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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You have a large dataset that undergoes frequent, small updates. You need a model to quickly reflect these changes while minimizing resource usage. Which standard dbt materialization would be the BEST fit?

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

2. You discover a downstream model, dim products, references an outdated raw dataset, raw_product_archive, intended for historical analysis and not active products. What's the most appropriate corrective action?

A) Update the dim_products model to reference the active product dataset.
B) Delete the raw_product_archive source, so it can't be used accidentally.
C) Leave it as-is since it's not actively causing any errors currently.
D) Add a schema test to dim_products to catch potential errors.

3. A project relies on multiple external data sources with varying update frequencies (some daily, some hourly, some near real-time). How might you approach designing your dbt environments to efficiently handle these sources?

A) Utilize incremental models and orchestration features provided by dbt to minimize unnecessary reprocessing of data.
B) Use separate dbt targets for each source type based on update frequency to optimize run schedules.
C) Consider staging data in your data warehouse with varying materialization strategies, allowing for streamlined transformation logic in dbt.
D) All of the above could contribute to an effective solution.

4. A project stakeholder prefers a text-based lineage representation over the graphical DAG. Which of these approaches might be a suitable alternative?

A) Disable the DAG visualization entirely within the generated documentation.
B) Utilize a macro that generates a Markdown list representation of model dependencies.
C) Extend the dbt documentation framework to create a custom text-based lineage view
D) Rely on third-party tools that specialize in parsing dbt projects to produce textual lineage outputs

5. (Multiple Select)

A) Avoiding excessive use of UNION operations, especially within loops.
B) Leveraging appropriate indexes on columns used in joins and WHERE clauses.
C) Preferring correlated subqueries over joins when relationships are one-to-one.
D) Using SELECT * whenever possible to avoid explicitly specifying needed columns.

Solutions: