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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Data Transformation Techniques	25%	- Common table expressions and subqueries - Macros and packages - Refactoring and incremental models - Jinja templating
Deployment and Orchestration	15%	- Jobs and scheduling in dbt Cloud - Environments (dev, staging, prod) - CI/CD with dbt Cloud - Git version control integration
Testing and Documentation	20%	- Schema tests (unique, not_null, accepted_values, relationships) - Documentation generation - dbt docs and DAG visualization - Custom data tests
dbt Fundamentals	15%	- dbt workflow and best practices - dbt project structure - dbt Core vs dbt Cloud
Models	25%	- Materializations (table, view, ephemeral, incremental) - Snapshots - Seeds - Sources and references - Writing and managing SQL models

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q185-Q190):

NEW QUESTION # 185
A newly-hired analyst needs to update a downstream model that depends on a chain of intermediate models. To minimize the potential for disruption, how can modularity help them?

A. Snapshots ensure that historical data won't be impacted unintentionally.
B. Incremental materializations will help verify that the changes are successful.
C. By using macros, modifications are limited to a single location.
D. With clear dependencies, they can pinpoint which upstream models to test.

Answer: D

Explanation:
Modularity and well-defined dependencies make it easier to isolate the impact of a change, simplifying updates and testing.

NEW QUESTION # 186
You discover a downstream model, dim products, references an outdated raw dataset, raw_product_archive, intended for historical analysis and not active products. What's the most appropriate corrective action?

A. Update the dim_products model to reference the active product dataset.
B. Leave it as-is since it's not actively causing any errors currently.
C. Delete the raw_product_archive source, so it can't be used accidentally.
D. Add a schema test to dim_products to catch potential errors.

Answer: A

Explanation:
Directly addressing the root cause ensures your model uses the correct source. Schema tests are safeguards but don't resolve the underlying dependency issue. Be cautious about deleting sources unless there's a very strong reason.

NEW QUESTION # 187
You have strict data retention policies. Which factors might influence how data is managed differently across your production, development, and raw data environments?

A. Raw data might be archived or deleted following a shorter retention period than production data
B. Development data might have less stringent retention requirements, allowing for more frequent deletion-
C. Regulatory requirements could mandate that production data be retained for longer periods.
D. All of the above.

Answer: D

Explanation:
A: Raw data can often be discarded post-processing, reducing storage costs. B: Regulations often dictate long-term retention for production data. C: Development data is often transient and subject to less strict retention.

NEW QUESTION # 188
You are building an incremental model.
Identify the circumstances in which is_incremental() would evaluate to True or False.

Answer:

Explanation:

Explanation:
1. There is a config block at the top of the model with materialized = 'table'
## Answer: False
2. The corresponding table already exists in the data warehouse
## Answer: True
3. The incremental model was executed with the --full-refresh flag
## Answer: False
4. The corresponding table does not already exist in the data warehouse
## Answer: False
The is_incremental() macro in dbt is used within an incremental model to determine whether the current invocation should perform an incremental update or a full rebuild. It evaluates to True only during an incremental run when the target table already exists in the data warehouse and dbt is not performing a full refresh.
For is_incremental() to return True, two conditions must be met:
* The model must be materialized as incremental, not table or view.Therefore, if the config specifies materialized = 'table', the model is not incremental, and the function evaluates to False.
* The table already exists in the target schema.In this case, dbt will run the model in incremental mode, making is_incremental() return **True`.
If the incremental model is run with --full-refresh, dbt intentionally rebuilds the entire table, meaning is_incremental() evaluates to False, even if the table exists.
Lastly, if the target table does not exist yet, dbt must create it from scratch, so the run is treated as a full rebuild, not an incremental update, causing is_incremental() to evaluate to False.
Thus, the only scenario where is_incremental() returns True is when the table exists and the model is actually incremental.

NEW QUESTION # 189
Code Review

A. An updated lineage graph produced by dbt docs generate to visualize the fix's impact on dependencies.
B. Additional schema tests on the model to prevent similar issues from recurring.
C. A second reviewer assigned specifically to assess the potential side effects of the fix.
D. An explanation of the error's root cause and the reasoning behind your chosen solution.

Answer: D

Explanation:
Contextualizing the fix is crucial during code review. Docs, tests, and additional reviews are fantastic but might not always be feasible within time constraints.

NEW QUESTION # 190
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