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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q188-Q193):

NEW QUESTION # 188
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. Since freshness is defined at the source level, all tables in the source must adhere to the freshness parameters.
B. Add error_after: null to the table configuration.
C. Add loaded_at_field: 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 # 189
(Multiple Select)

A. Integrating dbt with your CI/CD tool and using job-specific triggers or filters.
B. Scheduling less frequent jobs using dbt Cloud's built-in job scheduler
C. Using separate dbt profiles for production and development environments
D. Implementing pre-hooks and post-hooks to perform tasks before and after dbt model execution.

Answer: A,B,C

Explanation:
A enables environment-specific commands. B provides a way to launch different jobs. C handles scheduled tasks within dbt Cloud.

NEW QUESTION # 190
You want to automatically deploy updates to your documentation site hosted on a cloud service whenever changes to your dbt project are committed to your main branch. Which of the following is essential for this setup?

A. Write a custom script to check for project changes and handle deployment.
B. Configuring dbt Cloud to trigger documentation deploys.
C. Implementing a CI/CD pipeline that includes dbt build steps.
D. Enabling webhooks in your Git repository provider.

Answer: A,C,D

Explanation:
A). A CI/CD pipeline is needed to automate running dbt commands and the deployment process. B: Webhooks allow your CIICD system to get notified when changes are pushed to your repository, triggering the pipeline. D: A custom script could provide flexibility for check conditions and deployment logic-

NEW QUESTION # 191
A model passes its tests during development, but later fails when deployed on a less powerful production database. Which of the following might be the reason for this discrepancy?

A. Performance-related test thresholds are too lenient in development.
B. There's a bug in the version of dbt that runs in production.
C. The test was accidentally disabled in the production dbt project.
D. Source data distributions differ significantly between environments.

Answer: A,D

Explanation:
A and B could lead to tests passing in a forgiving environment but not catching issues in a more resource- constrained one. C would likely be caught earlier, and D is less likely

NEW QUESTION # 192
You establish a process using dbt tests and snapshots to track data quality changes over time. To maximize the utility of this historical record for troubleshooting, what additional step would be beneficial?

A. Integrate metadata about dbt model versions and schema changes alongside the snapshots.
B. Schedule regular alerts whenever a test result deviates significantly from previous runs.
C. Convert the dbt test results and snapshots into time-series tables for easier trend analysis.
D. Automatically purge snapshots older than a certain date to save storage resources.

Answer: A,B,C

Explanation:
These enhance the historical context and make the data actionable for resolving issues- A might discard valuable information prematurely

NEW QUESTION # 193
......

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