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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. (Multiple Select)

A) Preferring correlated subqueries over joins when relationships are one-to-one.
B) Leveraging appropriate indexes on columns used in joins and WHERE clauses.
C) Avoiding excessive use of UNION operations, especially within loops.
D) Using SELECT * whenever possible to avoid explicitly specifying needed columns.

2. You typically use dbt run to execute your project. However, you want to trigger a refresh of only models that depend on newly updated source dat a. How could you achieve this?

A) Configure your sources with the freshness property and run dbt source freshness.
B) Manually determine dependent models, then use dbt run -m
C) Add dbt run to the post-hook of your upstream ETL process, so it triggers upon data arrival.
D) Leverage dbt snapshots to identify changed sources and derive the dependent model list.

3. You're working with a model that aggregates data across many countries. Which assumption about the data might be especially important to test for and handle in your model logic?

A) The presence of unexpected HTML tags within textual data fields.
B) Time zones and daylight savings rules for timestamps could differ between data sources.
C) Encoding formats (UTF-8, etc.) may be inconsistent across source systems.
D) Different regions may use varying currency exchange rates.

4. You need to create a model that combines data from a large fact table with smaller dimension tables. Performance is paramount, and the data in the fact table updates incrementally but frequently. Which materialization strategy is likely to provide the optimal balance of efficiency and freshness?

A) Materialize the fact table as an incremental model and the dimension tables as views.
B) Materialize all tables (fact and dimensions) as views.
C) Materialize the fact table as a table and the dimension tables as incremental models.
D) Materialize all tables (fact and dimensions) as tables.

5. You want to display the runtime metrics (e.g., execution time) of each model directly on the DAG node. Which challenges might you face when implementing this?

A) dbt documentation is primarily static; it might not be able to render dynamic metrics that change with each execution.
B) Adding too much detailed information directly to the DAG could lead to visual clutter and make it overwhelming.
C) Retrieving runtime metrics for each historical run might not be natively supported by dbt
D) All of the above

Solutions: