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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: 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. Role of dbt in analytics engineering 2. Warehouse-centric transformation workflows
Topic 2: dbt Core Concepts	- Project structure and configuration 1. Packages and dependencies 2. dbt_project.yml configuration - Models and materializations 1. Views, tables, incremental models 2. Ref and source functions
Topic 3: Deployment and Orchestration	- Environments and workflows 1. Development vs production environments 2. Version control with Git - Running dbt in production 1. CI/CD integration patterns 2. dbt Cloud and job scheduling
Topic 4: Documentation and Lineage	- Data lineage understanding 1. Dependency tracking with ref() 2. Directed acyclic graph (DAG) - dbt documentation system 1. Auto-generated docs site 2. Model descriptions and metadata
Topic 5: Testing and Data Quality	- Built-in and custom tests 1. Generic tests (unique, not null, relationships) 2. Custom SQL tests - 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 (Q119-Q124):

NEW QUESTION # 119
You need to run a dbt job that performs a long-running data cleansing operation. The job occasionally times out. Which of the following are potential solutions?

A. Schedule the job to run concurrently with other dbt models to maximize resource usage-
B. Break the cleansing logic into multiple models, chaining them with dependencies-
C. Use a dbt macro to retry the cleansing operation with a backoff strategy within the model itself-
D. Increase the timeout setting in your CI/CD tool or dbt job configuration.

Answer: B,C,D

Explanation:
Each tackles the timeout issue differently:A reduces the work done in a single step. B provides more time directly . C adds retry resilience within the logic. D would likely worsen performance due to resource contention.

NEW QUESTION # 120
(Multiple Select)

A. Running dbt tests in parallel with dbt model builds whenever possible-
B. Configuring tests to run only on models that have changed or have upstream changes.
C. Designing tests, where feasible, to examine subsets of data rather than full tables.
D. Scheduling less critical tests to run during off-peak hours.

Answer: B,C,D

Explanation:
A targets tests to the most relevant areas, reducing workload. C leverages less busy periods to avoid resource contention. D reduces individual test runtimes through sampling. B is generally inadvisable, as testing and model building might compete for resources.

NEW QUESTION # 121
You've created a complex lineage macro, and you want to include unit tests for the macro logic itself. How might you approach this?

A. Include test cases for the macro within your model-level schema.yml files.
B. Utilize dbt's built-in macro testing framework.
C. Write a Jinja script that programmatically tests various input/output scenarios of your macro.
D. Currently, there is no standard way to directly unit test macros within a dbt project

Answer: C

Explanation:
While dbt doesn't have a dedicated macro testing framework, Jinja provides ways to construct tests. However, there's no built-in mechanism to run these as part of your typical dbt workflow.

NEW QUESTION # 122
You need a very niche data transformation that no existing packages address. When should you consider building your own dbt package?

A. If the transformation logic is complex and likely to be valuable to others in the dbt community.
B. When strict versioning and control of the functionality are critical for your project
C. Only after you have extensively searched for potential alternative solutions.
D. Whenever you need to encapsulate custom SQL logic for reuse across models.

Answer: A

Explanation:
The primary benefit of creating a custom package lies in sharing it with others. If it's solely for your project, macros or well-structured models might suffice

NEW QUESTION # 123
(Multiple Select)

A. Validating changes during development before deploying a new dbt model version.
B. Testing performance under a simulated heavy load.
C. Ensuring data synchronization after a migration.
D. Catching subtle errors or discrepancies that may not cause immediate functional issues.

Answer: A,C,D

Explanation:
These tests help ensure alignment between the source of truth and the analytics environment. D (performance testing) is a different type of test.

NEW QUESTION # 124
......

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