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dbt Labs dbt Analytics Engineering Certification Sample Questions:
1. You have a computationally expensive dbt model that creates a wide table with many columns. You realize that downstream analysis typically only needs a small subset of these columns. How might you optimize this for both
A) Implement a post-hook that immediately deletes unused columns from the model's output table.
B) Configure a dbt job to run as a lower priority task, accepting slower processing.
C) Increase memory and CPU resources allocated to the data warehouse to power through the large build-
D) Refactor the model to use a view materialization, selecting only necessary columns.
2. You're working with a development data warehouse meant to closely mirror production. To make code more portable between environments, you opt to use environment variables for connection details. What's the best way to manage these?
A) Use a dedicated secrets manager integrated with your dbt project.
B) Store sensitive credentials in plain text in the dbt_project.yml file, relying on file system permissions.
C) Define environment variables as part of your CI/CD tool's job configuration.
D) Set environment variables manually in your shell session prior to running dbt commands.
3. You introduce performance optimizations to a subset of models. You need to benchmark both old and new versions in an isolated environment before deployment. How might you structure your dbt commands for this comparison?
A) Utilize dbt seed to populate separate datasets representing the old and new model outputs for side-by-side analysis.
B) Add dbt hooks to temporarily archive the original output tables before overwriting them during benchmark runs.
C) Within a model, conditionally write to different schemas based on an environment variable, preserving both versions.
D) Use feature branches and switch between them with different dbt profiles for old and new code.
4. (Multiple Select)
A) Consider common reporting needs when designing downstream models.
B) Materialize all models as 'views' for maximum flexibility and to avoid potential storage bottlenecks.
C) Introduce CTEs within models to create logical groupings of transformations.
D) Minimize the number of intermediate models, even if it means complex SQL within a few large models.
5. A colleague asks, "Why go through the trouble of using dbt? Can't we just write SQL scripts and schedule them?" Which of the following is the most compelling argument for using dbt?
A) dbt automatically generates user-friendly data lineage visualizations.
B) dbt models are typically more performant than hand-written SQL scripts.
C) dbt promotes modularity, reusable code, and testing within data transformation pipelines.
D) dbt offers native support for advanced scheduling and orchestration features.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A,C | Question # 3 Answer: C,D | Question # 4 Answer: A,C | Question # 5 Answer: C |
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