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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. Warehouse-centric transformation workflows 2. Role of dbt in analytics engineering
Topic 2: Deployment and Orchestration	- Running dbt in production 1. CI/CD integration patterns 2. dbt Cloud and job scheduling - Environments and workflows 1. Development vs production environments 2. Version control with Git
Topic 3: dbt Core Concepts	- Project structure and configuration 1. dbt_project.yml configuration 2. Packages and dependencies - Models and materializations 1. Ref and source functions 2. Views, tables, incremental models
Topic 4: Documentation and Lineage	- dbt documentation system 1. Auto-generated docs site 2. Model descriptions and metadata - Data lineage understanding 1. Dependency tracking with ref() 2. Directed acyclic graph (DAG)
Topic 5: Testing and Data Quality	- Data validation strategies 1. CI-based validation workflows 2. Schema testing practices - Built-in and custom tests 1. Generic tests (unique, not null, relationships) 2. Custom SQL tests

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q90-Q95):

NEW QUESTION # 90
You implement dbt tests to monitor data quality over time. Which of the following scenarios could these tests potentially help you detect?

A. An unexpected spike in web traffic leading to invalid data entries in your source system-
B. Performance issues in your data warehouse impacting the speed of dbt model updates.
C. An accidental schema change by someone working outside of your dbt project-
D. Gradual degradation in source data completeness due to an API rate limit being reached

Answer: A,C,D

Explanation:
A, C, and D relate to the changing quality of the data itself, which tests can surface. B (warehouse performance) is a separate issue.

NEW QUESTION # 91
(Multiple Select)

A. Switch to a different dbt package offering similar functionality.
B. Submit an issue to the package's GitHub repository if its open-source.
C. Temporarily override the buggy macro or model within your dbt project.
D. Update the packages_yml file to point to your forked version of the package.

Answer: B,C

Explanation:
Contributing to open-source is great, but you likely need an immediate fix. Overriding gives you control while addressing the issue upstream. Forking is feasible, but the maintenance burden might outweigh the benefits.

NEW QUESTION # 92
You're working in a highly collaborative environment. How could you manage contributions and updates to complex lineage macros to ensure maintainability and prevent conflicts?

A. Limit the number of people authorized to make changes to sensitive lineage macros-
B. Establish coding style guides and peer-review processes specifically for macros.
C. All of the above-
D. Utilize a robust version control system (e.g., Git) for macro code.

Answer: C

Explanation:
A Version control is essential for tracking changes and collaboratiom B: Style guides and reviews improve code quality and reduce errors. C: Controlled access can be important, while still allowing for collaboration through review processes.

NEW QUESTION # 93
You're setting up a new dbt project on a recently provisioned data warehouse. What actions are essential before your initial development begins?

A. Configuring monitoring and alerting to track data warehouse usage and events related to dbt jobs
B. Defining and implementing a schema and naming conventions for your dbt models within the data warehouse.
C. Establishing access controls and user roles specific to the dbt project's requirements.
D. All of the above

Answer: D

Explanation:
A: Organization is key for maintainability and collaboration. B: Secure access is fundamental from the outset C: Visibility into job execution and resource usage is crucial.

NEW QUESTION # 94
Several models perform similar filtering and aggregation on a frequently used source table. What's the most effective way to streamline this process and improve maintainability?

A. Replicate the filtering and aggregation logic directly within each model.
B. Create a table materialization to hold the pre-processed data.
C. Create a snapshot to capture the pre-processed state of the source table.
D. Create an ephemeral model to prepare the data in a common format, referenced by downstream models.

Answer: D

Explanation:
Ephemeral models within CTEs allow for reusable intermediate steps in data transformation and are perfect for avoiding logic duplication across multiple models.

NEW QUESTION # 95
......

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