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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q241-Q246):

NEW QUESTION # 241
A new stakeholder needs to receive an email report generated from the results of a specific dbt model. Unfortunately, your Bl tool doesn't directly integrate with dbt. How could you address this?

A. Modify the model to output a CSV file and schedule a task to email the file as an attachment
B. Within the model's SQLi write logic to insert the output data into a dedicated reporting table-
C. Develop a separate script, triggered after the dbt job, to handle report generation and emailing-
D. Write a post-hook that queries the model, formats the results, and sends the email.

Answer: B,C,D

Explanation:
Each provides a means to get the data outA does it within dbt workflow B uses an external tool for the non- dbt task. C leaves the data ready for other tools. D is clumsy and adds storage overhead.

NEW QUESTION # 242
You have strict data retention policies. Which factors might influence how data is managed differently across your production, development, and raw data environments?

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

Answer: A

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 # 243
(Multiple Select)

A. Encapsulating complex logic in well-named macros with descriptive comments.
B. Using clear and consistent naming conventions across models and macros.
C. Preferring long, monolithic SQL files with minimal CTEs.
D. Minimizing explicit model dependencies to create loosely coupled components.

Answer: A,B

Explanation:
Well-documented macros and consistent naming boost readability. Large monolithic SQL files hinder maintainability.

NEW QUESTION # 244
Several models perform similar filtering and aggregation on a frequently used source table. What's the most effective way to streamline this process and improve maintainability?

A. Create an ephemeral model to prepare the data in a common format, referenced by downstream models.
B. Create a table materialization to hold the pre-processed data.
C. Create a snapshot to capture the pre-processed state of the source table.
D. Replicate the filtering and aggregation logic directly within each model.

Answer: A

Explanation:
Ephemeral models within CTEs allow for reusable intermediate steps in data transformation and are perfect for avoiding logic duplication across multiple models.

NEW QUESTION # 245
You've defined a model as follows:SQL

A. A pure SQL issue: The count() function isn't supported for the data type of a column in the 'cust_orders' source.
B. An incorrect materialization strategy causing the wrong data to be populated in a downstream source.
C. A data type mismatch between a column in 'stg_customers' and how the query is using it for the join.
D. A Jinja templating error: Misusing the source() function within the count() function.

Answer: A

Explanation:
While the others might cause problems, this error message strongly indicates a database- level type incompatibility, not a dbt-specific issue.

NEW QUESTION # 246
......

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