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最新的 Analytics Engineers dbt-Analytics-Engineering 免費考試真題:

1. Business requirements call for a report ranking products based on year-over-year (YoY) revenue growth. Historical data must be preserved. What's a potential issue in implementing this directly in a dbt model?

A) Incremental materializations aren't suitable for trend analysis.
B) Views cannot perform complex date manipulation required for YoY calculations.
C) ref() functions can hinder performance when working with historical data.
D) Data warehouses might not be optimized for period-over-period comparisons.

2. Unexpected Results

A) Use diff tools or version control history to compare the refactored logic against the previous version.
B) Revert the changes in your version control system to restore the model to its previous working state.
C) Implement targeted profiling to measure the execution time of specific query sections within the model.

3. You've defined a model as follows:SQL

A) An incorrect materialization strategy causing the wrong data to be populated in a downstream source.
B) A pure SQL issue: The count() function isn't supported for the data type of a column in the 'cust_orders' source.
C) A Jinja templating error: Misusing the source() function within the count() function.
D) A data type mismatch between a column in 'stg_customers' and how the query is using it for the join.

4. You introduce a minor formatting change to a large SQL model. Afterwards, the results change unexpectedly. Which dbt-related issue might explain this, even if the SQL itself appears correct?

A) A subtle indentation change in a CTE accidentally modifies the execution order within the query.
B) An upstream data issue only surfaces due to the formatting change making that part of the query execute differently.
C) You've used a reserved keyword as a column alias without properly quoting it.
D) There's a version mismatch between the dbt library used during development and in production.

5. You have a dbt model that depends on several upstream source tables. One of these source tables is occasionally updated very late, causing your job to fail if triggered at the usual time. What configuration could mitigate this?

A) Redesign the model with an incremental materialization strategy that gracefully handles partial updates.
B) Configure a dbt hook to run before the job, dynamically checking freshness of the source table.
C) Set the -full-refresh flag for the dbt job to ensure all tables are materialized.
D) Add a depends_on relationship pointing to the potentially late table within your model.

問題與答案：