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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Models	25%	- Seeds - Writing and managing SQL models - Sources and references - Snapshots - Materializations (table, view, ephemeral, incremental)
Topic 2: dbt Fundamentals	15%	- dbt workflow and best practices - dbt project structure - dbt Core vs dbt Cloud
Topic 3: Data Transformation Techniques	25%	- Macros and packages - Refactoring and incremental models - Jinja templating - Common table expressions and subqueries
Topic 4: Deployment and Orchestration	15%	- CI/CD with dbt Cloud - Git version control integration - Jobs and scheduling in dbt Cloud - Environments (dev, staging, prod)
Topic 5: Testing and Documentation	20%	- Custom data tests - dbt docs and DAG visualization - Documentation generation - Schema tests (unique, not_null, accepted_values, relationships)

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q193-Q198):

NEW QUESTION # 193
You're using the dbt-utils.get_column_values macro to list out the unique values from a specific source column in the documentation. However, the generated docs don't display any results. Which of the following reasons are most likely?

A. The macro itself might have a bug or an issue with how it's being used
B. There is a limit on how many unique values the macro can display within the documentation.

Answer: A,B

Explanation:
A: Some macros are designed specifically for model columns and may not function as expected with source columns. B: Macros might depend on data from a recent dbt run operation to generate their output. C: It's always possible that the macro has a bug or your implementation is not quite correct.

NEW QUESTION # 194
You've included the popular 'dbt-utils' package in your project. Where should you primarily expect to find the models and macros it offers?

A. Within the 'seeds' directory of your dbt project.
B. Automatically materialized as new tables in your data warehouse.
C. Within your 'models' directory namespaced to distinguish them from your own models.
D. In the 'dbt_project.yml' file under a 'packages' section.

Answer: C

Explanation:
Packages extend your project's functionality by placing items within your model's directory offering seamless integration.

NEW QUESTION # 195
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)

A. Implement incremental materializations if possible.
B. Utilize snapshots to capture the state of the raw table before the bulk updates.
C. Add retries with a delay to any 'ref calls referencing this table.
D. Schedule dbt jobs to run after the bulk updates are typically complete.

Answer: B,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 # 196
You discover that an essential dbt macro behaves differently between your development environment and production due to a subtle database version discrepancy. Which debugging approach would be most efficient?

A. Use database-specific functions to query the version information directly from within dbt models.
B. Set up a CIICD test that runs the macro in isolation using both database versions.
C. Add verbose logging within the macro to track execution differences step by step.
D. Temporarily introduce a conditional branch into the macro code that is environment-aware.

Answer: A,B,C

Explanation:
Each of these helps identify the root cause:A provides runtime insight into the issue. C lets you surface the relevant version for comparison. D allows for reproducible testing.

NEW QUESTION # 197
While reviewing a dbt project, you notice a pattern where ref() calls are chained together:
Model A references Model B, which in turn references Model C.
What's a potential downside of this pattern?

A. Decreased performance due to unnecessary materializations.
B. Test results might become less reliable.
C. Version control conflicts become more likely.
D. Model dependencies become harder to track and understand.

Answer: D

Explanation:
While chaining ref() calls works technically, it can quickly make dependency tracking overly complex, especially in larger projects.

NEW QUESTION # 198
......

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