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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q135-Q140):

NEW QUESTION # 135
What must happen before you can build models in dbt?
Choose 1 option.

Answer: B

Explanation:
The correct answer is C: Underlying data must be accessible on your data platform.
dbt does not perform data ingestion or data loading. Instead, dbt operates after raw data is already available in your warehouse. This means that before dbt can build any models-whether staging, intermediate, or mart- layer models-the underlying source data must already exist and be accessible in the connected data platform (Snowflake, BigQuery, Redshift, Databricks, etc.). dbt uses SQL to transform existing relations; therefore, if the data platform cannot access the underlying tables or external sources, model execution will fail.
Option A is incorrect because sources do not need to be defined before building models. Models can be built without using sources at all. Source definitions are optional metadata and lineage declarations, not prerequisites.
Option B is incorrect because service accounts are not required; dbt can connect through any credential mechanism supported by the warehouse (OAuth, user accounts, tokens, etc.).
Option D is incorrect because dbt itself performs transformations on raw data-cleaning raw data beforehand is not required; in fact, that is one of dbt's main responsibilities.
Thus, the only true prerequisite is that the warehouse must contain accessible underlying data.


NEW QUESTION # 136
Unexpected Results

Answer: A

Explanation:
Start by gaining situational awareness (logs), pinpointing bottlenecks (profiling), and then analyzing the code change itself (diff). Revert if needed but only after some investigation.


NEW QUESTION # 137
A raw source table undergoes periodic bulk updates that temporarily disrupt dependent models when they are running. Which of the following strategies might help prevent errors in these models? (Choose two)

Answer: C,D

Explanation:
Scheduling and snapshots help mitigate timing issues. Incremental models are useful generally but don't address this specific scenario. Retries might mask the disruption instead of resolving it.


NEW QUESTION # 138
During development, you frequently need to refresh and transform large datasets, leading to long dbt run times. Which techniques could you explore to improve your development workflow?

Answer: A

Explanation:
A: Incremental models optimize execution when changes are isolated. B: Ephemeral models avoid unnecessary persistence for temporary steps. C: Sampling decreases data volumes, speeding up development runs.


NEW QUESTION # 139
Your dbt project pulls data from a SaaS application using a third-party adapter. You observe performance issues. Where might you start optimizing?

Answer: C

Explanation:
Third-party adapters often introduce bottlenecks. Start with optimizing whats specific to the adapter The others might help in some cases but are less directly dependent on the external data source.


NEW QUESTION # 140
......

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