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

A. Write a macro within the model to switch behavior based on an environment variable.

B. Manually update the timestamp column in the incremental model's output table.

C. Add the -full-refresh flag to the dbt run command.

D. Use dbt artifacts or the compilation context to access the current run's timestamp and override model logic.

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

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

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

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

A. Temporarily disable the package and try running the model.

B. Run the package's models in isolation to see if the error reproduces.

C. Examine recent commits or releases of the package for changes that might have introduced a bug.

D. Add -log-format json to your dbt run command to get more structured error output.

A. Utilize a macro that generates a Markdown list representation of model dependencies.

B. Rely on third-party tools that specialize in parsing dbt projects to produce textual lineage outputs

C. Disable the DAG visualization entirely within the generated documentation.

D. Extend the dbt documentation framework to create a custom text-based lineage view

A. Generally, using raw data directly for reporting should be avoided due to potential quality and consistency issues.

B. In environments where strict data governance and transformation processes are in place.

C. When performance is a top priority, and the overhead of additional data transformations is undesirable.

D. When dealing with simple analyses, and there's high confidence in the quality of the raw data source.

A. Implement a CI/CD process that includes checks to prevent specific dbt commands from running in the production environment

B. Create a separate profiles.yml file for production that disables dangerous materialization strategies.

C. Rely purely on thorough code reviews to prevent execution of these commands in production.

D. Write a custom macro that only executes such operations on non-production targets.

A. Spreadsheet software (like Excel or Google Sheets)

B. Data observability platforms

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

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

A. All of the above.

B. Configuration in your dbt_projectyml file to associate models with the relevant tests.

C. The ability for the macro to query the results of test executions from the dbt run-

D. A custom CSS class for the warning icon and a way to apply it dynamically using the macro.

A. You are viewing an older cached version of the documentatiom

B. You forgot to run dbt docs generate after modifying the YAML file.

C. The model's YAML file has incorrect syntax preventing column descriptions from rendering.

D. The documentation was not configured correctly to display column descriptions.

A. Verify that your web browser supports the CSS and JavaScript used for custom DAG styling.

B. Ensure the macro is named correctly and located in the appropriate directory (macros folder) of your dbt project.

C. Update the cache expiration settings of your web browser.

D. The built-in DAG functionality might have limitations preventing this type of customization.

A. Source freshness checks are configured incorrectly, allowing older data to be processed

B. All of the above.

C. Some models have incorrect materializations, causing them to not update when expected

D. There are dependency issues in your DAG affecting execution order.

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

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

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

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

A. Insert a YAML anchor (&) in your dbt_projectyml file and reference it in your model files.

B. Create a YAML anchor within the relevant model file where the text should be reused.

C. YAML anchors are not directly supported in the context of dbt model and source descriptions-

D. Define a Jinja macro for the text block, and then call the macro within your descriptions.

A. Create a separate branch specifically for addressing this issue.

B. Deploy the modified model to a development or staging environment.

C. Adjust the model's SQL query and re-run it to observe the changes in output.

D. Write new unit tests that specifically target the faulty calculation logic with various input scenarios.