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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q89-Q94):

NEW QUESTION # 89
(Multiple Select)

A. You want to test different loading strategies for a large source table.
B. You need to apply custom column-level transformations to a raw source.
C. The source data is sensitive and requires role-based access control.
D. The source has multiple tables with related schemas requiring standardization.

Answer: B,D

Explanation:
yml files allow for complex source schemas and column transformations. Access control and loading strategies are usually handled at the database or project-level configuration.

NEW QUESTION # 90
After configuring a dbt package, you notice its models depend on a source table absent from your warehouse. How do you typically address this?

A. Update your source definitions to include the required table.
B. Rewrite the package's model from scratch within your project to remove the dependency.
C. Override the package model's SQL query to reference an existing table in your warehouse.
D. File an issue or submit a pull request to the package's repository.

Answer: C

Explanation:
Start by fulfilling the package's dependencies. If the table is truly generic, then customizing the SQL (B) might be the next step.

NEW QUESTION # 91
You need to create a model that combines data from a large fact table with smaller dimension tables. Performance is paramount, and the data in the fact table updates incrementally but frequently. Which materialization strategy is likely to provide the optimal balance of efficiency and freshness?

A. Materialize the fact table as an incremental model and the dimension tables as views.
B. Materialize the fact table as a table and the dimension tables as incremental models.
C. Materialize all tables (fact and dimensions) as views.
D. Materialize all tables (fact and dimensions) as tables.

Answer: A

Explanation:
This approach leverages the strengths of different materializations. Incremental updates to the large fact table minimize processing, while views on the smaller dimension tables avoid unnecessary materialization costs.

NEW QUESTION # 92
Your company wants to standardize pull request templates. Which information would be beneficial to include in such a template?

A. Specific instructions for the reviewer, if certain aspects need special attention.
B. A section to link to relevant design documents or specifications.
C. All of the above.
D. Checkboxes for confirming that tests have been run and documentation has been updated if necessary.

Answer: C

Explanation:
A: Checklists ensure core quality steps are followed. B: Linking design documents provides context for reviewers. C: Guidance helps reviewers focus their feedback.

NEW QUESTION # 93
dbt Model Snippet

A. The presence of filters on the source fct_orders or dim_customers.
B. The ref() functions arent correctly resolving dependencies.
C. The logic contained within a custom macro.
D. The underlying data types of o.customer_id and c.customer_id mismatch.

Answer: A

Explanation:
Filters on sources frequently cause missing rows in joins. Check your source definitions and any model- level filtering. While the others could be issues, start with the most common cause.

NEW QUESTION # 94
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