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| Section | Weight | Objectives |
|---|---|---|
| dbt Fundamentals | 15% | - dbt project structure - dbt workflow and best practices - dbt Core vs dbt Cloud |
| Data Transformation Techniques | 25% | - Refactoring and incremental models - Macros and packages - Common table expressions and subqueries - Jinja templating |
| Deployment and Orchestration | 15% | - Environments (dev, staging, prod) - Git version control integration - CI/CD with dbt Cloud - Jobs and scheduling in dbt Cloud |
| Models | 25% | - Materializations (table, view, ephemeral, incremental) - Snapshots - Sources and references - Seeds - Writing and managing SQL models |
| Testing and Documentation | 20% | - dbt docs and DAG visualization - Custom data tests - Schema tests (unique, not_null, accepted_values, relationships) - Documentation generation |
>> Test dbt-Analytics-Engineering Dates <<
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NEW QUESTION # 150
(Multiple Select)
Answer: B,C
Explanation:
B aligns model runs with data availability. C ensures Bl dashboards reflect the latest data. A is cost optimization, not a core freshness requirement. D snapshots are more about change tracking than regular updates.
NEW QUESTION # 151
(Multiple Select)
Answer: A,D
Explanation:
Linters improve code hygiene, and schema tests proactively validate assumptions about model dependencies. 'Defer' helps with logic errors, and freshness checks are for source health.
NEW QUESTION # 152
Match the macro to the appropriate hook so that the correct execution steps comply with these rules:
* macro_1() needs to be executed after every dbt run.
* macro_2() needs to be executed after a model runs.
* macro_3() needs to execute before every dbt run.
* macro_4() needs to be executed before a model runs.
Answer:
Explanation:
Explanation:
Hook 1
on-run-end: "{{ macro_x() }}"
The Answer:
macro_1
Hook 2
models:
<my_dbt_project>:
post-hook: "{{ macro_x() }}"
The Answer:
macro_2
Hook 3
on-run-start: "{{ macro_x() }}"
The Answer:
macro_3
Hook 4
{{
config(
pre-hook: "{{ macro_x() }}"
)
}}
macro_4
dbt supports run-level hooks and model-level hooks.
Run-level hooks fire once per invocation, while model- level hooks fire around each individual model.
on-run-end is a run-level after hook that executes once after the entire dbt command completes.
Because macro_1() must run after every dbt run, it correctly belongs here.
The post-hook configured under the models: section runs after each model in that scope finishes building.
This matches the requirement for macro_2() to execute after a model runs.
on-run-start is a run-level before hook and fires once before dbt begins executing any models for that command, making it the right place for macro_3() which must run before every dbt run.
Finally, the pre-hook specified inside a model's config() block runs before that specific model is built.
Since macro_4() must execute before a model runs, it belongs in the pre-hook configuration.
Thus the correct mapping is:
* on-run-end # macro_1
* model post-hook # macro_2
* on-run-start # macro_3
* model pre-hook # macro_4.
NEW QUESTION # 153
You frequently need to spin up short-lived development environments on demand for testing and experimentation. Which dbt feature would streamline this process, especially when it involves custom configuration?
Answer: A,B
Explanation:
A encapsulates environment settings in reusable blocks- B allows for ad-hoc adjustments when running dbt- C is relevant for cloud-based setups, but less flexible for on-the-fly changes- D relates to model selection, not environment setup.
NEW QUESTION # 154
While onboarding new data sources to your dbt project, you discover some have unexpectedly high null rates in key columns. How would you integrate tests for this into your dbt workflow?
Answer: C,D
Explanation:
B ensures data entering dbt is checked at the first opportunity. C pushes quality checks as far upstream as possible- A is valid, but less integrated if sources are managed outside dbt's purview D masks the problem rather than addresses it.
NEW QUESTION # 155
......
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