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| Section | Weight | Objectives |
|---|---|---|
| dbt Fundamentals | 15% | - dbt Core vs dbt Cloud - dbt project structure - dbt workflow and best practices |
| Deployment and Orchestration | 15% | - Jobs and scheduling in dbt Cloud - CI/CD with dbt Cloud - Environments (dev, staging, prod) - Git version control integration |
| Models | 25% | - Materializations (table, view, ephemeral, incremental) - Snapshots - Writing and managing SQL models - Seeds - Sources and references |
| Data Transformation Techniques | 25% | - Common table expressions and subqueries - Macros and packages - Refactoring and incremental models - Jinja templating |
| Testing and Documentation | 20% | - dbt docs and DAG visualization - Documentation generation - Schema tests (unique, not_null, accepted_values, relationships) - Custom data tests |
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NEW QUESTION # 102
You must exclude a specific model from being included in the generated documentation entirely. How would you achieve this?
Answer: D
Explanation:
The enabled flag in dbt_project.yml provides control over whether a model (or other resources) are includedin dbt operations, including doc generation. Check your dbt version documentation for the latest syntax
NEW QUESTION # 103
You're loading fresh data from an external API. How would you implement dbt tests to ensure this feed is consistently providing up-to-date data?
Answer: A,B
NEW QUESTION # 104
31. Your entire DAG looks like the image shown.
(Several stg_ models appear upstream, feeding into int_ and fct_ models.) The question asks:
"Was this modeling rule violated?
Staging models dependent on other staging models"
Answer: B
Explanation:
In dbt's recommended layered modeling architecture, the staging layer is intended to provide a clean, one- to-one representation of raw source tables. Each stg_ model should depend only on sources, not on other staging models. This ensures staging remains a simple, transparent layer where data is renamed, recast, standardized, and lightly transformed before being passed to intermediate and mart layers.
In the DAG shown, at least one staging model (for example, stg_line_items or stg_tpch_line_items) appears downstream of another staging model, meaning a stg_ model is referencing another stg_ model. This violates dbt's recommended modeling practice, because it creates unnecessary complexity in the staging layer and reduces modularity and transparency. Downstream layers such as intermediate (int_) and marts (fct_) should be used to combine, enrich, or join multiple staging outputs.
When staging models depend on each other, it becomes harder to trace lineage, reduces clarity about where transformations occur, and complicates the entire DAG. The proper pattern is:
* Sources # Staging (stg_) # Intermediate (int_) # Marts (fct_)
Since the DAG shows staging models referencing other staging models, the rules have indeed been violated.
NEW QUESTION # 105
You discover that dbt models run significantly slower in your production environment than in development. What potential causes should you investigate?
Answer: A,B,C
Explanation:
Each of these directly affects performance and could differ significantly between environments. C is relevant, but less likely the sole cause if development is performant.
NEW QUESTION # 106
(Multiple Select)
Answer: A,C
Explanation:
Tables trade off storage for excellent query performance and are a good fit for stable datasets or ones that can be efficiently updated.
NEW QUESTION # 107
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