최신dbt Labs dbt Analytics Engineering Certification - dbt-Analytics-Engineering무료샘플문제

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. An upstream data issue only surfaces due to the formatting change making that part of the query execute differently.

B. A subtle indentation change in a CTE accidentally modifies the execution order within the query.

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.

A. Raise an issue with the team responsible for the upstream data source to fix the error at its origin.

B. Write a custom dbt test to explicitly identify the bad data scenario.

C. Modify the model to ignore the rows causing the issue.

D. Add pre-processing logic within the model to clean or filter out the problematic data.

A. Rely on your data warehouse's native rollback functionality to reverse changes if needed.

B. Write extensive tests targeting this model in your production environment

C. Configure production jobs to use a pinned version of the dbt model.

D. Create a duplicate model in production and only sync changes once they're fully tested-

A. Convert the model to a snapshot to leverage historical data more effectively.

B. Ensure the underlying data adheres to DRY principles.

C. Pre-calculate common geospatial results as an incremental model.

D. Investigate whether the database offers specialized geospatial indexes or functions.

A. Setting up tags strategically and using the -s flag when executing dbt commands.

B. Configuring separate dbt projects for each subdirectory and running them independently.

C. Using dbt run -m to select models using a path pattern.

D. Defining a custom source in sources.yml that selects tables from the desired subdirectory.

A. dbt run -m +model_path to explicitly select a model by its file path.

B. dbt build -exclude model_name to temporarily skip certain models.

C. dbt test --select source name to validate the new models' source data.

D. dbt run -s to target models with a specific tag

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

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

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

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