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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q139-Q144):

NEW QUESTION # 139
Examine how freshness is defined at the database level:
- name: raw
database: raw
freshness: # default freshness
warn_after: {count: 12, period: hour}
error_after: {count: 24, period: hour}
loaded_at_field: _etl_loaded_at
How can one table from the source raw be excluded from source freshness?

A. Add loaded_at_field: null to the table configuration.
B. Since freshness is defined at the source level, all tables in the source must adhere to the freshness parameters.
C. Add error_after: null to the table configuration.
D. Add freshness: null to the table configuration.

Answer: D

Explanation:
In dbt, source freshness can be defined at the source level (affecting all tables) or overridden at the individual table level. When freshness is declared at the source root, dbt applies the freshness policy to every table within that source unless a specific table opts out. The official dbt documentation states that setting:
freshness: null
at the table level disables freshness checks for that specific table, even when the source itself defines default freshness rules. This is the only documented way to exclude an individual table from a source freshness policy.
Option B is incorrect because tables can override and disable freshness even when the parent source defines it. dbt's configuration hierarchy supports overriding child-level settings on a per-resource basis.
Option C (loaded_at_field: null) does not disable freshness-it simply removes the loaded_at_field, which would cause dbt to error because a freshness check requires a timestamp field to operate.
Option D (error_after: null) only removes the error threshold but does not disable freshness. The table would still participate in freshness checks and could trigger warnings.
Thus, the correct and dbt-supported method to exclude a table from freshness is to explicitly set:
freshness: null
making A the correct answer.

NEW QUESTION # 140
While working on a feature branch, you need to incorporate some critical bug fixes that were merged into the main branch. Which Git commands, or combination of commands, would you use?

A. git merge main
B. git checkout main followed by git pull
C. git rebase main
D. Both B and C are viable options.

Answer: A,C

Explanation:
B: Rebasing integrates the main branch's changes onto your feature branch, keeping a linear history. C: Merging directly creates a merge commit, making the history slightly less linear but often easier to track when features are integrated.

NEW QUESTION # 141
You're building tests for a dbt model that performs a join across multiple large datasets. You notice performance issues - tests are taking a very long time to run. What might be optimization strategies?

A. Create snapshots of the source tables and test on those snapshots instead.
B. Accept that tests on complex models will always be slow.
C. Focus on tests that can validate a subset of the data rather than the entire dataset.
D. Refactor model SQL to use incremental logic to avoid full table scans.

Answer: C,D

Explanation:
B and C target reducing the test's workload with sampling or optimizing the model itself. A might help under specific circumstances, but not always. D is a defeatist approach!

NEW QUESTION # 142
You need to test whether the values of a specific column have changed over time. What might be the best approach to implement this kind of test within your dbt project?

A. rely on native version history features in your data warehouse (if available).
B. Use a snapshot to preserve a point-in-time reference of the column.
C. Use a custom 'accepted_values' test together with a dbt seed.
D. Write a dbt macro to create a temporary table with historical data to compare against.

Answer: A,B,D

Explanation:
A provides a clean comparison point C enables more complex historical analysis. D leverages built-in warehouse features, if you have them.

NEW QUESTION # 143
The results of a dbt model contain values outside the expected range. You suspect an issue with Jinja templating. Where might this issue originate?

A. In the model's SQL, where you're using Jinja for calculations or dynamic logic.
B. All of the above.
C. In a macro used by the model.
D. Within a dbt_projectyml model or source configuration.

Answer: B

Explanation:
Jinja templating can exist in your project configuration files, macros, and the model SQL itself.

NEW QUESTION # 144
......

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