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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Testing and Documentation | 20% | - Custom data tests - Schema tests (unique, not_null, accepted_values, relationships) - Documentation generation - dbt docs and DAG visualization |
| Topic 2: dbt Fundamentals | 15% | - dbt Core vs dbt Cloud - dbt workflow and best practices - dbt project structure |
| Topic 3: Deployment and Orchestration | 15% | - Environments (dev, staging, prod) - CI/CD with dbt Cloud - Jobs and scheduling in dbt Cloud - Git version control integration |
| Topic 4: Models | 25% | - Writing and managing SQL models - Sources and references - Materializations (table, view, ephemeral, incremental) - Seeds - Snapshots |
| Topic 5: Data Transformation Techniques | 25% | - Macros and packages - Jinja templating - Refactoring and incremental models - Common table expressions and subqueries |
1. You define a source and later try to reference it. Compilation fails with: "Model depends on a node named 'my source' which was not found". What are some likely causes?
A) All of the above.
B) The source definition is missing or incorrect in your dbt_project.yml or sources file.
C) There's a typo in how you're referencing the source name using source().
D) Your dbt_project.yml configuration has the incorrect source-paths setting.
2. You run a dbt job that heavily utilizes CT Es (Common Table Expressions). In your development environment, it works flawlessly. However, in production (a different database technology), it throws errors. Which potential causes should you investigate?
A) This indicates a bug in dbt's cross-database compatibility layer.
B) The production database has limitations or different syntax conventions related to CTEs.
C) CTE performance optimizations are automatically applied in development, but not in production.
D) Your production environment has resource constraints that impact the execution of complex queries.
3. (Multiple Select)
A) The need for feature parity (functions, syntax) with the production warehouse.
B) The technology used for your production data warehouse.
C) The cost-efficiency of the development environment solution.
D) The overall size and complexity of your dbt project
4. You suspect discrepancies between a raw data source and its corresponding dbt representation. Which of the following would NOT directly help in pinpointing the cause of this mismatch?
A) Examining any source freshness warnings or errors in recent dbt runs.
B) Checking if any custom macros or tests are modifying the source data.
C) Comparing the dbt-generated SQL against the raw table's SELECT statement.
D) Reviewing the source definition within your dbt_project.yml .
5. You've built a macro to implement a common custom test pattern. When using this macro in multiple models, you realize the tests are running much slower than expected. What might be the cause?
A) The macro's SQL logic is inefficiently querying the model or other data.
B) Too many tests are being defined on individual models.
C) The macro is accidentally triggering downstream model rebuilds.
D) This is expected behavior, and custom tests always incur a performance penalty.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B,D | Question # 3 Answer: A,B,C | Question # 4 Answer: A | Question # 5 Answer: A,B |
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