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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: Deployment and Orchestration	- Running dbt in production 1. dbt Cloud and job scheduling 2. CI/CD integration patterns - Environments and workflows 1. Version control with Git 2. Development vs production environments
Topic 2: Testing and Data Quality	- Built-in and custom tests 1. Generic tests (unique, not null, relationships) 2. Custom SQL tests - Data validation strategies 1. Schema testing practices 2. CI-based validation workflows
Topic 3: dbt Core Concepts	- Models and materializations 1. Ref and source functions 2. Views, tables, incremental models - Project structure and configuration 1. dbt_project.yml configuration 2. Packages and dependencies
Topic 4: Documentation and Lineage	- dbt documentation system 1. Auto-generated docs site 2. Model descriptions and metadata - Data lineage understanding 1. Dependency tracking with ref() 2. Directed acyclic graph (DAG)
Topic 5: Analytics Engineering Foundations	- SQL proficiency for analytics 1. Data modeling in SQL 2. Joins, aggregations, and window functions - Modern data stack concepts (ELT vs ETL) 1. Warehouse-centric transformation workflows 2. Role of dbt in analytics engineering

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q181-Q186):

NEW QUESTION # 181
(Multiple Select)

A. Your data warehouse has time-based pricing tiers, and you need models to refresh during off-peak hours.
B. You need to frequently load incremental data using dbt's snapshot feature.
C. Downstream Bl dashboards rely on up-to-date data transformed by your dbt models-
D. Upstream data sources update on a predictable cadence (e.g., daily extracts).

Answer: C,D

Explanation:
B aligns model runs with data availability. C ensures Bl dashboards reflect the latest data. A is cost optimization, not a core freshness requirement. D snapshots are more about change tracking than regular updates.

NEW QUESTION # 182
You are building an incremental model.
Identify the circumstances in which is_incremental() would evaluate to True or False.

Answer:

Explanation:

Explanation:
1. There is a config block at the top of the model with materialized = 'table'
## Answer: False
2. The corresponding table already exists in the data warehouse
## Answer: True
3. The incremental model was executed with the --full-refresh flag
## Answer: False
4. The corresponding table does not already exist in the data warehouse
## Answer: False
The is_incremental() macro in dbt is used within an incremental model to determine whether the current invocation should perform an incremental update or a full rebuild. It evaluates to True only during an incremental run when the target table already exists in the data warehouse and dbt is not performing a full refresh.
For is_incremental() to return True, two conditions must be met:
* The model must be materialized as incremental, not table or view.Therefore, if the config specifies materialized = 'table', the model is not incremental, and the function evaluates to False.
* The table already exists in the target schema.In this case, dbt will run the model in incremental mode, making is_incremental() return **True`.
If the incremental model is run with --full-refresh, dbt intentionally rebuilds the entire table, meaning is_incremental() evaluates to False, even if the table exists.
Lastly, if the target table does not exist yet, dbt must create it from scratch, so the run is treated as a full rebuild, not an incremental update, causing is_incremental() to evaluate to False.
Thus, the only scenario where is_incremental() returns True is when the table exists and the model is actually incremental.

NEW QUESTION # 183
You run this command:
dbt build --select "source_status:fresher+" --state path/to/prod/artifacts Which two need to happen before it can be executed successfully?
Choose 2 options.

A. Invoke either dbt run or dbt build.
B. Define a freshness block on your source(s).
C. Test your sources with dbt test.
D. Add generic tests to your sources.
E. Invoke the command: dbt source freshness.

Answer: B,E

Explanation:
The selector source_status:fresher+ depends entirely on freshness metadata created by a previous dbt source freshness run. Source freshness results are stored in artifacts, and the --state flag points dbt to the location where these artifacts were previously generated. Without first running dbt source freshness, dbt has no stored freshness status and therefore cannot filter sources by "fresher" status. This makes option A required.
Additionally, source freshness checks only work when sources include a freshness block in their YAML configuration. This block specifies the loaded_at_field and the warn_after / error_after thresholds. If no freshness block exists, dbt does not compute freshness at all, meaning such sources cannot appear in the source_status:fresher+ selector. Therefore D is also required.
Options B and E are unrelated to source freshness artifacts and do not contribute to the preconditions for this selector. Option C does not impact freshness logic, as generic tests have no role in generating freshness metadata.
Thus, the two prerequisites are:
* Running dbt source freshness (to create freshness metadata).
* Defining a freshness configuration on the relevant sources.

NEW QUESTION # 184
You have strict data retention policies. Which factors might influence how data is managed differently across your production, development, and raw data environments?

A. Regulatory requirements could mandate that production data be retained for longer periods.
B. Development data might have less stringent retention requirements, allowing for more frequent deletion-
C. All of the above.
D. Raw data might be archived or deleted following a shorter retention period than production data

Answer: C

Explanation:
A: Raw data can often be discarded post-processing, reducing storage costs. B: Regulations often dictate long-term retention for production data. C: Development data is often transient and subject to less strict retention.

NEW QUESTION # 185
You discover that dbt models create objects with the default database user's permissions. However, a policy mandates that analytics objects must be owned by a dedicated, less privileged account. How could you adapt your dbt workflow?

A. Configure the target database to automatically assign desired ownership to new objects in the schema
B. Implement pre-hooks or post-hooks to execute ALTER OWNER statements after model creation.
C. Modify your dbt profile to use the less privileged account's credentials during job execution.
D. Write dbt macros that contain permission management logic as part of the model deployment.

Answer: B,C

Explanation:
A gives direct control within dbt C changes what dbt "does" by running as a different user- B might exist but be too broad, D overcomplicates models.

NEW QUESTION # 186
......

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