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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Testing and Data Quality	- Data validation strategies 1. Schema testing practices 2. CI-based validation workflows - Built-in and custom tests 1. Generic tests (unique, not null, relationships) 2. Custom SQL tests
Analytics Engineering Foundations	- Modern data stack concepts (ELT vs ETL) 1. Role of dbt in analytics engineering 2. Warehouse-centric transformation workflows - SQL proficiency for analytics 1. Data modeling in SQL 2. Joins, aggregations, and window functions
Deployment and Orchestration	- Running dbt in production 1. CI/CD integration patterns 2. dbt Cloud and job scheduling - Environments and workflows 1. Development vs production environments 2. Version control with Git
Documentation and Lineage	- Data lineage understanding 1. Dependency tracking with ref() 2. Directed acyclic graph (DAG) - dbt documentation system 1. Model descriptions and metadata 2. Auto-generated docs site
dbt Core Concepts	- Models and materializations 1. Ref and source functions 2. Views, tables, incremental models - Project structure and configuration 1. dbt_project.yml configuration 2. Packages and dependencies

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q217-Q222):

NEW QUESTION # 217
(Multiple Select)

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

Answer: B,C

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

NEW QUESTION # 218
You're collaborating on a project where team members have varying levels of dbt experience. How can you use the dbt_project.yml to help standardize practices?

A. Configure project-level tests to ensure adherence to data quality standards.
B. Include a docs: section with detailed descriptions of core models and transformations.
C. Set up pre-commit hooks within the project file to enforce linting and formatting rules.
D. Define custom macros in the project file to encapsulate commonly used SQL patterns.

Answer: A

Explanation:
Project-level tests in dbt_project.yml provide a centralized way to maintain data quality expectations across the team.

NEW QUESTION # 219
You encounter an unexpected dbt run error. Rather than re-running all models, you want to resume from the point of failure. Which of the following, if any, might allow for this?

A. dbt unfortunately lacks native support for resumable jobs.
B. Using dbt snapshots to track model states, allowing you to rollback changes as needed
C. The --fail-fast flag instructs dbt to only execute successful models, making restarts unnecessary
D. dbt Cloud's job retry features for automatically resuming failed job executions.

Answer: D

Explanation:
C can retry in event of transient errors, but not fundamental code problems. The rest are incorrect dbt typically isn't resumable mid-job.

NEW QUESTION # 220
A marketing team needs a model to calculate the 30-day rolling average of new customer signups. The raw signups table is very large and updated hourly. Which approach strikes the best balance between accuracy and computational efficiency?

A. A table materialization that completely rebuilds the rolling average daily.
B. An incremental model that updates the rolling average calculations only for new signup records.
C. An ephemeral model that calculates the rolling average within a series of complex CTEs.
D. A view materialization that recalculates the 30-day rolling average for each query.

Answer: B

Explanation:
Incremental models are designed for metrics like this. They minimize processing overhead while ensuring the rolling average stays up-to-date. Options A and B are computationally expensive, while D doesn't address storage for reuse.

NEW QUESTION # 221
You wrote this test against your fct_orders model to confirm status filters were properly applied by a parent model:
{{
config(
enabled=true,
severity='error'
)
}}
select *
from {{ ref('fct_orders') }}
where status_code = 13
Which statement about this test is true?

A. This test will warn instead of erroring when you use the --warn-error flag.
B. Records with status_code = 13 will cause this test to fail.
C. The file must be saved in the tests/generic directory.
D. You must attach the test to the model in orders.yml for it to run.

Answer: B

Explanation:
This test is a singular test, meaning it is defined as a standalone SQL file placed inside the /tests directory (not attached through YAML like generic tests). dbt executes singular tests by running the SQL query, and the test fails if the query returns any rows. In this case, the test selects all records from fct_orders where status_code = 13. That means if even one row in fct_orders has a status code of 13, the test will fail, because dbt interprets returned rows as evidence of a data-quality issue.
The configuration block at the top sets severity='error', meaning the test raises an error when it fails, rather than a warning. This setting overrides default severity behavior, so option B is incorrect. Singular tests do not need to be attached in YAML because they run automatically when placed under /tests # making option A incorrect. Meanwhile, generic tests live in /tests/generic, but singular tests should live directly under /tests (option C incorrect).
Therefore, the only correct statement is that records with status_code = 13 will cause the test to fail, because the test query is explicitly written to identify such rows.

NEW QUESTION # 222
......

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