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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Transformation Techniques	25%	- Refactoring and incremental models - Macros and packages - Common table expressions and subqueries - Jinja templating
Topic 2: dbt Fundamentals	15%	- dbt workflow and best practices - dbt Core vs dbt Cloud - dbt project structure
Topic 3: Testing and Documentation	20%	- Custom data tests - Schema tests (unique, not_null, accepted_values, relationships) - dbt docs and DAG visualization - Documentation generation
Topic 4: Deployment and Orchestration	15%	- Jobs and scheduling in dbt Cloud - Git version control integration - CI/CD with dbt Cloud - Environments (dev, staging, prod)
Topic 5: Models	25%	- Seeds - Snapshots - Writing and managing SQL models - Sources and references - Materializations (table, view, ephemeral, incremental)

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
(Multiple Select)

A. Columns intended to contain timestamps actually store valid timestamp values.
B. Foreign key relationships are consistently enforced.
C. Tables intended to be empty as part of a process truly have zero records.

Answer: A,B,C

Explanation:
Each of these represents a common assumption about source data quality, and any violation could break your dbt models or produce incorrect results.

NEW QUESTION # 13
You're considering adding workflow orchestration to manage dependencies between your dbt project, data loading tasks, and the triggering of downstream Bl updates. Which of the following types of tools would be most suitable for this?

A. Data lineage tools
B. General-purpose workflow orchestration tools (e.g., Airflow, Prefect)
C. Data quality monitoring tools
D. CI/CD platforms

Answer: B

Explanation:
While options B (CIICD) and C (data quality monitoring) are potentially relevant parts of the ecosystem, general-purpose workflow orchestration tools are directly designed to manage dependencies and task sequences across various technologies.

NEW QUESTION # 14
Your fct_orders model depends on both raw_sales and stg_customers. Due to upstream changes, the customer_id column in raw_sales is now named cust_id. How could you manage this change with minimal disruption?

A. Create an alias for customer_id as cust_id in the stg_customers model.
B. Update the fct_orders model to reference cust_id.
C. Utilize dbt's ref() function to dynamically adapt to the new column name.
D. Use a dbt source to rename the column in raw sales.

Answer: C

Explanation:
The ref() function is designed for these situations. It makes your models adaptable to upstream changes. Option A would work but creates maintenance overhead.

NEW QUESTION # 15
A downstream process relies on a critical model to be fully refreshed daily. How can you enforce this behavior using dbt_project.yml configuration?

A. Configure a schedule property within the models: section.
B. Apply a post-hook that triggers a full refresh of the model.
C. Set the model's materialized property to incremental _ refresh-
D. Update the model configuration to materialized: table and add a persist_docs: true property.

Answer: B

Explanation:
Post-hooks enable you to run operations after a model executes. The other options don't provide the required control over the refresh behavior.

NEW QUESTION # 16
You maintain a large dbt project with various reporting models that source data from a set of core business tables. These core tables often include columns with sensitive information. What technique could ensure compliance and maintainability when restricting access to this data?

A. Write complex WHERE clauses in each reporting model to exclude rows containing sensitive data.
B. Create incremental models that directly remove the sensitive columns before reporting models utilize them.
C. Apply row-level security within the data warehouse itself for fine-grained control.
D. Use views to create different projections of the core tables, exposing only the necessary columns.

Answer: D

Explanation:
Views provide a modular and maintainable way to define access restrictions. This approach centralizes the filtering logic, preventing repeated and potentially inefficient WHERE clauses across models.

NEW QUESTION # 17
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