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

(Multiple Select)

正解:A、C 解答を投票する
解説: (JPNTest メンバーにのみ表示されます)
(Multiple Select)

正解:B、D 解答を投票する
解説: (JPNTest メンバーにのみ表示されます)
A model fails with an error similar to:Numeric overflow converting to data type numeric(18,2)

解説: (JPNTest メンバーにのみ表示されます)
You've made several commits to your feature branch. Before merging into the main branch, you want to consolidate the changes into a single, clear commit. What Git technique would help achieve this?

解説: (JPNTest メンバーにのみ表示されます)
You've identified the same complex date calculations repeated across multiple models. Which approach aligns with modularity and DRY (Don't Repeat Yourself) principles?

解説: (JPNTest メンバーにのみ表示されます)
You're expanding your dbt deployment to support a new regional team based in a different geographic location from your main development team. They'll access the same production data in your centralized data warehouse. Which factors need careful consideration?

解説: (JPNTest メンバーにのみ表示されます)
You're experimenting with new transformations. You want to iterate on a model without affecting data in the production warehouse. Which dbt command should you use along with appropriate flags?

解説: (JPNTest メンバーにのみ表示されます)
While reviewing a dbt project, you notice a pattern where ref() calls are chained together:
Model A references Model B, which in turn references Model C.
What's a potential downside of this pattern?

解説: (JPNTest メンバーにのみ表示されます)
You're setting up a new dbt project on a recently provisioned data warehouse. What actions are essential before your initial development begins?

解説: (JPNTest メンバーにのみ表示されます)
You introduce a new data source for a dbt model. Production policies require review and approval of the source's schema before it can be used for reporting. How could you streamline this within your dbt project?

正解:A、C、D 解答を投票する
解説: (JPNTest メンバーにのみ表示されます)
Your development environment uses a smaller-scale version of the production warehouse. You often debug issues that depend on data volume, requiring a way to temporarily "scale up" your development environment. Which strategies are feasible?

正解:A、B、C 解答を投票する
解説: (JPNTest メンバーにのみ表示されます)

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