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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?
A. The DAG visualization would be too complex to understand.
B. Unnecessary computations, as monthly models would rebuild daily data redundantly.
C. Testing would become impractical due to increased runtimes.
D. Conflicts might arise due to models overwriting each other's data.
Answer: B
Explanation:
Rebuilding pre-aggregated data is inefficient. While testing becomes slower (D), the primary concern is computational waste.
NEW QUESTION # 200
Outdated Macro
A. Deploy the modified macro to a staging environment and run a representative subset of dbt models.
B. Run dbt Is to identify all locations where the macro is used and ensure compatibility.
C. Write unit tests to validate the macro's output with a variety of input scenarios, including edge cases.
D. Create a new source table specifically for testing the macro without impacting production data.
Answer: A,C
Explanation:
Unit tests isolate the macro's logic, while a staging environment allows for real-world impact assessment. A separate source table might be overkill; dbt Is focuses on dependencies, not functionality.
NEW QUESTION # 201
You're tasked with creating a model for audit purposes. The data must be preserved exactly as it was at a specific point in time. Update frequency is low, and there's no need for additional transformations on the captured dat a. What materialization should you use?
A. Incremental
B. Table
C. Snapshot
D. View
Answer: C
Explanation:
Snapshots are designed for historical preservation. They capture the state of a source at a moment in time, providing a perfect audit trail.
NEW QUESTION # 202
You frequently need to debug model failures mid-DAG during development. Which of the following dbt features would be MOST helpful in this scenario?
A. Macros for modularizing SQL code
B. dbt snapshots
C. The dbt run -fail-fast flag
D. The dbt test command
Answer: C
Explanation:
--fail-fast instructs dbt to stop processing as soon as a model fails, making it great for debugging a chain without processing everything every time.
NEW QUESTION # 203
Which two mechanisms allow dbt to write DRY code by reusing logic, preventing writing the same code multiple times?
Choose 2 options.
A. Writing and using dbt macros
B. Changing a model materialization from view to ephemeral
C. Using dbt packages
D. Creating singular tests
E. Copy/pasting folders containing multiple models
Answer: A,C
Explanation:
The correct answers are B: writing and using dbt macros and D: using dbt packages.
dbt strongly encourages DRY (Don't Repeat Yourself) principles, and two of the core mechanisms that support reusable logic are macros and packages. Macros allow you to write Jinja-powered reusable functions that can generate SQL statements dynamically, reducing duplication across models, tests, and project logic.
Macros can encapsulate filters, joins, auditing logic, timestamps, and more-allowing developers to centralize logic in one place while referencing it across many models.
Packages extend this concept even further by allowing entire sets of macros, models, tests, and utilities to be imported into a project. Packages like dbt-utils contain widely used generic macros that help standardize transformations and testing. Using packages ensures consistent logic across teams and eliminates the need to rewrite common transformations.
Option A contradicts DRY principles because copy/pasting increases maintenance burden. Option C is not a mechanism for reusing logic; singular tests validate logic but do not reduce duplication. Option E simply changes a model's materialization and does not support code reuse.
Thus, macros and packages are the only correct dbt mechanisms that provide reusable, modular, DRY logic.
NEW QUESTION # 204
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