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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You frequently need to implement custom SQL tests that are very similar but with slight variations in parameters. Which dbt feature could streamline this process?

A) dbt snapshots
B) The relationships test
C) dbt seeds
D) dbt macros

2. You have strict data retention policies. Which factors might influence how data is managed differently across your production, development, and raw data environments?

A) Regulatory requirements could mandate that production data be retained for longer periods.
B) All of the above.
C) Development data might have less stringent retention requirements, allowing for more frequent deletion-
D) Raw data might be archived or deleted following a shorter retention period than production data

3. You've been tasked with improving the discoverability of dbt models within your project. Which of the following changes would be the MOST effective in achieving this goal?

A) Ensuring models have clear and concise descriptions that accurately reflect their purpose.
B) Adding a tags list to all models, even if the tags are somewhat generic.
C) Writing longer, more verbose descriptions for each column in your models.
D) Using custom macros to dynamically generate model descriptions based on metadata.

4. "Isn't debugging harder with many smaller models in a DAG?" How would you address this concern expressed by a colleague?

A) "We'll rely extensively on snapshots to trace errors back to their origin."
B) "Debugging is slightly harder, but the benefits outweigh the added effort."
C) "Smaller models are easier to test individually, leading to faster issue identification."
D) "Our DAG will be simple, so debugging model dependencies won't be a concern."

5. You're planning your production dbt deployment strategy. When deciding between completely separate development and deployment environments vs. using different schemas within a single data warehouse, what are I key factors to weigh?

A) The desired level of isolation and access control granularity.
B) The complexity of your dbt project and the number of models.
C) The need to collaborate between developers with varying skill levels.
D) Your data warehouse's pricing model and how it charges for resource usage.

Solutions: