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| Section | Weight | Objectives |
|---|---|---|
| dbt Fundamentals | 15% | - dbt workflow and best practices - dbt Core vs dbt Cloud - dbt project structure |
| Models | 25% | - Sources and references - Seeds - Materializations (table, view, ephemeral, incremental) - Snapshots - Writing and managing SQL models |
| Testing and Documentation | 20% | - Custom data tests - Documentation generation - dbt docs and DAG visualization - Schema tests (unique, not_null, accepted_values, relationships) |
| Data Transformation Techniques | 25% | - Macros and packages - Jinja templating - Refactoring and incremental models - Common table expressions and subqueries |
| Deployment and Orchestration | 15% | - Git version control integration - Jobs and scheduling in dbt Cloud - Environments (dev, staging, prod) - CI/CD with dbt Cloud |
1. (Multiple Select)
A) Configure separate dbt jobs with different schedules tailored to the model groups.
B) Have all models run nightly but use where conditions within models to limit work when data is unchanged.
C) Employ dbt snapshots to selectively run only models that depend on changed sources.
D) Use dbt tags to group models based on update frequency
2. (Multiple Select)
A) Validating that the row count of the staging model falls within an expected range.
B) Ensuring column ordering matches an external specification.
C) Checking for referential integrity with a separate lookup table.
D) Enforcing specific ratios or relationships between aggregated values.
3. You discover a downstream model, dim products, references an outdated raw dataset, raw_product_archive, intended for historical analysis and not active products.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) Leave it as-is since it's not actively causing any errors currently.
B) Add a schema test to dim_products to catch potential errors.
C) Update the dim_products model to reference the active product dataset.
D) Delete the raw_product_archive source, so it can't be used accidentally.
4. Your dbt project pulls data from a SaaS application using a third-party adapter. You observe performance issues. Where might you start optimizing?
A) Check your dbt_project.yml for project-level performance settings.
B) Investigate the third-party adapter's settings or documentation for potential bottlenecks.
C) Review the source definitions to see if unnecessary data is being selected.
D) Configure the source as an incremental materialization within your project file.
5. (Multiple Select)
A) Changes in the structure or content of the baseline dataset itself.
B) A change in database vendor for your data warehouse.
C) Natural drift in the distribution of real-world data compared to the static baseline.
D) Changes in business logic lead to intentional deviations from the baseline.
Solutions:
| Question # 1 Answer: A,D | Question # 2 Answer: A,C,D | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: A,C,D |
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