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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You encounter an unexpected dbt run error. Rather than re-running all models, you want to resume from the point of failure. Which of the following, if any, might allow for this?

A) dbt Cloud's job retry features for automatically resuming failed job executions.
B) dbt unfortunately lacks native support for resumable jobs.
C) Using dbt snapshots to track model states, allowing you to rollback changes as needed
D) The --fail-fast flag instructs dbt to only execute successful models, making restarts unnecessary

2. You assume two columns of type 'numeric' will always align in terms of precision and scale (number of decimal places). What's a key way to design your models and tests to be resilient even if this assumption changes?

A) Avoid relying on exact equality when comparing values from these columns.
B) Always store all numeric values as strings to prevent these issues.
C) Use explicit casting functions to enforce matching precision during calculations.
D) Write tests to compare the column metadata rather than their contents.

3. You have a model that assumes an input dataset will always be sorted by a particular column. Unfortunately, this is not always true. What might be the best way to make your model more robust?

A) Add an explicit ORDER BY clause to the start of your model's SQL.
B) Accept that this is a source data issue and cannot be addressed within dbt.
C) Increase memory allocated to your data warehouse in hopes the sorting happens by default.
D) Implement a custom test to fail the model whenever the sorting assumption is violated.

4. You want to standardize descriptions across a large number of models that share the same underlying source schemas. Which approach might be the most efficient?

A) Write a Jinja macro that dynamically generates descriptions based on metadata.
B) Focus on using a version control system and enforce descriptions directly within the source system.
C) Manually copy and paste the descriptions into each model's YAML file.
D) Create a CSV lookup file and maintain custom source descriptions within the file.

5. Dependency Break

A) All of the above.
B) Using the dbt deps command to visualize dependencies and anticipate potential impacts.
C) Setting up alerts or notifications if dbt runs fail unexpectedly in a production environment.
D) Adding schema tests to the source table to catch structural changes early on.

Solutions: