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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. Type Mismatch

A) Modify the source table directly in the database to adjust the data type, ensuring backward compatibility.
B) Introduce a staging model specifically to perform the type conversion, creating a clean separation.
C) Handle the type conversion with a custom macro and apply it selectively within your dbt project.
D) Use a CAST operation within the affected model's SQL, leaving the underlying source unchanged.

2. You have a dbt project with models defined as views. After deploying to a production database, the view objects are present, but no data is displayed when querying them. What might be the root cause?

A) Dbt view materializations behave differently in production.
B) Your dbt job didn't run successfully, so the views were created empty.
C) Permissions granted in production don't allow reading data from the underlying tables.
D) There's a bug in dbt's SQL generation that specifically affects views in certain environments.

3. Your development environment uses a smaller-scale version of the production warehouse. You often debug issues that depend on data volume, requiring a way to temporarily "scale up" your development environment. Which strategies are feasible?

A) Selectively snapshot a subset of production data into your development environment.
B) Dynamically increase resource allocation (CPU, memory) within your data warehouse, if supported.
C) Write dbt macros that use temporary tables to simulate larger datasets during development
D) Run a separate dbt project against production data for testing, but target a staging schema.

4. During development, you frequently need to refresh and transform large datasets, leading to long dbt run times. Which techniques could you explore to improve your development workflow?

A) Temporarily decrease the sample size of data during development, while reverting to full data processing in other environments.
B) Strategically employ ephemeral models in development to avoid materializing intermediate data repeatedly.
C) All of the above.
D) Use development environment settings that incrementally build models only when code changes.

5. You typically use dbt run to execute your project. However, you want to trigger a refresh of only models that depend on newly updated source dat a. How could you achieve this?

A) Manually determine dependent models, then use dbt run -m
B) Configure your sources with the freshness property and run dbt source freshness.
C) Leverage dbt snapshots to identify changed sources and derive the dependent model list.
D) Add dbt run to the post-hook of your upstream ETL process, so it triggers upon data arrival.

Solutions: