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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Implementing dbt Tests	10%	- Custom tests - Test configuration and execution - Built-in tests
Leveraging dbt State	5%	- State-aware operations - State management
Managing Data Pipelines	15%	- CI/CD integration - Pipeline orchestration - Deployment strategies
External Dependencies	10%	- External sources integration - Using packages - Managing snapshots
dbt Models Governance	15%	- Version control integration - Naming conventions and standards - Project organization and structure
Developing dbt Models	20%	- Model optimization 1. Performance tuning 2. Model contracts - Model design and structure 1. Materialization types 2. Ref and source functions 3. Incremental models
Creating and Maintaining Documentation	10%	- Documentation standards - Descriptions and metadata - Generating documentation
Debugging and Error Resolution	15%	- Resolving data quality issues - Identifying modeling errors - Debugging techniques

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q13-Q18):

NEW QUESTION # 13
(Multiple Select)

A. A 'post-hook' in your project configuration automatically fixes the issues flagged by tests.
B. Your tests are likely configured to raise warnings rather than cause true errors.
C. Tests must be explicitly included in a model's test property for them to block model creation.
D. Your dbt run command includes flags that override failing tests.

Answer: B,D

Explanation:
A is a common pattern to allow non-critical tests to provide information without necessarily halting the process. C Certain dbt flags can force execution even when tests fail. B is incorrect; tests apply without model-specific references. D is unlikely (unless you have very sophisticated automation')

NEW QUESTION # 14
You need to regularly run performance tests to benchmark a time-sensitive incremental model. These tests involve more complex setup than standard dbt tests. How could you efficiently integrate them into your workflow?

A. Define the performance tests as their own dbt project, triggered separately-
B. Create a dbt operation (macro) to encapsulate the test logic and invoke it as needed.
C. Write the tests as generic tests and use dbt's built-in warning thresholds.
D. Convert them into dbt seeds to easily execute them alongside model runs-

Answer: A,B

Explanation:
A provides separation and specialized configuration if needed. C offers reusability and the ability to run from within a dbt workflow. B (seeds) isnt suitable for complex testing logic. D (generic tests) lack the power required for performance benchmarking.

NEW QUESTION # 15
Examine the configuration for the source:
sources:
- name: jaffle_shop
schema: jaffle_shop_raw_current
tables:
- name: orders
identifier: customer_orders
Which reference to the source is correct?

A. {{ source('jaffle_shop_raw_current', 'orders') }}
B. {{ source('jaffle_shop_raw_current', 'customer_orders') }}
C. {{ source('jaffle_shop', 'customer_orders') }}
D. {{ source('jaffle_shop', 'orders') }}

Answer: D

Explanation:
In dbt, the source() function resolves a source by its declared source name and table name, not by the physical schema or identifier in the warehouse. The YAML block defines a source named jaffle_shop, and under that source, a table named orders. The identifier: customer_orders field tells dbt that although the logical table name is orders, the actual physical object in the warehouse is named customer_orders.
dbt always expects the syntax:
{{ source(source_name, table_name) }}
Here, the correct reference uses jaffle_shop as the source name and orders as the table name because these are the logical names assigned in the YAML. dbt internally resolves the physical table name via the identifier field, so the model should not reference customer_orders directly.
Option A and B are incorrect because the first argument is not the schema; dbt does not use schemas in the source() call. Option D is incorrect because customer_orders is the warehouse identifier, not the logical table name recognized by dbt.
Therefore, the correct reference is:
{{ source('jaffle_shop', 'orders') }}
This ensures consistent modeling, dependency tracking, and accurate documentation.

NEW QUESTION # 16
You're working in a dbt Cloud development environment. You make a change to a model definition that accidentally introduces an invalid column reference. What's the likely sequence of events when you trigger a dbt run?

A. The job will fail during compilation, and a clear error message will point to the invalid reference.
B. The job will succeed but produce incorrect output due to the invalid reference.
C. The job execution state will depend on the specific SQL dialect of your data warehouse.
D. The job will succeed, as dbt only validates schema changes at deployment time.

Answer: A

Explanation:
dbt performs pre-execution validation, catching many schema errors early. Behavior in C is less likely and D shouldnt be so inconsistent.

NEW QUESTION # 17
(Multiple Select)

A. Run dbt Is to confirm that all dependencies for the macro and the model are resolved correctly.
B. Implement unit tests specifically for the macro to validate its output under different inputs
C. Check the generated column names in the compiled SQL to ensure they match existing columns in the data.
D. Add {{ log(macros, info=true) statements within the macro to track how it executes-

Answer: B,C

Explanation:
Validate the generated SQL output from the macro, and use dedicated tests to ensure the macro's logic is sound. Logging can be helpful, and dbt Is is generally for dependency checking, not dynamic object validation.

NEW QUESTION # 18
......

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