最新的dbt Labs dbt Analytics Engineering Certification - dbt-Analytics-Engineering免費考試真題

A. Introduce CTEs within models to create logical groupings of transformations.

B. Materialize all models as 'views' for maximum flexibility and to avoid potential storage bottlenecks.

C. Minimize the number of intermediate models, even if it means complex SQL within a few large models.

D. Consider common reporting needs when designing downstream models.

A. Pass the country code as a CLI argument when executing dbt run.

B. Create a seed file mapping country codes to filter values and reference it in the model.

C. Use environment variables in conjunction with Jinja to substitute the country code.

D. Build a dynamic query string entirely within a Jinja macro.

A. Version control systems (e.g., Git)

B. Modern Bl tools (like Tableau, Looker, Power Bl)

C. Data observability platforms

D. Spreadsheet software (like Excel or Google Sheets)

A. A snapshot is incorrectly merging new data with incompatible types into an existing table.

B. A Jinja macro meant to filter out non-numeric strings has a logic flaw.

C. A source table has inconsistent data, mixing true numeric values with strings.

A. ref() functions can hinder performance when working with historical data.

B. Views cannot perform complex date manipulation required for YoY calculations.

C. Data warehouses might not be optimized for period-over-period comparisons.

D. Incremental materializations aren't suitable for trend analysis.

A. Custom dbt flags (using -vars) to control the logging level of the SQL execution.

B. dbt hooks to redirect model-related logs to a centralized logging platform.

C. Environment variables to conditionally enable verbose logging macros inside the model.

D. The -warn-error flag to treat warnings as errors, making your run fail louder on potential issues.

A. Normalize the cohort data and dimensions, using joins for analysis.

B. A wide table with columns for each potential filtering combination (country, product category, acquisition channel, etc.).

C. Pre-calculate all possible cohort and dimension combinations as an incremental model.

D. Introduce a JSON column to store dimensions dynamically, reducing the number of columns.

A. The documentation generator has a known limitation on how test results are displayed.

B. Your data pipeline hasn't been executed recently, so there are no recent test results.

C. The project settings are explicitly configured to hide test results in the documentation.

D. The type of test you're using is not supported for displaying results in the documentation.

A. Validating that the column names generated by the Jinja loop would be valid SQL identifiers.

B. Ensuring the grains variable is defined in dbt_project.yml or a macro.

C. Checking that the date_part() function is compatible with your database-

D. Making sure the source table fct_orders contains the expected columns.

A. You need to frequently load incremental data using dbt's snapshot feature.

B. Your data warehouse has time-based pricing tiers, and you need models to refresh during off-peak hours.

C. Upstream data sources update on a predictable cadence (e.g., daily extracts).

D. Downstream Bl dashboards rely on up-to-date data transformed by your dbt models-

A. Enabling webhooks in your Git repository provider.

B. Write a custom script to check for project changes and handle deployment.

C. Implementing a CI/CD pipeline that includes dbt build steps.

D. Configuring dbt Cloud to trigger documentation deploys.

A. All of the above.

B. Introducing sensitive data sources requires careful consideration and might be better handled outside the warehouse.

C. dbt models might become unnecessarily complex due to unfiltered data.

D. It could become expensive due to storage and compute costs, especially with large, raw datasets.