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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Testing and Documentation | 20% | - Documentation generation - Custom data tests - Schema tests (unique, not_null, accepted_values, relationships) - dbt docs and DAG visualization |
| Topic 2: Data Transformation Techniques | 25% | - Common table expressions and subqueries - Macros and packages - Jinja templating - Refactoring and incremental models |
| Topic 3: Deployment and Orchestration | 15% | - Jobs and scheduling in dbt Cloud - CI/CD with dbt Cloud - Git version control integration - Environments (dev, staging, prod) |
| Topic 4: Models | 25% | - Snapshots - Sources and references - Materializations (table, view, ephemeral, incremental) - Seeds - Writing and managing SQL models |
| Topic 5: dbt Fundamentals | 15% | - dbt project structure - dbt workflow and best practices - dbt Core vs dbt Cloud |
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NEW QUESTION # 155
You want to host your dbt project documentation on an internal company server that's behind a firewall. Which approach would be best suited for this scenario?
Answer: C
Explanation:
Uploading the static files (HTML, CSS, JavaScript) to your internal server provides a straightforward way to host the documentation behind a firewall.
NEW QUESTION # 156
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?
Answer: C
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 # 157
You've defined a unique test on a key column within a model. When running dbt test, you encounter a surprising failure. Which of the following might be the cause?
Answer: A,B,C,D
Explanation:
All of these could cause a previously passing test to fail, highlighting the interplay between data, model logic, and test definitions.
NEW QUESTION # 158
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 # 159
You discover an intermittent issue where your models sometimes produce incorrect results. After investigation, the problem seems to occur when the source data contains a particular combination of unexpected values. How would you best address this?
Answer: B,D
Explanation:
B helps reliably prevent bad data from affecting output. C seeks a long-term solution by fixing the root cause. A might be a temporary workaround, but not ideal. D is risky and hides the issue.
NEW QUESTION # 160
......
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