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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Creating and Maintaining Documentation	10%	- Documentation standards - Descriptions and metadata - Generating documentation
Topic 2: Developing dbt Models	20%	- Model design and structure 1. Materialization types 2. Ref and source functions 3. Incremental models - Model optimization 1. Model contracts 2. Performance tuning
Topic 3: Debugging and Error Resolution	15%	- Resolving data quality issues - Debugging techniques - Identifying modeling errors
Topic 4: Managing Data Pipelines	15%	- CI/CD integration - Deployment strategies - Pipeline orchestration
Topic 5: External Dependencies	10%	- Managing snapshots - External sources integration - Using packages
Topic 6: dbt Models Governance	15%	- Version control integration - Naming conventions and standards - Project organization and structure
Topic 7: Leveraging dbt State	5%	- State-aware operations - State management
Topic 8: Implementing dbt Tests	10%	- Test configuration and execution - Built-in tests - Custom tests

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q162-Q167):

NEW QUESTION # 162
You debug a production issue by running a model in a testing environment with additional logging output. Which dbt features or command-line additions would facilitate this?

A. Custom dbt flags (using -vars) to control the logging level of the SQL execution.
B. dbt hooks to redirect model-related logs to a centralized logging platform.
C. The -warn-error flag to treat warnings as errors, making your run fail louder on potential issues.
D. Environment variables to conditionally enable verbose logging macros inside the model.

Answer: A,C,D

Explanation:
These provide control during execution:A changes model behavior. B makes execution more sensitive. C alters dbt's log handling.

NEW QUESTION # 163
You wrote this test against your fct_orders model to confirm status filters were properly applied by a parent model:
{{
config(
enabled=true,
severity='error'
)
}}
select *
from {{ ref('fct_orders') }}
where status_code = 13
Which statement about this test is true?

A. This test will warn instead of erroring when you use the --warn-error flag.
B. Records with status_code = 13 will cause this test to fail.
C. The file must be saved in the tests/generic directory.
D. You must attach the test to the model in orders.yml for it to run.

Answer: B

Explanation:
This test is a singular test, meaning it is defined as a standalone SQL file placed inside the /tests directory (not attached through YAML like generic tests). dbt executes singular tests by running the SQL query, and the test fails if the query returns any rows. In this case, the test selects all records from fct_orders where status_code = 13. That means if even one row in fct_orders has a status code of 13, the test will fail, because dbt interprets returned rows as evidence of a data-quality issue.
The configuration block at the top sets severity='error', meaning the test raises an error when it fails, rather than a warning. This setting overrides default severity behavior, so option B is incorrect. Singular tests do not need to be attached in YAML because they run automatically when placed under /tests # making option A incorrect. Meanwhile, generic tests live in /tests/generic, but singular tests should live directly under /tests (option C incorrect).
Therefore, the only correct statement is that records with status_code = 13 will cause the test to fail, because the test query is explicitly written to identify such rows.

NEW QUESTION # 164
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. Write tests within the initial staging models that calculate and check the null percentage.
B. Create not_null tests on the raw source tables in a separate dbt project dedicated to source validation.
C. Designate some nulls as acceptable by extending the not_null test with the OR condition.
D. Include a data quality monitoring step in your upstream ETL process before data reaches dbt

Answer: A,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 # 165
You suspect discrepancies between a raw data source and its corresponding dbt representation. Which of the following would NOT directly help in pinpointing the cause of this mismatch?

A. Checking if any custom macros or tests are modifying the source data.
B. Comparing the dbt-generated SQL against the raw table's SELECT statement.
C. Reviewing the source definition within your dbt_project.yml .
D. Examining any source freshness warnings or errors in recent dbt runs.

Answer: D

Explanation:
While freshness warnings might hint at problems, they don't directly reveal data mismatches- Checking the actual SQL against the raw source and project configurations is key.

NEW QUESTION # 166
You're working with a dataset where a column is expected to be updated frequently with newly arriving events. Which type of test would be MOST suitable to check that this process is working as intended?

A. A custom test comparing timestamps on the column in your dbt model to the current time.
B. A generic 'not_null' test on the column.
C. A test comparing the row count of the source table between different dbt runs.
D. A schema test on the column in the source table.

Answer: A

Explanation:
It focuses on freshness, which is the core concern in this scenario. The other options, while valid, don't target this specific assumption.

NEW QUESTION # 167
......

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