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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Data Transformation Techniques	25%	- Jinja templating - Refactoring and incremental models - Common table expressions and subqueries - Macros and packages
Models	25%	- Snapshots - Materializations (table, view, ephemeral, incremental) - Seeds - Sources and references - Writing and managing SQL models
dbt Fundamentals	15%	- dbt Core vs dbt Cloud - dbt project structure - dbt workflow and best practices
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%	- Custom data tests - Schema tests (unique, not_null, accepted_values, relationships) - dbt docs and DAG visualization - Documentation generation

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Actual dbt Labs dbt-Analytics-Engineering Exam Questions in PDF

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q97-Q102):

NEW QUESTION # 97
Which of the following scenarios would likely require you to use the -no-compile flag with the dbt docs generate command?

A. Adding a new model and its corresponding description in your project.
B. Updating existing column descriptions to reflect recent upstream changes.
C. Running the documentation generation process in a production setting where compilation could introduce risks.
D. You've made syntax errors in your model definitions and want to prevent documentation errors due to compilation failures.

Answer: C

Explanation:
The -no-compile flag skips model compilation and is useful when you only want to update the docs without the risk of model compilation affecting a production environment.

NEW QUESTION # 98
you're working with a dbt project where a central model fct_orders is refactored. Several downstream models rely on this fact table. Which of the following is the MOST efficient way to determine which downstream models might be affected by this change?

A. Manually review the code of all downstream models.
B. Run dbt test to see which tests might fail.
C. Use the dbt Is command to generate a lineage graph.
D. Execute dbt run and observe for errors.
E. Utilize the dbt docs generate and dbt docs serve commands.

Answer: E

Explanation:
While options B, D, and E can indirectly hint at broken models, they're not the most efficient methods. The dbt docs commands allow you to directly visualize and explore dependencies within your dbt project, making it the ideal choice for this scenario.

NEW QUESTION # 99
You get a warning during dbt run:WARNlNG: Found 100 rows in 'stg_customers' that will become duplicates in the 'dim_customers' target.

A. Increase the memory allocated for the database to handle the large number of duplicates.
B. It's safe to ignore this warning, as de-duplication is handled automatically by dbt.
C. Update the stg_customers source definition to include filtering logic to remove duplicate rows.
D. Investigate your dim_customers modeling logic to ensure a unique key is correctly used to prevent duplicates.

Answer: D

Explanation:
This typically indicates a logic flaw in your model creating the target table. Modifying the source or increasing memory won't resolve the core issue.

NEW QUESTION # 100
You've written dbt tests that assume a certain distribution of values in a column. Later, the upstream process generating the data changes unexpectedly. What's the most likely consequence?

A. The changed data distribution has no impact on downstream dbt models.
B. Your dbt models may silently produce incorrect results due to the changed distribution.
C. The tests will fail, preventing the changed data from being loaded, preserving the old behavior.
D. dbt will automatically adjust your tests to accommodate the new distribution.

Answer: B

Explanation:
This highlights the risk of outdated assumptions. Without failing tests, you may not even realize your analysis is now based on flawed data.

NEW QUESTION # 101
You're refactoring a complex SQL query into a dbt model. You notice that the model's output has significantly fewer rows than expected. Which of the following are potential causes?

A. Upstream data has quality issues with null values or unexpected joins.
B. An incorrect materialization setting on the model.
C. Your model has accidental WHERE conditions that are too restrictive.
D. There may be a LIMIT statement leftover from development.

Answer: A,C,D

Explanation:
All except D could limit the result set more than expected. Materialization settings impact how tables are built, not the SQL logic itself.

NEW QUESTION # 102
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