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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: dbt Fundamentals | 15% | - dbt project structure - dbt Core vs dbt Cloud - dbt workflow and best practices |
| Topic 2: Testing and Documentation | 20% | - Schema tests (unique, not_null, accepted_values, relationships) - dbt docs and DAG visualization - Custom data tests - Documentation generation |
| Topic 3: Models | 25% | - Materializations (table, view, ephemeral, incremental) - Seeds - Sources and references - Writing and managing SQL models - Snapshots |
| Topic 4: Data Transformation Techniques | 25% | - Refactoring and incremental models - Jinja templating - Macros and packages - Common table expressions and subqueries |
| Topic 5: Deployment and Orchestration | 15% | - CI/CD with dbt Cloud - Environments (dev, staging, prod) - Jobs and scheduling in dbt Cloud - Git version control integration |
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NEW QUESTION # 136
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?
Answer: C
Explanation:
While other options have benefits, dbt's core strength lies in enforcing structure and maintainability in data transformation processes.
NEW QUESTION # 137
You are using seeds in your dbt project for static data lookups. During a dbt seed run, you encounter an error indicating a duplicate primary key value. What are the likely consequences?
Answer: A,B
Explanation:
A and C are correct. Most databases enforce unique primary keys, so the whole dbt seed operation would fail to preserve data integrity. B is incorrect. Seed operations are typically "all or nothing". D is incorrect. dbt treats seeds as important as other model types.
NEW QUESTION # 138
Your dbt_project.yml contains the following: YAML
Answer: A
Explanation:
YAML relies on precise indentation. Materialization should be at the same level as the model name, not nested further.
NEW QUESTION # 139
What must happen before you can build models in dbt?
Choose 1 option.
Answer: A
Explanation:
The correct answer is C: Underlying data must be accessible on your data platform.
dbt does not perform data ingestion or data loading. Instead, dbt operates after raw data is already available in your warehouse. This means that before dbt can build any models-whether staging, intermediate, or mart- layer models-the underlying source data must already exist and be accessible in the connected data platform (Snowflake, BigQuery, Redshift, Databricks, etc.). dbt uses SQL to transform existing relations; therefore, if the data platform cannot access the underlying tables or external sources, model execution will fail.
Option A is incorrect because sources do not need to be defined before building models. Models can be built without using sources at all. Source definitions are optional metadata and lineage declarations, not prerequisites.
Option B is incorrect because service accounts are not required; dbt can connect through any credential mechanism supported by the warehouse (OAuth, user accounts, tokens, etc.).
Option D is incorrect because dbt itself performs transformations on raw data-cleaning raw data beforehand is not required; in fact, that is one of dbt's main responsibilities.
Thus, the only true prerequisite is that the warehouse must contain accessible underlying data.
NEW QUESTION # 140
You want to include a glossary of terms in your dbt project docs so that users can easily reference definitions. How could you achieve this?
Answer: C,D
Explanation:
A: A flexible approach with Jinja macros and a glossary file allows you to dynamically include your glossary in the documentation_ D. Custom macros can provide automation by extracting glossary terms from specifically marked column descriptions.
NEW QUESTION # 141
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