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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You need a materialization that automatically adapts to upstream schema changes while still preserving query performance. Which dbt materialization offers this kind of flexibility?

A) Incremental
B) Ephemeral
C) View
D) Snapshot

2. You have an urgent hotfix that needs to be deployed while a larger feature branch is still under review. Which strategies could you consider?

A) Merge the hotfix into the feature branch and wait for the entire feature to be approved.
B) Create a dedicated branch from the main branch specifically for the hotfix.
C) Merge the hotfix into the main branch and then rebase the feature branch on top of the new main branch.
D) Temporarily revert the feature branch changes, deploy the hotfix, and then restore the feature branch.

3. You've built a macro to implement a common custom test pattern. When using this macro in multiple models, you realize the tests are running much slower than expected. What might be the cause?

A) The macro's SQL logic is inefficiently querying the model or other data.
B) The macro is accidentally triggering downstream model rebuilds.
C) Too many tests are being defined on individual models.
D) This is expected behavior, and custom tests always incur a performance penalty.

4. You want to host your dbt project documentation on an internal company server that's behind a firewall. Which approach would be best suited for this scenario?

A) Upload the static files generated by dbt docs generate to your internal web server.
B) Deploy the docs using dbt Cloud's built-in hosting features.
C) Directly modify the HTML and CSS of the generated documentation site to add firewall-specific rules.
D) Utilize the dbt docs serve command, making the local web server accessible within your private network.

5. dbt Model Snippet:SQL

A) Making sure the source table fct_orders contains the expected columns.
B) Ensuring the grains variable is defined in dbt_project.yml or a macro.
C) Validating that the column names generated by the Jinja loop would be valid SQL identifiers.
D) Checking that the date_part() function is compatible with your database-

Solutions: