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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: dbt Fundamentals | 15% | - dbt workflow and best practices - dbt Core vs dbt Cloud - dbt project structure |
| Topic 2: Data Transformation Techniques | 25% | - Refactoring and incremental models - Jinja templating - Macros and packages - Common table expressions and subqueries |
| Topic 3: Models | 25% | - Materializations (table, view, ephemeral, incremental) - Writing and managing SQL models - Snapshots - Sources and references - Seeds |
| Topic 4: Testing and Documentation | 20% | - dbt docs and DAG visualization - Schema tests (unique, not_null, accepted_values, relationships) - Documentation generation - Custom data tests |
| Topic 5: Deployment and Orchestration | 15% | - Jobs and scheduling in dbt Cloud - Environments (dev, staging, prod) - CI/CD with dbt Cloud - Git version control integration |
>> New dbt-Analytics-Engineering Test Topics <<
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NEW QUESTION # 130
Is this materialization supported by Python models in dbt?
Ephemeral
Answer: A
Explanation:
dbt Python models support a limited set of materializations because they rely on execution within the data platform's Python compute engine (such as Snowpark for Snowflake, Dataproc for BigQuery, or Spark).
These engines require models to materialize into actual relations-tables or views-in order to persist the results of Python-based transformations.
The ephemeral materialization, however, is fundamentally incompatible with this behavior. Ephemeral models do not create relations in the warehouse; instead, dbt inlines their SQL logic directly into downstream models. Since Python models cannot be inlined (they execute Python code, not SQL), dbt does not allow ephemeral Python models. dbt requires Python model outputs to be materialized as either:
* table
* view
* incremental
Therefore, ephemeral is not supported for Python models, and attempting to configure a Python model as ephemeral will result in a compilation error.
The reason is straightforward: ephemeral logic depends on SQL compilation, while Python models depend on executing Python code in the data platform. Because these mechanisms are incompatible, dbt restricts Python models to relational materializations only.
Thus, the correct answer is No - ephemeral is not supported for Python models.
NEW QUESTION # 131
A user reports a critical security issue discovered in a third-party dbt package you use. Where could you most likely find updated information about potential vulnerabilities or fixes?
Answer: C
Explanation:
Package maintainers are the primary source of truth- However, options like A and C might provide additional resources:A. Your packages.yml might help identify the package C: dbt Cloud might aggregate some vulnerability information.
NEW QUESTION # 132
You want to add new columns to an existing model with a new version.
What will trigger warnings for removal of older model versions?
Choose 1 option.
Answer: A
Explanation:
dbt includes built-in support for model versioning and controlled deprecation. When you introduce a new version of a model, dbt will only warn about the old version being removed if the old version explicitly includes a deprecation_date configuration. This parameter signals to dbt that the model is scheduled for removal after a certain date, allowing dbt to produce structured warnings to users and downstream systems.
These warnings serve as a formal notice that the old version should no longer be referenced and will eventually be removed.
Option A, simply creating a new version, does not trigger warnings by itself. dbt allows multiple versions to coexist without any deprecation messaging unless you explicitly configure it. Option C (enabled: false) disables the model entirely, which is not a graceful deprecation strategy and does not result in version- removal warnings. Option D (adding "deprecated" in the description) is only informational text and has no functional effect on dbt's deprecation system.
Thus, only B-setting deprecation_date on the old version-activates dbt's official deprecation warning mechanism, making it the correct answer.
NEW QUESTION # 133
Your project has models calculating order metrics. Some models aggregate data daily; others do monthly roll-ups. What's a likely performance issue if all models are materialized as tables?
Answer: C
Explanation:
Rebuilding pre-aggregated data is inefficient. While testing becomes slower (D), the primary concern is computational waste.
NEW QUESTION # 134
A teammate opens a pull request with numerous changes across many files. To help with the review process, you would suggest that they:
Answer: A
Explanation:
A: Smaller PRs allow for more focused and effective reviews. B: Clear commit messages help understand the step-by-step changes within the PR. C: A summary provides a bird's-eye view for the reviewer.
NEW QUESTION # 135
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