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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Implementing dbt Tests	10%	- Test configuration and execution - Custom tests - Built-in tests
Topic 2: Debugging and Error Resolution	15%	- Resolving data quality issues - Debugging techniques - Identifying modeling errors
Topic 3: Creating and Maintaining Documentation	10%	- Descriptions and metadata - Documentation standards - Generating documentation
Topic 4: dbt Models Governance	15%	- Naming conventions and standards - Version control integration - Project organization and structure
Topic 5: External Dependencies	10%	- Managing snapshots - Using packages - External sources integration
Topic 6: Leveraging dbt State	5%	- State-aware operations - State management
Topic 7: Managing Data Pipelines	15%	- Deployment strategies - CI/CD integration - Pipeline orchestration
Topic 8: Developing dbt Models	20%	- Model optimization 1. Performance tuning 2. Model contracts - Model design and structure 1. Incremental models 2. Ref and source functions 3. Materialization types

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q302-Q307):

NEW QUESTION # 302
Business requirements call for a report ranking products based on year-over-year (YoY) revenue growth. Historical data must be preserved. What's a potential issue in implementing this directly in a dbt model?

A. ref() functions can hinder performance when working with historical data.
B. Incremental materializations aren't suitable for trend analysis.
C. Views cannot perform complex date manipulation required for YoY calculations.
D. Data warehouses might not be optimized for period-over-period comparisons.

Answer: D

Explanation:
YoY calculations can strain database performance. Consider strategies like pre-aggregating data at the month level. Others issues are less likely to be a bottleneck.

NEW QUESTION # 303
Which two configuration items can be defined under models: in your dbt_project.yml file?
Choose 2 options.

A. target
B. schema
C. test
D. source
E. tags

Answer: B,E

NEW QUESTION # 304
Your dbt project contains sensitive dat
a. You need to load seed data (e.g., lookup tables) while ensuring it's not accidentally committed to version control. How can you best achieve this?

A. Employ a secrets management tool and integrate it with your dbt deployment.
B. Write the seed data into log files, excluding them from version control.
C. Create separate seed files and add them to your .gitignore or equivalent.
D. Utilize environment variables to store seed data values.

Answer: C

Explanation:
Seed files designed for local use should be kept out of version control. While the other options have merits, they don't directly prevent sensitive data from being committed.

NEW QUESTION # 305
(Multiple Select)

A. Avoid refactoring dbt models that are directly queried by external tools.
B. Instruct dbt to rename columns rather than dropping and recreating them during updates.
C. Create views on top of dbt model tables, providing a stable abstraction layer for the Bl tools.
D. Establish a naming convention in dbt that external tools can rely upon.

Answer: C

Explanation:
B decouples the tools from the underlying model. A is database dependent, C is fragile, D inhibits improvement

NEW QUESTION # 306
(Multiple Select)

A. The nested CASE statement with repeated conditional checks is causing redundant calculations.
B. Dynamically generating column names in a loop is inefficient for most databases.
C. The fct_orders table might lack an appropriate index for the warehouse_code column.
D. The Jinja templating itself introduces a significant processing overhead.

Answer: A,B

Explanation:
Dynamic column creations and repeated CASEevaluations often hinder database optimization. Indexes matter too, while Jinja itself is less likely the core bottleneck.

NEW QUESTION # 307
......

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