yml. Option D (test) is incorrect because tests must be defined in model or source YAML files, not inside the project configuration. Option E (target) is not a configuration that applies to models; rather, it refers to dbt runtime environments and cannot be configured under the models: block.
dbt's project configuration system ensures that model-level behavior is managed centrally and consistently, and schema and tags are two of the officially supported configuration keys under models:.

NEW QUESTION # 361
Data Quality Issue

A. Run dbt test on the model and see if existing tests fail, indicating a possible regression.
B. Run dbt compile followed by dbt run -select model_name to isolate the model and observe its output closely.
C. Inspect any filters or transformations within the model's SQL that might be unintentionally excluding data.
D. Add specific schema tests to the model, asserting that the column in question should not contain nulls.

Answer: A,B,C,D

Explanation:
This requires a comprehensive approach covering logic errors, regression tests, proactive quality checks, and isolated execution.

NEW QUESTION # 362
......

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