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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q141-Q146):

NEW QUESTION # 141
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. Create temporary branches in your dbt projects to isolate upgrade-related code changes for each environment
B. Thoroughly test all dbt models across your staging and development environments to identify necessary updates.
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 # 142
You want to implement a macro that displays a warning icon next to models in the DAG that haven't been tested. Which of the following would be necessary to make this work?

A. The ability for the macro to query the results of test executions from the dbt run-
B. Configuration in your dbt_projectyml file to associate models with the relevant tests.
C. All of the above.
D. A custom CSS class for the warning icon and a way to apply it dynamically using the macro.

Answer: C

Explanation:
A: You'll need CSS to style the warning icon- B. The macro needs to determine if the associated tests for each model have passed or failed. C: dbt needs a way to understand which tests are related to which models for the macro to function correctly.

NEW QUESTION # 143
You want to display the runtime metrics (e.g., execution time) of each model directly on the DAG node. Which challenges might you face when implementing this?

A. dbt documentation is primarily static; it might not be able to render dynamic metrics that change with each execution.
B. All of the above
C. Retrieving runtime metrics for each historical run might not be natively supported by dbt
D. Adding too much detailed information directly to the DAG could lead to visual clutter and make it overwhelming.

Answer: B

Explanation:
A: You might need to generate static summaries or find workarounds to display dynamic metrics. B. Collecting runtime metrics across all historical runs might require additional logging or integration with dbt artifacts (manifestjson, run_resultsjson)- C: The DAG visualization needs a balance between information and readability.

NEW QUESTION # 144
You encounter an unexpected dbt run error. Rather than re-running all models, you want to resume from the point of failure. Which of the following, if any, might allow for this?

A. Using dbt snapshots to track model states, allowing you to rollback changes as needed
B. The --fail-fast flag instructs dbt to only execute successful models, making restarts unnecessary
C. dbt Cloud's job retry features for automatically resuming failed job executions.
D. dbt unfortunately lacks native support for resumable jobs.

Answer: C

Explanation:
C can retry in event of transient errors, but not fundamental code problems. The rest are incorrect dbt typically isn't resumable mid-job.

NEW QUESTION # 145
(Multiple Select)

A. The cost-efficiency of the development environment solution.
B. The need for feature parity (functions, syntax) with the production warehouse.
C. The technology used for your production data warehouse.
D. The overall size and complexity of your dbt project

Answer: A,B,C

Explanation:
A ensures compatibility and consistent developer experience. B avoids excessive development costs. C prevents surprises when porting code- D influences the scale of resources you need in development.

NEW QUESTION # 146
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