dbt Labs dbt-Analytics-Engineering Test Braindumps | Exam dbt-Analytics-Engineering Sample

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: Analytics Engineering Foundations	- SQL proficiency for analytics 1. Joins, aggregations, and window functions 2. Data modeling in SQL - Modern data stack concepts (ELT vs ETL) 1. Warehouse-centric transformation workflows 2. Role of dbt in analytics engineering
Topic 2: 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
Topic 3: Deployment and Orchestration	- Running dbt in production 1. CI/CD integration patterns 2. dbt Cloud and job scheduling - Environments and workflows 1. Version control with Git 2. Development vs production environments
Topic 4: dbt Core Concepts	- Project structure and configuration 1. dbt_project.yml configuration 2. Packages and dependencies - Models and materializations 1. Views, tables, incremental models 2. Ref and source functions
Topic 5: 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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q114-Q119):

NEW QUESTION # 114
You're tasked with creating a model for audit purposes. The data must be preserved exactly as it was at a specific point in time. Update frequency is low, and there's no need for additional transformations on the captured dat a. What materialization should you use?

A. View
B. Incremental
C. Snapshot
D. Table

Answer: C

Explanation:
Snapshots are designed for historical preservation. They capture the state of a source at a moment in time, providing a perfect audit trail.

NEW QUESTION # 115
(Multiple Select)

A. Loading data to fact tables as part of the regular ETL process.
B. Adding test records to validate a new custom data test.
C. Inserting rows for a generic date dimension table.
D. Populating a staging environment with representative data for testing.

Answer: C,D

Explanation:
dbt seed is ideal for static data, often used in development and testing. Fact tables usually rely on upstream data sources.

NEW QUESTION # 116
When merging code on a remote repository hosting platform (like GitHub), you notice options like "squash and merge" and "rebase and merge." Which factors would influence your choice between these options?

A. Whether you want to preserve the feature branch commit history in detail.
B. All of the above.
C. Your team's established conventions and preferences for maintaining the project history.
D. The size and complexity of the changes within the pull request.

Answer: B

Explanation:
A: Squash and merge simplifies history; rebase and merge preserve details. B: Large pull requests might benefit from squashing for a cleaner main branch view. C: Teams should have consistent ways to manage history for ease of collaboration.

NEW QUESTION # 117
You're working with a dataset where a column is expected to be updated frequently with newly arriving events. Which type of test would be MOST suitable to check that this process is working as intended?

A. A schema test on the column in the source table.
B. A generic 'not_null' test on the column.
C. A custom test comparing timestamps on the column in your dbt model to the current time.
D. A test comparing the row count of the source table between different dbt runs.

Answer: C

Explanation:
It focuses on freshness, which is the core concern in this scenario. The other options, while valid, don't target this specific assumption.

NEW QUESTION # 118
(Multiple Select)

A. not_null and unique tests on primary key columns.
B. Schema tests to confirm the existence of tables and columns.
C. Tests checking for row count discrepancies between source systems and target tables.
D. Tests comparing the latest data against historical baselines.

Answer: A,B,C

Explanation:
A, B, and C focus on verifying basic data transfer and structural integrity. D is important, but comes after making sure the data is there at all_

NEW QUESTION # 119
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