dbt Labs dbt Analytics Engineering Certification : dbt-Analytics-Engineering

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

Question #1

A model intermittently fails. Sometimes it runs without errors. Which category of issue should you suspect FIRST?

A. Errors within complex macros used by the model, triggered only under specific data conditions.
B. dbt-specific configuration errors causing unpredictable behavior at the engine level.
C. Data quality issues in the source that are not consistently present.
D. Race conditions due to models in your DAG running concurrently without proper synchronization.

Question #2

You encounter the following error when running dbt compile:Runtime Error in model customer _ churn (models/transformations/customer_churn.sql)

A. The fct_orders model depends on other models that haven't compiled successfully.
B. The fct_orders source is not configured correctly in your dbt_project.yml file.
C. You need to add a schema test to validate the existence of the fct orders table.
D. There's a typo in how you're referencing the fct_orders object in your SQL.

Question #3

You're working with a dataset where a column is expected to be updated frequently with newly arriving events. Which type of test would be MOST suitable to check that this process is working as intended?

A. A generic 'not_null' test on the column.
B. A schema test on the column in the source table.
C. A test comparing the row count of the source table between different dbt runs.
D. A custom test comparing timestamps on the column in your dbt model to the current time.

Question #4

You encounter an unexpected dbt run error. Rather than re-running all models, you want to resume from the point of failure. Which of the following, if any, might allow for this?

A. dbt Cloud's job retry features for automatically resuming failed job executions.
B. dbt unfortunately lacks native support for resumable jobs.
C. Using dbt snapshots to track model states, allowing you to rollback changes as needed
D. The --fail-fast flag instructs dbt to only execute successful models, making restarts unnecessary

Question #5

(Multiple Select)

A. Require thorough code reviews with a focus on testability before deploying model changes to production.
B. Using dbt Cloud's built-in job monitoring and notification features, if applicable.
C. Integrating dbt's logging with your existing monitoring and alerting platforms.
D. Implement custom metrics tracking the duration and resource usage of individual dbt models.

Solutions: