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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
dbt Fundamentals	15%	- dbt Core vs dbt Cloud - dbt project structure - dbt workflow and best practices
Models	25%	- Snapshots - Writing and managing SQL models - Materializations (table, view, ephemeral, incremental) - Sources and references - Seeds
Testing and Documentation	20%	- Documentation generation - Schema tests (unique, not_null, accepted_values, relationships) - Custom data tests - dbt docs and DAG visualization
Deployment and Orchestration	15%	- CI/CD with dbt Cloud - Git version control integration - Environments (dev, staging, prod) - Jobs and scheduling in dbt Cloud
Data Transformation Techniques	25%	- Common table expressions and subqueries - Macros and packages - Jinja templating - Refactoring and incremental models

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q260-Q265):

NEW QUESTION # 260
You typically use dbt run to execute your project. However, you want to trigger a refresh of only models that depend on newly updated source dat a. How could you achieve this?

A. Manually determine dependent models, then use dbt run -m
B. Leverage dbt snapshots to identify changed sources and derive the dependent model list.
C. Add dbt run to the post-hook of your upstream ETL process, so it triggers upon data arrival.
D. Configure your sources with the freshness property and run dbt source freshness.

Answer: B,D

Explanation:
B automates discovery of what's stale. D uses snapshots for the same purpose. A is tedious, C runs all models, not selectively

NEW QUESTION # 261
You're setting up a new dbt project and want to customize the naming of compiled objects in the data warehouse. Which section of the dbt_project.yml file is the most relevant?

A. vars:
B. models:
C. snapshots:
D. seeds:

Answer: B

Explanation:
The models: section, particularly the nested configurations within it, is where you control the naming of materialized models in your database.

NEW QUESTION # 262
You're tasked with adding functionality to your dbt project for:

A. Handling slowly changing dimensions.
B. Auditing data quality with comprehensive anomaly detection tests.
C. Calculating complex financial metrics specific to your industry

Answer: B

Explanation:
Data quality and SCD management are common needs addressed by many dbt packages. Financial metrics tend to be more domain-specific, and you might need custom logic.

NEW QUESTION # 263
A colleague asks, "Why go through the trouble of using dbt? Can't we just write SQL scripts and schedule them?" Which of the following is the most compelling argument for using dbt?

A. dbt offers native support for advanced scheduling and orchestration features.
B. dbt models are typically more performant than hand-written SQL scripts.
C. dbt automatically generates user-friendly data lineage visualizations.
D. dbt promotes modularity, reusable code, and testing within data transformation pipelines.

Answer: D

Explanation:
While other options have benefits, dbt's core strength lies in enforcing structure and maintainability in data transformation processes.

NEW QUESTION # 264
You're setting up baseline comparisons with historical dat
a. Which factors are crucial to consider when selecting or generating the baseline dataset?

A. The data volume in the baseline should be large enough to identify statistical patterns.
B. The baseline needs to align in structure (schemas) with the dbt models being tested.
C. The baseline should cover a representative time range relevant to your analysis.
D. If using data, the baseline might need sanitization to remove PII-

Answer: B,C,D

Explanation:
Each of these directly impacts the validity and usefulness of the baseline for comparison. B, while good, is less universally critical.

NEW QUESTION # 265
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