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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: Deployment and Orchestration	- Running dbt in production 1. CI/CD integration patterns 2. dbt Cloud and job scheduling - Environments and workflows 1. Development vs production environments 2. Version control with Git
Topic 2: Documentation and Lineage	- dbt documentation system 1. Model descriptions and metadata 2. Auto-generated docs site - Data lineage understanding 1. Directed acyclic graph (DAG) 2. Dependency tracking with ref()
Topic 3: Analytics Engineering Foundations	- Modern data stack concepts (ELT vs ETL) 1. Role of dbt in analytics engineering 2. Warehouse-centric transformation workflows - SQL proficiency for analytics 1. Joins, aggregations, and window functions 2. Data modeling in SQL
Topic 4: dbt Core Concepts	- Models and materializations 1. Views, tables, incremental models 2. Ref and source functions - Project structure and configuration 1. dbt_project.yml configuration 2. Packages and dependencies
Topic 5: Testing and Data Quality	- Data validation strategies 1. Schema testing practices 2. CI-based validation workflows - Built-in and custom tests 1. Generic tests (unique, not null, relationships) 2. Custom SQL tests

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q32-Q37):

NEW QUESTION # 32
Data Quality Issue

A. Run dbt test on the model and see if existing tests fail, indicating a possible regression.
B. Add specific schema tests to the model, asserting that the column in question should not contain nulls.
C. Run dbt compile followed by dbt run -select model_name to isolate the model and observe its output closely.
D. Inspect any filters or transformations within the model's SQL that might be unintentionally excluding data.

Answer: A,B,C,D

Explanation:
This requires a comprehensive approach covering logic errors, regression tests, proactive quality checks, and isolated execution.

NEW QUESTION # 33
Your dbt project pulls data from a SaaS application using a third-party adapter. You observe performance issues. Where might you start optimizing?

A. Configure the source as an incremental materialization within your project file.
B. Check your dbt_project.yml for project-level performance settings.
C. Review the source definitions to see if unnecessary data is being selected.
D. Investigate the third-party adapter's settings or documentation for potential bottlenecks.

Answer: D

Explanation:
Third-party adapters often introduce bottlenecks. Start with optimizing whats specific to the adapter The others might help in some cases but are less directly dependent on the external data source.

NEW QUESTION # 34
You discover that dbt models create objects with the default database user's permissions. However, a policy mandates that analytics objects must be owned by a dedicated, less privileged account. How could you adapt your dbt workflow?

A. Modify your dbt profile to use the less privileged account's credentials during job execution.
B. Write dbt macros that contain permission management logic as part of the model deployment.
C. Implement pre-hooks or post-hooks to execute ALTER OWNER statements after model creation.
D. Configure the target database to automatically assign desired ownership to new objects in the schema

Answer: A,C

Explanation:
A gives direct control within dbt C changes what dbt "does" by running as a different user- B might exist but be too broad, D overcomplicates models.

NEW QUESTION # 35
(Multiple Select)

A. Integrating dbt with your CI/CD tool and using job-specific triggers or filters.
B. Implementing pre-hooks and post-hooks to perform tasks before and after dbt model execution.
C. Scheduling less frequent jobs using dbt Cloud's built-in job scheduler
D. Using separate dbt profiles for production and development environments

Answer: A,C,D

Explanation:
A enables environment-specific commands. B provides a way to launch different jobs. C handles scheduled tasks within dbt Cloud.

NEW QUESTION # 36
You discover that a heavily used model is taking significantly longer to materialize. After investigation, you determine that the complexity of the model's logic is a contributing factor. Which of the following actions is LEAST likely to help improve performance?

A. Change the materialization from a table to a view.
B. Consider applying an incremental materialization strategy if suitable.
C. Break down the model into smaller, intermediate models using CTEs.
D. Investigate potential optimizations in the underlying SQL code.

Answer: A

Explanation:
Switching from a table to a view alone doesn't address the core performance issue of complex logic. Other options prioritize breaking down the transformations or directly optimizing the SQL itself.

NEW QUESTION # 37
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