dbt Labs dbt Analytics Engineering Certification : dbt-Analytics-Engineering

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. (Multiple Select)

A) Nesting multiple correlated subqueries within a single query_
B) Leveraging window functions to calculate values across rows or groups.
C) Using SELECT * to include all columns dynamically
D) Writing custom macros to encapsulate frequently used SQL logic.

2. You define several sources. However, some are frequently updated, while others rarely change. How can you optimize your dbt run command to account for this difference?

A) Employ the defer option if downstream models permit and ensure freshness checks are adjusted.
B) Set up dbt schedules within your dbt_project.yml with different intervals for each group.
C) Group sources based on update frequency and create separate dbt run commands for each group.
D) Include the -exclude flag along with less-frequently updated source names.

3. You receive a comment on your pull request that says, "This change could be implemented more efficiently." What are some effective ways to respond while keeping the code review process constructive?

A) Request the reviewer to provide a code snippet demonstrating a more efficient implementation.
B) All of the above
C) Explain your current approach and the reasoning behind your choices.
D) Discuss the potential trade-offs between efficiency and other factors like readability or maintainability.

4. You discover that an essential dbt macro behaves differently between your development environment and production due to a subtle database version discrepancy. Which debugging approach would be most efficient?

A) Add verbose logging within the macro to track execution differences step by step.
B) Temporarily introduce a conditional branch into the macro code that is environment-aware.
C) Set up a CIICD test that runs the macro in isolation using both database versions.
D) Use database-specific functions to query the version information directly from within dbt models.

5. While reviewing a dbt project, you notice a pattern where ref() calls are chained together:
Model A references Model B, which in turn references Model C.
What's a potential downside of this pattern?

A) Decreased performance due to unnecessary materializations.
B) Test results might become less reliable.
C) Version control conflicts become more likely.
D) Model dependencies become harder to track and understand.

Solutions: