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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: Testing and Data Quality	- Built-in and custom tests 1. Custom SQL tests 2. Generic tests (unique, not null, relationships) - Data validation strategies 1. Schema testing practices 2. CI-based validation 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. Development vs production environments 2. Version control with Git
Topic 3: 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 4: dbt Core Concepts	- Models and materializations 1. Views, tables, incremental models 2. Ref and source functions - Project structure and configuration 1. dbt_project.yml configuration 2. Packages and dependencies
Topic 5: Analytics Engineering Foundations	- Modern data stack concepts (ELT vs ETL) 1. Role of dbt in analytics engineering 2. Warehouse-centric transformation workflows - SQL proficiency for analytics 1. Data modeling in SQL 2. Joins, aggregations, and window functions

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q267-Q272):

NEW QUESTION # 267
You've created a complex lineage macro, and you want to include unit tests for the macro logic itself. How might you approach this?

A. Currently, there is no standard way to directly unit test macros within a dbt project
B. Utilize dbt's built-in macro testing framework.
C. Include test cases for the macro within your model-level schema.yml files.
D. Write a Jinja script that programmatically tests various input/output scenarios of your macro.

Answer: D

Explanation:
While dbt doesn't have a dedicated macro testing framework, Jinja provides ways to construct tests. However, there's no built-in mechanism to run these as part of your typical dbt workflow.

NEW QUESTION # 268
(Multiple Select)

A. Tests comparing the latest data against historical baselines.
B. not_null and unique tests on primary key columns.
C. Tests checking for row count discrepancies between source systems and target tables.
D. Schema tests to confirm the existence of tables and columns.

Answer: B,C,D

Explanation:
A, B, and C focus on verifying basic data transfer and structural integrity. D is important, but comes after making sure the data is there at all_

NEW QUESTION # 269
You suspect discrepancies between a raw data source and its corresponding dbt representation. Which of the following would NOT directly help in pinpointing the cause of this mismatch?

A. Reviewing the source definition within your dbt_project.yml .
B. Examining any source freshness warnings or errors in recent dbt runs.
C. Checking if any custom macros or tests are modifying the source data.
D. Comparing the dbt-generated SQL against the raw table's SELECT statement.

Answer: B

Explanation:
While freshness warnings might hint at problems, they don't directly reveal data mismatches- Checking the actual SQL against the raw source and project configurations is key.

NEW QUESTION # 270
Your data warehouse environment undergoes a major version upgrade, introducing changes in SQL syntax and supported functions. How would you handle the migration of dbt projects to ensure compatibility?

A. Thoroughly test all dbt models across your staging and development environments to identify necessary updates.
B. Create temporary branches in your dbt projects to isolate upgrade-related code changes for each environment
C. All of the above.
D. Utilize custom macros to introduce conditional logic or abstract away syntax differences between versions, if feasible.

Answer: C

Explanation:
A: Branches facilitate staged testing and rollback, if necessary. B: Testing in lower environments uncovers potential issues before production deployment. C Macros can in some cases smooth over compatibility issues.

NEW QUESTION # 271
You're ready to integrate your feature branch into the main branch. Which scenarios would necessitate a pull request and code review process?

A. You are working in a collaborative team environment with established quality control practices.
B. You are the only contributor to the dbt project.
C. You are working on a personal dbt project.
D. In all dbt projects, pull requests should be mandatory.

Answer: A,D

Explanation:
C: Code reviews are crucial in collaborative settings for improving quality and sharing knowledge. D: While not strictly mandatory always, pull requests are a best practice even when working alone as they encourage structured change tracking-

NEW QUESTION # 272
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