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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q193-Q198):

NEW QUESTION # 193
You've added a new exposure to your project, but it doesn't appear in dbt docs. Here's part of your dbt_project.yml:YAML

A. The maturity property is missing within your exposure's configuration.
B. Exposures of type 'notebook' are not automatically detected by dbt docs generate.
C. You haven't run dbt docs serve after making changes to your project file.
D. The owner property is not correctly formatted as an email address.

Answer: B

Explanation:
dbt docs primarily works with model-based exposures. The others might be issues in specific cases, but check the exposure type first.

NEW QUESTION # 194
You define a new generic test on model customers in a YAML file:
version: 2
models:
- name: customers
columns:
- name: customer_id
tests:
- unique
- not_null
The next time your project compiles you get this error:
Raw Error:
mapping values are not allowed in this context
in "<unicode string>", line 7, column 21
What is the cause of this error?

A. unique and not_null should be indented at the same level as tests
B. tests should be wrapped in double quotes (")
C. unique and not_null should not be elements in a list
D. tests should be a dictionary key, not a list

Answer: A

Explanation:
This error occurs because the YAML structure is incorrectly indented, causing dbt's parser (and YAML itself) to misinterpret the test definitions. In dbt, generic tests must be declared as a list under the tests: key, but YAML is extremely sensitive to indentation levels. In the faulty YAML, unique and not_null are indented incorrectly relative to the tests: key, which produces the error: "mapping values are not allowed in this context." According to the dbt Testing documentation, valid generic test syntax follows this exact pattern:
columns:
- name: id
tests:
- unique
- not_null
The indentation under tests: must be consistent and aligned so that YAML interprets the items as list elements, not as malformed mappings. When indentation is wrong, YAML attempts to parse list entries as key-value mappings, which leads to the error seen during compilation.
dbt does not require generic test names to be quoted, nor does it expect tests to be a dictionary. The test list format is correct-only the indentation is wrong. Therefore, the root cause is incorrect YAML indentation, making Option D correct.

NEW QUESTION # 195
You need to set up a production-like environment for load testing and performance tuning of dbt models. How might you approach the resource allocation and provisioning of this environment?

A. Partner with your data infrastructure team to get guidance based on their experience.
B. Create a CI/CD job that dynamically scales a cloned production environment for the test, then scales it back down.
C. Write dbt macros that artificially increase dataset sizes in development for simulating load.
D. Run performance tests directly against production during a designated off-peak period.

Answer: A,B

NEW QUESTION # 196
You suspect discrepancies in a critical model. To troubleshoot, you need to understand the order of model execution, but the DAG is complex. What would be the BEST strategy to get this information?

A. Run dbt docs generate and inspect the generated documentation.
B. Visually inspect the manifest.json file.
C. Manually trace the dependencies in your model files.
D. Use dbt Is -select

Answer: A

Explanation:
While other options provide some information, dbt docs generate creates comprehensive documentation including a visual DAG to show execution flow clearly.

NEW QUESTION # 197
Which types of transformations are often best suited for raw data as an initial step in your dbt project?

A. Applying business logic and complex aggregations for reporting purposes.
B. Casting and typecasting of columns to ensure consistent data types-
C. Both A and C-
D. Renaming columns and performing basic data cleaning

Answer: C

Explanation:
A: Consistent data types across sources are essential for further modeling. C Initial cleaning and renaming improve the usability of raw data.

NEW QUESTION # 198
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