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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deployment and Orchestration | 15% | - Jobs and scheduling in dbt Cloud - Git version control integration - CI/CD with dbt Cloud - Environments (dev, staging, prod) |
| Topic 2: Models | 25% | - Sources and references - Snapshots - Materializations (table, view, ephemeral, incremental) - Seeds - Writing and managing SQL models |
| Topic 3: dbt Fundamentals | 15% | - dbt project structure - dbt Core vs dbt Cloud - dbt workflow and best practices |
| Topic 4: Data Transformation Techniques | 25% | - Common table expressions and subqueries - Refactoring and incremental models - Jinja templating - Macros and packages |
| Topic 5: Testing and Documentation | 20% | - dbt docs and DAG visualization - Documentation generation - Custom data tests - Schema tests (unique, not_null, accepted_values, relationships) |
dbt Labs dbt Analytics Engineering Certification Sample Questions:
1. 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) Your production environment has resource constraints that impact the execution of complex queries.
B) CTE performance optimizations are automatically applied in development, but not in production.
C) The production database has limitations or different syntax conventions related to CTEs.
D) This indicates a bug in dbt's cross-database compatibility layer.
2. A colleague has made updates to some model descriptions and wants you to quickly regenerate the docs to only include those changes. Which command variation would be the most efficient without a full recompilation?
A) dbt docs generate -select [model_name]
B) Utilize dbt's incremental model feature along with dbt docs generate -no-compile.
C) It's not possible to partially regenerate dbt docs; you need to run the full dbt docs generate command.
D) dbt docs generate --models +[model_namel
3. Your dbt models create tables, but you notice a consistent "_dbt" suffix appended to all table names. Where does this likely originate from, and how could you modify it?
A) It's a naming convention enforced by the database engine, not directly controlled by dbt
B) This behavior is configurable in your dbt_projectyml settings related to model schemas.
C) Its a default behavior of dbt to help distinguish model names from pre-existing tables.
D) There's likely a custom macro in your codebase adding the suffix
4. Several models perform similar filtering and aggregation on a frequently used source table. What's the most effective way to streamline this process and improve maintainability?
A) Create a table materialization to hold the pre-processed data.
B) Replicate the filtering and aggregation logic directly within each model.
C) Create an ephemeral model to prepare the data in a common format, referenced by downstream models.
D) Create a snapshot to capture the pre-processed state of the source table.
5. You define a source table and include an override:YAML
A) The value 'my_project' is not a recognized keyword by dbt.
B) The overrides property is not valid for tables within your dbt_project.yml.
C) The overrides property needs to be defined at the source level, not the table level.
D) You need to create a macro named 'my_project' that contains the specific overriding logic.
Solutions:
| Question # 1 Answer: A,C | Question # 2 Answer: A | Question # 3 Answer: B,C,D | Question # 4 Answer: C | Question # 5 Answer: C |
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