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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q202-Q207):

NEW QUESTION # 202
You have strict data retention policies. Which factors might influence how data is managed differently across your production, development, and raw data environments?

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

Answer: D

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 # 203

Answer:

Explanation:

Explanation:
Information Type
Retrieved From
Singular tests
.sql files
Column data types
Data platform information schema
Generic tests
.yml configuration
SQL code
C. .sql files
Column descriptions
.yml configuration
Model dependencies
.sql files
The dbt docs command compiles metadata about your project by gathering information from three primary sources: your warehouse's information schema, your YAML configuration files, and your SQL model files. Understanding which metadata comes from which source is essential for debugging and for effective documentation practices.
Singular tests live inside .sql files within the /tests directory. Since dbt renders these tests directly from SQL files, their definitions appear in documentation sourced from that location.
Column data types come from the warehouse itself. dbt introspects the data platform information schema to retrieve actual types because dbt does not infer or define column types-only the warehouse does.
Generic tests (e.g., unique, not_null, accepted_values) are declared in .yml files. These YAML definitions contain test configurations, descriptions, and parameters, which dbt uses to document and execute these tests.
SQL code for models is naturally sourced from .sql files where the models are defined. This includes logic such as SELECT statements, CTEs, and transformations.
Column descriptions are written exclusively in .yml files. dbt never extracts descriptions from SQL comments-only from YAML.
Model dependencies come from the ref() and source() calls inside .sql model files, which dbt parses to build the DAG.

NEW QUESTION # 204
You have a dbt project with models defined as views. After deploying to a production database, the view objects are present, but no data is displayed when querying them. What might be the root cause?

A. Permissions granted in production don't allow reading data from the underlying tables.
B. Your dbt job didn't run successfully, so the views were created empty.
C. There's a bug in dbt's SQL generation that specifically affects views in certain environments.
D. Dbt view materializations behave differently in production.

Answer: A

Explanation:
B is a common security-related cause of 'missing' data. The others are less likely; views, logs, and dbt core logic tend to be consistent.

NEW QUESTION # 205
Data Quality Issue

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

Answer: A,B,C,D

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

NEW QUESTION # 206
You're collaborating on a project where team members have varying levels of dbt experience. How can you use the dbt_project.yml to help standardize practices?

A. Configure project-level tests to ensure adherence to data quality standards.
B. Include a docs: section with detailed descriptions of core models and transformations.
C. Define custom macros in the project file to encapsulate commonly used SQL patterns.
D. Set up pre-commit hooks within the project file to enforce linting and formatting rules.

Answer: A

Explanation:
Project-level tests in dbt_project.yml provide a centralized way to maintain data quality expectations across the team.

NEW QUESTION # 207
......

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