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| Section | Weight | Objectives |
|---|---|---|
| Deployment and Orchestration | 15% | - Environments (dev, staging, prod) - Git version control integration - CI/CD with dbt Cloud - Jobs and scheduling in dbt Cloud |
| Models | 25% | - Sources and references - Snapshots - Materializations (table, view, ephemeral, incremental) - Writing and managing SQL models - Seeds |
| Data Transformation Techniques | 25% | - Macros and packages - Refactoring and incremental models - Common table expressions and subqueries - Jinja templating |
| dbt Fundamentals | 15% | - dbt project structure - dbt workflow and best practices - dbt Core vs dbt Cloud |
| Testing and Documentation | 20% | - Schema tests (unique, not_null, accepted_values, relationships) - Custom data tests - Documentation generation - dbt docs and DAG visualization |
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NEW QUESTION # 25
After adding several new models to your project, you want to execute only those models and their direct dependencies. Which command combination best achieves this?
Answer: B
Explanation:
The + syntax with dbt run allows you to specify models and their upstream dependencies. Dbt build encompasses run and test along with snapshot execution.
NEW QUESTION # 26
You have a large dataset that undergoes frequent, small updates. You need a model to quickly reflect these changes while minimizing resource usage. Which standard dbt materialization would be the BEST fit?
Answer: C
Explanation:
Incremental models are ideal for this scenario. They only process the changed data, reducing the overall computational load compared to rebuilding the entire table each time.
NEW QUESTION # 27
You're testing a model containing financial calculations. Your stakeholders require a high degree of accuracy. Which consideration is most important when designing tests for this scenario?
Answer: A,C
Explanation:
B recognizes that financial calculations often have rounding imprecision, necessitating tolerance-based checks. C is crucial as even minor input variations can propagate into significant errors for financial models. A is generally a bad practice; D focuses on quantity, not the specific quality needed here.
NEW QUESTION # 28
28. Consider this DAG:
* model_a # model_c # model_e
* model_b # model_d # model_f
(With model_c and model_d both feeding into the final layer.)
You execute:
dbt run --fail-fast
in production with 2 threads. During the run, model_b and model_c are running in parallel when model_b returns an error.
Assume there are no other errors in the model files, and model_c was still running when model_b failed.
Which model or models will successfully build as part of this dbt run? Choose 1 option.
Answer: A
Explanation:
The --fail-fast flag tells dbt to stop scheduling any new nodes as soon as one node fails. Importantly, dbt does not kill models that are already running; in-flight nodes are allowed to finish.
Here's what happens step by step with 2 threads:
* Roots model_a and model_b start first.
* model_a finishes successfully. That makes model_c eligible to run.
* dbt now runs model_b and model_c in parallel.
* While they are running, model_b fails.
* Because --fail-fast is set, dbt immediately stops scheduling any additional models (like model_d, model_e, or model_f).
* model_c was already running when model_b failed, so it is allowed to complete successfully.
Downstream models of either branch (model_d, model_e, and model_f) never start, because fail-fast prevents any further nodes from being queued after the first failure.
So, the only models that successfully build during this run are:
* model_a (completed before model_b failed)
* model_c (already running at the time of failure and allowed to finish) Hence the correct choice is B: model_a, model_c.
NEW QUESTION # 29
Which two mechanisms allow dbt to write DRY code by reusing logic, preventing writing the same code multiple times?
Choose 2 options.
Answer: A,D
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 # 30
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