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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Analytics Engineering Foundations	- SQL proficiency for analytics 1. Data modeling in SQL 2. Joins, aggregations, and window functions - Modern data stack concepts (ELT vs ETL) 1. Warehouse-centric transformation workflows 2. Role of dbt in analytics engineering
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
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
Documentation and Lineage	- dbt documentation system 1. Auto-generated docs site 2. Model descriptions and metadata - Data lineage understanding 1. Directed acyclic graph (DAG) 2. Dependency tracking with ref()
Testing and Data Quality	- Data validation strategies 1. CI-based validation workflows 2. Schema testing practices - 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 (Q194-Q199):

NEW QUESTION # 194
(Multiple Select)

A. Macros for fiscal period calculations or string manipulations.
B. A custom adapter to connect to a niche data source.
C. Snapshots that track the state of your sources on a schedule.
D. Models addressing a domain-specific use case (e.g., subscription analytics).

Answer: A,D

Explanation:
Packages excel at providing reusable macros and domain-specific model sets. Snapshots and adapters are usually independent of or extend the core dbt feature set.

NEW QUESTION # 195
You're setting up a development environment in a cloud data warehouse with usage-based billing. How might you approach cost management in this context?

A. Set up usage alerts in your data warehouse that notify the team if costs exceed certain thresholds.
B. Use dbt seeds to load smaller datasets for testing, reducing overall data processing costs.
C. Implement automated scripts to shut down the development environment during non-working hours.
D. Create a separate profiles.yml specifically for the development environment that utilizes smaller data warehouse resource classes.

Answer: A

Explanation:
A). Controlling uptime reduces unnecessary costs. B. Right-sizing data warehouse resources helps align usage and cost C: Smaller datasets lower compute and storage costs. D: Proactive alerts prevent unexpected cost spikes.

NEW QUESTION # 196
A colleague has introduced a new sensitive column into a source table. You need to update the dbt project documentation accordingly. Which of the following actions should you take?

A. Add the column description and tag it using a custom sensitive tag in the YAML file.
B. Consult your organization's data governance policies and consider additional measures beyond simple documentation.
C. Update the source documentation and add references to that documentation in the appropriate model's YAML file
D. Add a clear description of the sensitive column in its corresponding YAML file.

Answer: A,B,C

Explanation:
B). Tagging sensitive data helps categorize it, allowing for potential filtering or access control within the documentation. C: Handling sensitive data goes beyond simple documentation. Its crucial to follow your organization's data security and governance protocols. D: Referencing source documentation provides full context and traceability of the sensitive data's origin.

NEW QUESTION # 197
You need to test for consistency between a raw source table and its corresponding dbt model after transformation. This requires comparing many columns for null counts and matching value distributions. Which approach would be most efficient?

A. Manually writing SQL queries to separately calculate and compare these metrics.
B. Relying entirely on generic not_null and unique tests.
C. Creating a custom dbt test that leverages statistical comparison techniques.
D. Using dbt's built-in source freshness tests for a high-level check.

Answer: C

Explanation:
A is tedious, B is too coarse-grained, and D is insufficient. A custom test designed for this comparison provides automation and the needed specificity.

NEW QUESTION # 198
Your dbt models create tables, but you notice a consistent "_dbt" suffix appended to all table names. Where does this likely originate from, and how could you modify it?

A. It's a naming convention enforced by the database engine, not directly controlled by dbt
B. This behavior is configurable in your dbt_projectyml settings related to model schemas.
C. There's likely a custom macro in your codebase adding the suffix
D. Its a default behavior of dbt to help distinguish model names from pre-existing tables.

Answer: B,C,D

Explanation:
A is how dbt works by default C could override the names, and D has the power to change the default suffix.

NEW QUESTION # 199
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