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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
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)
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
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
Deployment and Orchestration	- Environments and workflows 1. Version control with Git 2. Development vs production environments - Running dbt in production 1. dbt Cloud and job scheduling 2. CI/CD integration patterns
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. Warehouse-centric transformation workflows 2. Role of dbt in analytics engineering

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dbt Labs dbt Analytics Engineering Certification Exam 認定 dbt-Analytics-Engineering 試験問題 (Q62-Q67):

質問 # 62
You discover a vulnerability in a third-party lineage-enhancing dbt package you use. Where might you first look for information and potential fixes?

A. Centralized dbt documentation or a discussion forum.
B. Directly in the manifest.json or packages.yml files within your project
C. Only the authors of the dbt package would be able to address the vulnerability.
D. The GitHub repository or documentation of the dbt package.

正解：D

解説：
The package maintainer is the primary source for updates and vulnerability fixes. However, other resources like A & C might provide additional information or community solutions.

質問 # 63
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. Create a macro, encapsulating the date calculations, and reference it in the affected models.
B. Introduce a new intermediate table to store the result of the date calculations.
C. Write a custom Python function and import it into your dbt project.
D. Continue copying the date calculation logic into each relevant model.

正解：A

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

質問 # 64
You're integrating a new dbt package and find its models conflict with naming conventions in your existing project. Which approach generally provides the most flexible solution?

A. Contact the package maintainers to request changes aligning with your conventions.
B. Manually edit the SQL within the conflicting package models.
C. Utilize the dbt_project.yml file to re-alias the models provided by the package.
D. Override the package's macros and redefine them within your project.

正解：C

解説：
Re-aliasing at the project level offers a centralized and maintainable way to manage naming conflicts. Editing package code directly should be avoided if possible.

質問 # 65
A team member suggests building a dbt package for a set of reusable transformations your team frequently uses. When is this MOST likely a good idea?

A. The transformations are complex and involve domain-specific logic that might be beneficial to others.
B. The transformations are relatively simple and could be written as macros.
C. The transformations are project-specific but used across multiple models.
D. You need to limit access to these transformations to a specific team within your organization.

正解：A

解説：
Sharing common, complex logic is a key reason for creating packages. Simple logic is better as macros. Project-specificity favors macros or CTEs. Packages arent ideal for access control.

質問 # 66
You need to set up a production-like environment for load testing and performance tuning of dbt models. How might you approach the resource allocation and provisioning of this environment?

A. Partner with your data infrastructure team to get guidance based on their experience.
B. Write dbt macros that artificially increase dataset sizes in development for simulating load.
C. Run performance tests directly against production during a designated off-peak period.
D. Create a CI/CD job that dynamically scales a cloned production environment for the test, then scales it back down.

正解：A、D

質問 # 67
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