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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: Testing and Data Quality	- Data validation strategies 1. CI-based validation workflows 2. Schema testing practices - Built-in and custom tests 1. Generic tests (unique, not null, relationships) 2. Custom SQL tests
Topic 2: Documentation and Lineage	- Data lineage understanding 1. Dependency tracking with ref() 2. Directed acyclic graph (DAG) - dbt documentation system 1. Auto-generated docs site 2. Model descriptions and metadata
Topic 3: Deployment and Orchestration	- Running dbt in production 1. CI/CD integration patterns 2. dbt Cloud and job scheduling - Environments and workflows 1. Version control with Git 2. Development vs production environments
Topic 4: 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 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. Role of dbt in analytics engineering 2. Warehouse-centric transformation workflows

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q293-Q298):

NEW QUESTION # 293
(Multiple Select)

A. Switch the model materialization from 'view' to 'table'.
B. Add intermediate CTEs within the model's SQL to inspect data at different transformation stages.
C. Implement a custom data test to specifically check the column for null values.
D. Examine the source data for the model to verify the presence of expected values.

Answer: B,D

Explanation:
Verifying source data and stepwise debugging are key. Tests help but come later. Changing the materialization is unlikely to directly address the null-value issue.

NEW QUESTION # 294
Consider this DAG for a dbt project. You have configured your environment to use one thread.
When running dbt run, you determine that model_d fails to materialize.

How will changing the command from dbt run to dbt run --fail-fast impact the execution of dbt run when model_d fails to materialize? Choose 1 option.
Options:

A. dbt will attempt to materialize the rest of the models.
B. dbt will drop the existing version of model_d in the data platform.
C. dbt will attempt to materialize everything else except for model_f.
D. dbt will stop building any additional nodes in the DAG.

Answer: D

Explanation:
The --fail-fast flag changes dbt's execution behavior by immediately stopping the scheduling of any further models as soon as the first failure occurs. This is true regardless of the number of threads; in this case, the environment uses one thread, meaning models run strictly in sequence based on dependency order.
In the DAG provided, model_d is downstream of model_b, and model_f depends on model_d. The execution sequence when using a single thread would be something like:
* model_a runs
* model_c runs
* model_e runs
* model_b runs
* model_d runs # fails
Once model_d fails:
* With normal dbt run, dbt would attempt to continue running any unrelated models (though in this DAG, there are no more independent nodes left anyway).
* With --fail-fast, dbt immediately stops scheduling any additional work, halting the run.
Therefore, no further models-including model_f or any other-will run. dbt does not drop models, retry them, or attempt related branches. It simply stops.

NEW QUESTION # 295
You've made several commits to your feature branch. Before merging into the main branch, you want to consolidate the changes into a single, clear commit. What Git technique would help achieve this?

A. Run git rebase main followed by git commit -amend.
B. Use git rebase -i (interactive rebase) to squash commits together.
C. Manually copy your files into a new branch, commit, and issue a pull request.
D. git cherry-pick specific commits from your branch and reapply them.

Answer: B

Explanation:
Interactive rebasing (git rebase -i) provides fine-grained control to squash, edit, or rearrange commits, creating a cleaner commit history

NEW QUESTION # 296
You encounter the following error when running dbt compile:Runtime Error in model customer _ churn (models/transformations/customer_churn.sql)

A. The fct_orders model depends on other models that haven't compiled successfully.
B. You need to add a schema test to validate the existence of the fct orders table.
C. There's a typo in how you're referencing the fct_orders object in your SQL.
D. The fct_orders source is not configured correctly in your dbt_project.yml file.

Answer: C

Explanation:
Explanation: This is a runtime error indicating incorrect usage of fct_orders. Options A and C could cause errors but of a different nature. Schema tests won't fix a referencing error.

NEW QUESTION # 297
You define a source table and include an override:YAML

A. You need to create a macro named 'my_project' that contains the specific overriding logic.
B. The overrides property is not valid for tables within your dbt_project.yml.
C. The overrides property needs to be defined at the source level, not the table level.
D. The value 'my_project' is not a recognized keyword by dbt.

Answer: C

Explanation:
While dbt supports overrides, they generally work at higher levels within your project file, not at the model level.

NEW QUESTION # 298
......

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