dbt Analytics Engineering Certification (DBT-AE) Practice Questions: Deployment and Operations Domain

Correct Answer: B

Explanation: Incremental models in dbt are designed to handle large datasets efficiently by only processing new or changed data since the last run. The `is_incremental()` macro allows you to define logic for handling incremental runs. Option A is incorrect because full refreshes are resource-intensive and unnecessary for daily partitioned data. Option C is incorrect because using `LIMIT` would not ensure all necessary data is processed. Option D is a good practice for filtering data but does not specifically address the need for incremental processing. For more information, refer to the dbt documentation on incremental models: https://docs.getdbt.com/docs/build/incremental-models

Correct Answer: B

Explanation: Option B is correct because while adding indexes can improve performance, it is not a strategy that is directly managed within dbt. Indexing should be handled at the database level and not through dbt configurations. Options A, C, and D are valid strategies within the context of dbt for optimizing model performance.

Correct Answer: C

Explanation: Simplifying Jinja logic and offloading complex calculations to the database can significantly improve performance by leveraging the database's processing power. Option A might not be feasible if the transformation is integral to the model's logic. Option B, while useful for adding columns, does not address performance issues related to Jinja complexity. Option D might temporarily alleviate the issue but is not a sustainable solution for optimizing code performance. For best practices with Jinja, see: https://docs.getdbt.com/docs/building-a-dbt-project/jinja-macros

Correct Answer: A

Explanation: The command `dbt test --select state:modified+` selects models that have been modified and their dependencies for testing. This is useful in a CI/CD workflow to focus on changes and their impact. Option B is incorrect because it runs the models instead of testing them. Option C does not include dependencies, which might lead to incomplete testing. Option D is redundant because `state:modified+` already excludes unmodified models. For more on state-based selectors, see: https://docs.getdbt.com/reference/node-selection/state

Correct Answer: B

Explanation: Option B is correct because it uses `{{ var('alias_name') }}` to dynamically create a column alias using a dbt variable. Option A is incorrect because it assumes `alias_name` is a Jinja variable, not a dbt variable. Option C is incorrect because `config` is not used for aliasing in SQL. Option D is incorrect because it uses a literal string instead of a dynamic alias. For more on using Jinja with dbt, see the [dbt Jinja documentation](https://docs.getdbt.com/docs/building-a-dbt-project/jinja-context).

Correct Answer: D

Explanation: The correct configuration for an incremental model that processes new records based on a date column is option D. This configuration uses the `incremental_strategy='append'` to add new records and specifies `partition_by` to optimize the table by the `order_date` column. Option A and B are incorrect because they lack the `partition_by` configuration. Option C is incorrect because `updated_at` is not a valid argument for specifying new records in dbt incremental models. Refer to the [dbt documentation](https://docs.getdbt.com/docs/build/incremental-models) for more details.

Correct Answer: C

Explanation: The correct answer is C. In Jinja, when you want to include a string variable in a SQL query, you should use single quotes around the variable to ensure it's treated as a string in SQL. Option A is incorrect because it would cause a syntax error in SQL. Option B is incorrect because double quotes are typically used for identifiers, not strings. Option D is incorrect because backticks are used for identifiers in some SQL dialects, not strings. For more information, refer to the [dbt Jinja documentation](https://docs.getdbt.com/docs/building-a-dbt-project/jinja-context).

Correct Answer: B

Explanation: Option B is correct because using a Jinja `if` statement allows you to conditionally include the column only if it exists, ensuring the model runs smoothly across different environments. Options A and C are not practical as they would lead to failures or unnecessary complexity. Option D does not address the issue of the column's existence.

Correct Answer: B

Explanation: Breaking down a complex model into smaller, intermediate models can help optimize performance by simplifying the logic and reducing the complexity of each operation. This approach can also make the pipeline easier to debug and maintain. Option A is incorrect because converting the model to a view might not improve performance, as views are often less efficient for complex transformations compared to materialized tables. Option C is incorrect because `--full-refresh` forces a complete rebuild and does not optimize performance. Option D could help in some scenarios, but it is not a direct optimization for complex joins. For more information, refer to dbt's [performance optimization guide](https://docs.getdbt.com/docs/guides/performance-optimization).

Correct Answer: C

Explanation: The correct command to run only modified models and their dependencies is `dbt run --select state:modified+`. The `state:modified+` selector identifies models that have changed since the last run, along with their downstream dependencies. Option A is incorrect because `--models` is an outdated syntax replaced by `--select`. Option B and D are incorrect because they use `state:new`, which only selects models that are new, not modified. For more details, see the dbt documentation on [state-based selectors](https://docs.getdbt.com/reference/node-selection/state-comparison).