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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You've written dbt tests that assume a certain distribution of values in a column. Later, the upstream process generating the data changes unexpectedly. What's the most likely consequence?

A) dbt will automatically adjust your tests to accommodate the new distribution.
B) Your dbt models may silently produce incorrect results due to the changed distribution.
C) The tests will fail, preventing the changed data from being loaded, preserving the old behavior.
D) The changed data distribution has no impact on downstream dbt models.

2. You're considering using dbt's --store-failures flag when running tests. Which of the following are potential implications of using this flag?

A) Successful models will not be rebuilt if downstream dbt tests are marked as failures.
B) The overall dbt test command may run faster-
C) This flag alters how dbt calculates test results
D) Troublesl-ming failing tests will become easier, as data related to the failure is persisted.

3. A pull request includes changes to both dbt models and configuration files in your dbt_project.yml file. During the merge, conflicts arise in the configuration file. How would you approach resolving these conflicts?

A) Give priority to the model changes and manually edit the configuration file after.
B) Ask the author of the pull request to rebase their changes and resolve the configuration conflict in their branch.
C) Utilize Git's merge conflict markers to carefully combine changes in the configuration file.
D) Request a separate, smaller pull request dedicated to the configuration changes to isolate the conflict.

4. You need to set up a production-like environment for load testing and performance tuning of dbt models. How might you approach the resource allocation and provisioning of this environment?

A) Partner with your data infrastructure team to get guidance based on their experience.
B) Write dbt macros that artificially increase dataset sizes in development for simulating load.
C) Create a CI/CD job that dynamically scales a cloned production environment for the test, then scales it back down.
D) Run performance tests directly against production during a designated off-peak period.

5. You have complex dbt models involving incremental logic based on a timestamp column. To debug a production issue, you want to temporarily force a full rebuild of a specific incremental model. Which dbt command-line options could help?

A) Use dbt artifacts or the compilation context to access the current run's timestamp and override model logic.
B) Write a macro within the model to switch behavior based on an environment variable.
C) Manually update the timestamp column in the incremental model's output table.
D) Add the -full-refresh flag to the dbt run command.

Solutions: