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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	- Environments and workflows 1. Development vs production environments 2. Version control with Git - Running dbt in production 1. CI/CD integration patterns 2. dbt Cloud and job scheduling
Topic 3: Testing and Data Quality	- Data validation strategies 1. CI-based validation workflows 2. Schema testing practices - Built-in and custom tests 1. Custom SQL tests 2. Generic tests (unique, not null, relationships)
Topic 4: dbt Core Concepts	- Project structure and configuration 1. Packages and dependencies 2. dbt_project.yml configuration - Models and materializations 1. Views, tables, incremental models 2. Ref and source functions
Topic 5: 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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q286-Q291):

NEW QUESTION # 286
You're integrating a new API-based data source. Select the most suitable way to incorporate this into your dbt project.

A. Create a seed file to load the data directly into a table.
B. Create a source with a loaded_at_field and write custom logic to snapshot API data.
C. Utilize a dbt package tailored for the specific API, if available.
D. Write a dbt macro to encapsulate the API interaction logic.

Answer: C

Explanation:
Explanation: Pre-built packages streamline the integration of common APIs. Options A or B are workarounds, and D might be part of a custom solution but not the primary approach.

NEW QUESTION # 287
You define several sources. However, some are frequently updated, while others rarely change. How can you optimize your dbt run command to account for this difference?

A. Set up dbt schedules within your dbt_project.yml with different intervals for each group.
B. Include the -exclude flag along with less-frequently updated source names.
C. Employ the defer option if downstream models permit and ensure freshness checks are adjusted.
D. Group sources based on update frequency and create separate dbt run commands for each group.

Answer: D

Explanation:
Separating dbt run commands offers immediate control without using advanced features like schedules or deferring. The other options have merit in specific scenarios.

NEW QUESTION # 288
You've identified the same complex date calculations repeated across multiple models. Which approach aligns with modularity and DRY (Don't Repeat Yourself) principles?

A. Introduce a new intermediate table to store the result of the date calculations.
B. Write a custom Python function and import it into your dbt project.
C. Continue copying the date calculation logic into each relevant model.
D. Create a macro, encapsulating the date calculations, and reference it in the affected models.

Answer: D

Explanation:
Macros excel in encapsulating reusable logic, promoting modularity and avoiding code repetition. While option C could work, it introduces unnecessary materialization.

NEW QUESTION # 289
You discover a downstream model, dim products, references an outdated raw dataset, raw_product_archive, intended for historical analysis and not active products. What's the most appropriate corrective action?

A. Add a schema test to dim_products to catch potential errors.
B. Delete the raw_product_archive source, so it can't be used accidentally.
C. Leave it as-is since it's not actively causing any errors currently.
D. Update the dim_products model to reference the active product dataset.

Answer: D

Explanation:
Directly addressing the root cause ensures your model uses the correct source. Schema tests are safeguards but don't resolve the underlying dependency issue. Be cautious about deleting sources unless there's a very strong reason.

NEW QUESTION # 290
(Multiple Select)

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

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 # 291
......

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