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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
Deployment and Orchestration	- Environments and workflows 1. Version control with Git 2. Development vs production environments - Running dbt in production 1. CI/CD integration patterns 2. dbt Cloud and job scheduling
Documentation and Lineage	- dbt documentation system 1. Model descriptions and metadata 2. Auto-generated docs site - Data lineage understanding 1. Dependency tracking with ref() 2. Directed acyclic graph (DAG)
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
Testing and Data Quality	- Built-in and custom tests 1. Custom SQL tests 2. Generic tests (unique, not null, relationships) - Data validation strategies 1. Schema testing practices 2. CI-based validation workflows

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q156-Q161):

NEW QUESTION # 156
Choose whether these scenarios describe a test or a contract:

Answer:

Explanation:

Explanation:
1. Can only be defined on SQL models
The Answer:
contract
2. Errors are returned before the model is built
The Answer:
contract
3. May be configured to customize severity
The Answer:
test
4. May be run on ephemeral models
The Answer:
test
dbt tests and contracts serve different purposes in ensuring data quality and model correctness.
Tests evaluate data after it is produced, while contracts validate the structure of a model before dbt attempts to build it.
A model contract is schema-level enforcement that describes required columns, data types, and constraints.
Contracts can only be applied to SQL models, not Python or ephemeral models. Because contracts validate the model's schema before executing any SQL, dbt surfaces those errors before the model is built, preventing invalid schemas from being deployed.
In contrast, tests evaluate the data after dbt builds a model. Tests can be written generically (unique, not_null, relationships, accepted_values) or as custom SQL tests.
They run after the model is materialized. Tests also allow severity configuration, enabling warnings instead of failures for less critical issues-something contracts do not support.
Tests also run on ephemeral models, because dbt expands ephemeral SQL inline within downstream models, allowing tests to still execute logically against the resulting compiled SQL. Contracts, however, do not apply because ephemeral models never materialize into database objects.
Thus:
* "SQL-only" and "errors before build" # contract
* "custom severity" and "run on ephemeral models" # test
If you want the next question formatted the same way, send it!

NEW QUESTION # 157
A raw source table undergoes periodic bulk updates that temporarily disrupt dependent models when they are running. Which of the following strategies might help prevent errors in these models? (Choose two)

A. Implement incremental materializations if possible.
B. Schedule dbt jobs to run after the bulk updates are typically complete.
C. Utilize snapshots to capture the state of the raw table before the bulk updates.
D. Add retries with a delay to any 'ref calls referencing this table.

Answer: B,C

Explanation:
Scheduling and snapshots help mitigate timing issues. Incremental models are useful generally but don't address this specific scenario. Retries might mask the disruption instead of resolving it.

NEW QUESTION # 158
A new dataset is loaded into your warehouse as a table daily. You need to create a dbt source for it. Which is the MOST essential element to include in your configuration?

A. An enabled flag set to 'True'
B. A detailed description in the dbt_projectyml.
C. A source freshness check.
D. Schema and table name.

Answer: D

Explanation:
At a minimum, dbt needs to know where to find this source table. Freshness checks and others are valuable but not strictly required for the source to function.

NEW QUESTION # 159
(Multiple Select)

A. The error_after freshness check syntax.
B. Misspelling "orders" as "orderes".
C. Indenting freshness incorrectly under the customers table.
D. Using an 's' at the end of tables.

Answer: B,C

Explanation:
YAML cares about spelling and indentation. The others are valid parts of source definition syntax.

NEW QUESTION # 160
You're setting up a new dbt project on a recently provisioned data warehouse. What actions are essential before your initial development begins?

A. All of the above
B. Establishing access controls and user roles specific to the dbt project's requirements.
C. Defining and implementing a schema and naming conventions for your dbt models within the data warehouse.
D. Configuring monitoring and alerting to track data warehouse usage and events related to dbt jobs

Answer: A

Explanation:
A: Organization is key for maintainability and collaboration. B: Secure access is fundamental from the outset C: Visibility into job execution and resource usage is crucial.

NEW QUESTION # 161
......

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