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[General] Buy Now and Get Free dbt Labs dbt-Analytics-Engineering Exam Questions Updates

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【General】 Buy Now and Get Free dbt Labs dbt-Analytics-Engineering Exam Questions Updates

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q199-Q204):NEW QUESTION # 199
A project relies on multiple external data sources with varying update frequencies (some daily, some hourly, some near real-time). How might you approach designing your dbt environments to efficiently handle these sources?
  • A. Use separate dbt targets for each source type based on update frequency to optimize run schedules.
  • B. All of the above could contribute to an effective solution.
  • C. Consider staging data in your data warehouse with varying materialization strategies, allowing for streamlined transformation logic in dbt.
  • D. Utilize incremental models and orchestration features provided by dbt to minimize unnecessary reprocessing of data.
Answer: B
Explanation:
A: Separate targets allow for tailored run schedules and resource usage. B: Data staging techniques give granular control over data freshness. C: Incremental models and orchestration enhance efficiency.

NEW QUESTION # 200
A common dimension table is used in 80% of your models. What might be a side effect of frequently changing the structure of this dimension?
  • A. You might need to update a large number of downstream models.
  • B. The dbt docs output would become unmanageable.
  • C. Compile times would significantly increase across the project.
  • D. It would cause errors in models that are not directly dependent on this dimension.
Answer: A
Explanation:
Changing core dimensions has ripple effects on dependent models. While docs could become more complex, the primary issue is maintaining those dependencies.

NEW QUESTION # 201
You've created a new model ref_country_codes. It references a raw table in your data warehouse raw_web_events. To ensure this dependency is valid and up-to-date, you should include which element in your model definition?
  • A. A custom macro to validate data in raw web events
  • B. A source freshness test on raw web events
  • C. A schema test on ref_country_codes
  • D. A dbt seed file populating ref_country_codes
Answer: B
Explanation:
Verifying raw object dependencies is about data freshness. A source freshness test on the upstream source raw_web_events will alert you if that table becomes stale, causing potential issues in your ref_country_codes model.

NEW QUESTION # 202
You're setting up CI/CD for your dbt project to automate deployments. Which aspects of your testing strategy might need adaptation for a production-focused CI/CD workflow?
  • A. Emphasizing tests with longer run times for thorough checks before deployment.
  • B. Disabling all tests that rely on creating new tables, to avoid impacting the production database.
  • C. Including smoke tests that run in production to verify core functionality after a deployment.
  • D. Prioritizing tests with strict thresholds to avoid false positives from minor data variations in production.
Answer: C,D
Explanation:
B ensures the deployed changes actually work in the real environment. C helps maintain CI/CD flow without unnecessary disruptions. A is counterproductive in a time-sensitive CI/CD context. D would overly limit your testing coverage.

NEW QUESTION # 203
Your baseline comparison tests start failing intermittently. Data distributions change slightly from day to day, sometimes triggering the thresholds. Which strategy could make your tests more robust?
  • A. Rewriting your SQL to compare averages or medians instead of exact value counts-
  • B. Introducing configurable tolerances into your test definitions.
  • C. Removing the baseline comparison tests entirely, as they provide unreliable signals.
  • D. Scheduling the tests to run less frequently (e.g., weekly rather than daily).
Answer: A,B
Explanation:
A makes tests less sensitive to minor noise- B allows for controlled flexibility. C is a workaround, sacrificing freshness. D gives up on a valuable check_

NEW QUESTION # 204
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