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| Section | Weight | Objectives |
|---|---|---|
| dbt Fundamentals | 15% | - dbt project structure - dbt Core vs dbt Cloud - dbt workflow and best practices |
| Models | 25% | - Sources and references - Seeds - Writing and managing SQL models - Materializations (table, view, ephemeral, incremental) - Snapshots |
| Data Transformation Techniques | 25% | - Common table expressions and subqueries - Macros and packages - Jinja templating - Refactoring and incremental models |
| Deployment and Orchestration | 15% | - Git version control integration - CI/CD with dbt Cloud - Environments (dev, staging, prod) - Jobs and scheduling in dbt Cloud |
| Testing and Documentation | 20% | - Custom data tests - dbt docs and DAG visualization - Schema tests (unique, not_null, accepted_values, relationships) - Documentation generation |
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NEW QUESTION # 118
You've added detailed descriptions to your sources.yml file. Which of the following scenarios would still necessitate adding descriptions directly within your model files?
Answer: B,C,D
Explanation:
A: Model descriptions allow for tailored explanations on how a source table is specifically used within the context of the model. B: Maintaining historical context of changes directly within the model can be valuable for traceability. C: Differing model use cases can warrant distinct descriptions for the same source table.
NEW QUESTION # 119
What must happen before you can build models in dbt?
Choose 1 option.
Answer: D
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 # 120
You want to experiment with a new column in an existing model without risking production changes. Which configuration strategy in your dbt_project.yml would support this safely?
Answer: D
Explanation:
Model configurations with the enabled property offer a structured way to experiment within the project file.
NEW QUESTION # 121
Which of the following are essential elements of a well-structured pull request description?
Answer: B,C,D
Explanation:
A: Clear titles aid quick understanding of the PRs purpose. C: Explanations help reviewers and future developers understand the thought process behind the code. D: Issue references create traceability and context.
NEW QUESTION # 122
Your team establishes a policy for handling different levels of test failures (e.g., warnings vs. critical errors). How could you implement this policy effectively within your dbt workflows?
Answer: A,B,D
Explanation:
Each provides a mechanism to act upon varying severity levelsA leverages dbt's built-in features- C integrates with external monitoring tools. D offers flexible execution control.
NEW QUESTION # 123
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