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| Section | Weight | Objectives |
|---|---|---|
| Deployment and Orchestration | 15% | - Git version control integration - CI/CD with dbt Cloud - Jobs and scheduling in dbt Cloud - Environments (dev, staging, prod) |
| Testing and Documentation | 20% | - Custom data tests - Schema tests (unique, not_null, accepted_values, relationships) - dbt docs and DAG visualization - Documentation generation |
| Data Transformation Techniques | 25% | - Jinja templating - Macros and packages - Common table expressions and subqueries - Refactoring and incremental models |
| Models | 25% | - Snapshots - Sources and references - Materializations (table, view, ephemeral, incremental) - Writing and managing SQL models - Seeds |
| dbt Fundamentals | 15% | - dbt Core vs dbt Cloud - dbt workflow and best practices - dbt project structure |
>> Actual dbt-Analytics-Engineering Test <<
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NEW QUESTION # 234
Which command materializes my_model and only its first-degree parent model(s) in the data platform?
Choose 1 option.
Answer: B
Explanation:
The correct answer is D: dbt run --select +1 my_model.
In dbt's selection syntax, the + operator is used to include parents (upstream) and/or children (downstream) of a given node. When used as +my_model or my_model+, it means "include all ancestors" or "all descendants" respectively, with no limit on depth.
To limit how many levels of parents or children are included, dbt allows a numeric depth between the + and the resource name. +1 my_model (or my_model+1) means:
* Select my_model
* Plus only its first-degree parents (direct dependencies), and no further ancestors.
Because you want to materialize the models, you must use dbt run, not dbt compile. dbt compile only compiles SQL and does not create or update relations in the data platform.
So:
* Option A (dbt run --select +my_model) would include all upstream ancestors, not just first-degree parents.
* Options B and E use compile, so they don't materialize.
* Option C is just syntactically wrong.
Therefore, the only command that both runs models and limits selection to my_model plus its first-degree parents is dbt run --select +1 my_model.
NEW QUESTION # 235
You've defined a unique test on a key column within a model. When running dbt test, you encounter a surprising failure. Which of the following might be the cause?
Answer: A,B,C,D
Explanation:
All of these could cause a previously passing test to fail, highlighting the interplay between data, model logic, and test definitions.
NEW QUESTION # 236
You need to roll out a database migration mid-month. It requires changes to some of your dbt model schemas. How would you manage a smooth transition?
Answer: C
Explanation:
C allows for gradual rollout and controlled testing. A' would lead to downtime, B potentially too much downtime, and D increases maintenance overhead.
NEW QUESTION # 237
You discover that a heavily used model is taking significantly longer to materialize. After investigation, you determine that the complexity of the model's logic is a contributing factor. Which of the following actions is LEAST likely to help improve performance?
Answer: C
Explanation:
Switching from a table to a view alone doesn't address the core performance issue of complex logic. Other options prioritize breaking down the transformations or directly optimizing the SQL itself.
NEW QUESTION # 238
A dbt run failed with an error message.
Order these steps to fix your pipeline.
Answer:
Explanation:
Explanation:
Check your terminal or log file for the output from the most recent dbt run.
Look for specific error messages associated with the models that failed.
3## Isolate the problem by dbt run --select model_name to run a single model and confirm whether the issue is localized to that model.
4## Use dbt run --select model_name+ to run the model and its downstream dependencies, ensuring that your fix works across the DAG.
Brief Explanation
* First, you always inspect the latest run output (step 1).
* Then, identify the exact failing models and error messages (step 2).
* Next, you reproduce the issue on the individual model to be sure the fix works locally (step 3).
* Finally, you re-run the model plus its downstream dependencies to validate the fix across the DAG (step 4).
NEW QUESTION # 239
......
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