dbt-Analytics-Engineering 無料問題集「dbt Labs dbt Analytics Engineering Certification」

（A）The level of detail needed in downstream reporting.

（B）Existing model patterns within the project for consistency.

（C）The desired frequency for updating the data

（D）The number of users currently active on the data warehouse.

（A）Preferring long, monolithic SQL files with minimal CTEs.

（B）Encapsulating complex logic in well-named macros with descriptive comments.

（C）Minimizing explicit model dependencies to create loosely coupled components.

（D）Using clear and consistent naming conventions across models and macros.

（A）A timeout issue caused by the model exceeding resource limits.

（B）Incorrect ref() usage causing cyclical dependencies to occur-

（C）Data within a column exceeding the precision or scale of the defined data type

（D）Missing permissions to write data to the target table in the warehouse.

（A）git cherry-pick specific commits from your branch and reapply them.

（B）Use git rebase -i (interactive rebase) to squash commits together.

（C）Run git rebase main followed by git commit -amend.

（D）Manually copy your files into a new branch, commit, and issue a pull request.

（A）Continue copying the date calculation logic into each relevant model.

（B）Introduce a new intermediate table to store the result of the date calculations.

（C）Write a custom Python function and import it into your dbt project.

（D）Create a macro, encapsulating the date calculations, and reference it in the affected models.

（A）Potential network latency affecting performance of dbt runs for the geographically separate team.

（B）Controlling dbt access based on team membership and geographic location to align with security policies.

（C）Data residency regulations that might restrict the flow or storage of data between geographic regions.

（D）All of the above.

（A）dbt seed with -select to load data into production.

（B）dbt run with -target to specify a development environment.

（C）dbt test with -defer to temporarily bypass upstream dependencies.

（D）dbt compile to ensure changes are valid without executing models.

（A）Test results might become less reliable.

（B）Model dependencies become harder to track and understand.

（C）Version control conflicts become more likely.

（D）Decreased performance due to unnecessary materializations.

（A）Configuring monitoring and alerting to track data warehouse usage and events related to dbt jobs

（B）Defining and implementing a schema and naming conventions for your dbt models within the data warehouse.

（C）All of the above

（D）Establishing access controls and user roles specific to the dbt project's requirements.

（A）Create a pre-hook that automates schema validation and sends the output to the designated team.

（B）Write dbt tests that explicitly fail if the new source schema does not match the approved definition.

（C）Run a separate dbt project in production exclusively for source validation, providing access to relevant stakeholders.

（D）Use dbfs ephemeral models feature to temporarily load the data for the approval process.

（A）Run a separate dbt project against production data for testing, but target a staging schema.

（B）Dynamically increase resource allocation (CPU, memory) within your data warehouse, if supported.

（C）Selectively snapshot a subset of production data into your development environment.

（D）Write dbt macros that use temporary tables to simulate larger datasets during development