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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt-Analytics-Engineering New Study Questions - dbt-Analytics-Engineering Valid Test Braindumps

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q19-Q24):

NEW QUESTION # 19
You've identified the same complex date calculations repeated across multiple models. Which approach aligns with modularity and DRY (Don't Repeat Yourself) principles?

A. Introduce a new intermediate table to store the result of the date calculations.
B. Continue copying the date calculation logic into each relevant model.
C. Create a macro, encapsulating the date calculations, and reference it in the affected models.
D. Write a custom Python function and import it into your dbt project.

Answer: C

Explanation:
Macros excel in encapsulating reusable logic, promoting modularity and avoiding code repetition. While option C could work, it introduces unnecessary materialization.

NEW QUESTION # 20
When integrating a new data source, you notice many existing models could benefit from it. What's the most maintainable way to incorporate the new data within a DRY approach?

A. Add the new data source directly to each relevant model's SQL query.
B. Build a snapshot of the new data combined with relevant data from existing models.
C. Establish a naming convention for the new data elements to avoid hardcoding them within model SQL.
D. Create a base model that incorporates the new data source and use the ref() function in downstream models.

Answer: D

Explanation:
A base model serves as a modular point of integration for the new data. The ref() function promotes DRY principles and eases maintenance if the new source structure changes.

NEW QUESTION # 21
You debug a production issue by running a model in a testing environment with additional logging output. Which dbt features or command-line additions would facilitate this?

A. dbt hooks to redirect model-related logs to a centralized logging platform.
B. The -warn-error flag to treat warnings as errors, making your run fail louder on potential issues.
C. Custom dbt flags (using -vars) to control the logging level of the SQL execution.
D. Environment variables to conditionally enable verbose logging macros inside the model.

Answer: B,C,D

Explanation:
These provide control during execution:A changes model behavior. B makes execution more sensitive. C alters dbt's log handling.

NEW QUESTION # 22
You run a dbt job that heavily utilizes CT Es (Common Table Expressions). In your development environment, it works flawlessly. However, in production (a different database technology), it throws errors. Which potential causes should you investigate?

A. Your production environment has resource constraints that impact the execution of complex queries.
B. CTE performance optimizations are automatically applied in development, but not in production.
C. This indicates a bug in dbt's cross-database compatibility layer.
D. The production database has limitations or different syntax conventions related to CTEs.

Answer: A,D

Explanation:
Acan lead to different behavior under load. B different dialects have nuances about CTE support C is unlikely, D less probable than a dialect-specific issue.

NEW QUESTION # 23
You need to test whether the values of a specific column have changed over time. What might be the best approach to implement this kind of test within your dbt project?

A. Use a snapshot to preserve a point-in-time reference of the column.
B. Use a custom 'accepted_values' test together with a dbt seed.
C. rely on native version history features in your data warehouse (if available).
D. Write a dbt macro to create a temporary table with historical data to compare against.

Answer: A,C,D

Explanation:
A provides a clean comparison point C enables more complex historical analysis. D leverages built-in warehouse features, if you have them.

NEW QUESTION # 24
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