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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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Use the Latest dbt Labs dbt-Analytics-Engineering Questions to pass your Certification Exam

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q278-Q283):

NEW QUESTION # 278
You get the following error when compiling a model:ln 'models/marts/sales_report.sql':

A. Introduce a unit test to assert that the model's output has no duplicate rows.
B. Examine your model's SQL and ensure you've defined a column or combination of columns to serve as a unique identifier.
C. Add a config.get("unique_key") entry within your model configuration in dbt_project.yml.
D. Delete the existing model and create a new one with the materialization set to table.

Answer: B

Explanation:
Incremental models must have a unique key You must address this in the model's logic itself. Changing the materialization or testing wont solve the root cause.

NEW QUESTION # 279
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. Create an ephemeral model to prepare the data in a common format, referenced by downstream models.
B. Create a table materialization to hold the pre-processed data.
C. Replicate the filtering and aggregation logic directly within each model.
D. Create a snapshot to capture the pre-processed state of the source table.

Answer: A

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 # 280
You have a dbt macro that generates a list of models to execute dynamically. Which dbt command should you use in conjunction with this macro to actually run those models?

A. dbt Is to retrieve project metadata and feed it into your custom execution logic.
B. dbt parse to pre-compile the project graph, allowing model selection by reference.
C. dbt debug to step through model execution while using the macro's output
D. dbt build to create a task graph, extracting model nodes, and running them as needed.

Answer: B

Explanation:
A makes the dbt graph available, including macros, during execution- B gets metadata, but not for execution. C helps troubleshoot, but not run macros. D isn't designed for dynamic macro output

NEW QUESTION # 281
You're using dbt Cloud and want to tighten control over when production deployments can occur. What dbt Cloud feature, in conjunction with your CI/CD configuration, would allow you to enforce this?

A. Environment variables for sensitive credentials.
B. Deployment jobs and schedules.
C. dbt Cloud's built-in source freshness checks.
D. Webhooks for triggering jobs by external events.

Answer: B

Explanation:
B enables you to define specific times or conditions for production deployments. Others are less directly related to controlling the timing.

NEW QUESTION # 282
You suspect discrepancies in a critical model. To troubleshoot, you need to understand the order of model execution, but the DAG is complex. What would be the BEST strategy to get this information?

A. Manually trace the dependencies in your model files.
B. Run dbt docs generate and inspect the generated documentation.
C. Use dbt Is -select
D. Visually inspect the manifest.json file.

Answer: B

Explanation:
While other options provide some information, dbt docs generate creates comprehensive documentation including a visual DAG to show execution flow clearly.

NEW QUESTION # 283
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