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Which is true about writing generic tests?
Choose 1 option.
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E
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Explanation
The correct answer is E: They are written using the {% macro %} wrapper. Generic tests in dbt are implemented as macros, which means they must be defined using the {% macro %} Jinja syntax. These macros return a SQL query that evaluates to rows representing test failures. dbt executes these queries and marks the test as failed if any rows are returned. This requirement is explicitly documented: all generic tests are macros located in a test directory or within a package. Option A is partially correct but not universal. Generic tests typically accept arguments such as model or column_name, but dbt does not require both or either. Custom tests can accept any parameters defined by the user. Option B is incorrect because tests do not require ref(); dbt injects the model relation for you when the test is executed. Option C is false because generic tests must be declared in YAML under a model or source for dbt to run them. Option D is incorrect because tests do not always need a column_name (e.g., relationship tests, row-count tests, table-level validations). Therefore, the defining and universally required characteristic is that generic tests are written as macros, making E the correct answer. |
Which two dbt commands work with dbt retry?
Choose 2 options.
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A, E
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Explanation
The correct answers are A: run-operation and E: snapshot. According to dbt’s retry documentation, the dbt retry command works by examining the artifacts of a previous invocation (manifest, run results) and then re-running failed nodes for commands that generate node executions. Only commands that write run results and maintain execution state can be retried. These include dbt run, dbt test, dbt seed, dbt snapshot, and dbt run-operation when the operation executes nodes or macros that generate runtime artifacts. Option A is correct because run-operation supports retry when the macro being run triggers execution tracked in run results. Option E is correct because snapshots execute SQL against the warehouse and record stateful results, meaning dbt can retry failed snapshots using the retry mechanism. Options B (parse), C (debug), and D (deps) do not create runnable nodes or execution results; they simply validate or install project resources. These commands do not produce retryable artifacts, so dbt retry cannot operate on them. Thus, the only options that work with dbt retry from the list provided are run-operation and snapshot. |
Which two mechanisms allow dbt to write DRY code by reusing logic, preventing writing the same code multiple times?
Choose 2 options.
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B, D
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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. |
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Find answers to the most common questions about the dbt Labs dbt-Analytics-Engineering exam, including what it is, how to prepare, and how it can boost your career.
The dbt Labs dbt-Analytics-Engineering certification is a globally-acknowledged credential that is awarded to candidates who pass this certification exam by obtaining the required passing score. This credential attests and validates the candidates' knowledge and hands-on skills in domains covered in the dbt Labs dbt-Analytics-Engineering certification syllabus. The dbt Labs dbt-Analytics-Engineering certified professionals with their verified proficiency and expertise are trusted and welcomed by hiring managers all over the world to perform leading roles in organizations. The success in dbt Labs dbt-Analytics-Engineering certification exam can be ensured only with a combination of clear knowledge on all exam domains and securing the required practical training. Like any other credential, dbt Labs dbt-Analytics-Engineering certification may require periodic renewal to stay current with new innovations in the concerned domains.