dbt Labs 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) The level of detail needed in downstream reporting.
B) The number of users currently active on the data warehouse.
C) Existing model patterns within the project for consistency.
D) The desired frequency for updating the data

2. Your data warehouse environment undergoes a major version upgrade, introducing changes in SQL syntax and supported functions. How would you handle the migration of dbt projects to ensure compatibility?

A) All of the above.
B) Utilize custom macros to introduce conditional logic or abstract away syntax differences between versions, if feasible.
C) Thoroughly test all dbt models across your staging and development environments to identify necessary updates.
D) Create temporary branches in your dbt projects to isolate upgrade-related code changes for each environment

3. You're working with highly sensitive production data that contains personally identifiable information (PII). How would this impact the design of your dbt project environments?

A) Employ data masking or tokenization techniques within dbt models that process sensitive data.
B) All of the above.
C) Potentially create separate, lower-level environments with obfuscated, anonymized, or synthetic datasets for broader development.
D) Implement strict access controls ensuring only authorized individuals can access production environments.

4. A model passes its tests during development, but later fails when deployed on a less powerful production database. Which of the following might be the reason for this discrepancy?

A) There's a bug in the version of dbt that runs in production.
B) Source data distributions differ significantly between environments.
C) The test was accidentally disabled in the production dbt project.
D) Performance-related test thresholds are too lenient in development.

5. Your dbt project contains sensitive dat
a. You need to load seed data (e.g., lookup tables) while ensuring it's not accidentally committed to version control. How can you best achieve this?

A) Write the seed data into log files, excluding them from version control.
B) Employ a secrets management tool and integrate it with your dbt deployment.
C) Utilize environment variables to store seed data values.
D) Create separate seed files and add them to your .gitignore or equivalent.

Solutions: