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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Testing and Documentation	20%	- Schema tests (unique, not_null, accepted_values, relationships) - Custom data tests - dbt docs and DAG visualization - Documentation generation
Topic 2: Deployment and Orchestration	15%	- Environments (dev, staging, prod) - Git version control integration - Jobs and scheduling in dbt Cloud - CI/CD with dbt Cloud
Topic 3: Models	25%	- Snapshots - Writing and managing SQL models - Materializations (table, view, ephemeral, incremental) - Sources and references - Seeds
Topic 4: dbt Fundamentals	15%	- dbt workflow and best practices - dbt Core vs dbt Cloud - dbt project structure
Topic 5: Data Transformation Techniques	25%	- Common table expressions and subqueries - Macros and packages - Refactoring and incremental models - Jinja templating

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q256-Q261):

NEW QUESTION # 256
You're getting inconsistent errors when adding new models in your dbt_project.yml. You suspect typos or indentation issues. How can you proactively improve this situation?

A. Set up a pre-commit hook that runs a YAML validation script before committing changes.
B. Write tests covering various aspects of dbt_project.yml to proactively catch errors.
C. Use a code editor or IDE with YAML-specific linting and auto-formatting support.
D. All of the above.

Answer: D

Explanation:
Each of these plays a role in preventing YAML errors.

NEW QUESTION # 257
Which two code snippets result in a lineage line being shown in the DAG? Choose 2 options.

A. dbt_jsmith.stg_jaffle_shop__customers
B. {{ source('source', 'table') }}
C. {{ target.schema }}
D. {{ ref('stg_jaffle_shop__customers') }}
E. {{ source('jaffle_shop.customers') }}

Answer: B,D

Explanation:
In dbt, the Directed Acyclic Graph (DAG) is created from model dependencies, which dbt identifies only through two mechanisms: the ref() function for referencing dbt models and the source() function for referencing declared sources. These functions are explicitly designed to tell dbt how models relate to each other so that dbt can track lineage, enforce build order, and generate documentation.
Option A, {{ source('source', 'table') }}, correctly references a declared source. Any model using source() establishes an upstream dependency on that source table. dbt will therefore draw a lineage line in the DAG from the source to the model.
Option B, {{ ref('stg_jaffle_shop__customers') }}, is the canonical way to reference another dbt model. ref() ensures dbt determines dependencies at compile time, and thus creates a DAG edge between this model and its parent.
The remaining options do not create lineage:
* C directly references a database object, which dbt cannot interpret as a dependency.
* D uses a Jinja variable unrelated to lineage.
* E is invalid, because source() requires two arguments: a source name and table name, not a dotted string.
Therefore, A and B are the only valid answers.

NEW QUESTION # 258
A common dimension table is used in 80% of your models. What might be a side effect of frequently changing the structure of this dimension?

A. You might need to update a large number of downstream models.
B. The dbt docs output would become unmanageable.
C. It would cause errors in models that are not directly dependent on this dimension.
D. Compile times would significantly increase across the project.

Answer: A

Explanation:
Changing core dimensions has ripple effects on dependent models. While docs could become more complex, the primary issue is maintaining those dependencies.

NEW QUESTION # 259
You suspect discrepancies between a raw data source and its corresponding dbt representation. Which of the following would NOT directly help in pinpointing the cause of this mismatch?

A. Examining any source freshness warnings or errors in recent dbt runs.
B. Reviewing the source definition within your dbt_project.yml .
C. Checking if any custom macros or tests are modifying the source data.
D. Comparing the dbt-generated SQL against the raw table's SELECT statement.

Answer: A

Explanation:
While freshness warnings might hint at problems, they don't directly reveal data mismatches- Checking the actual SQL against the raw source and project configurations is key.

NEW QUESTION # 260
(Multiple Select)

A. Implementing more rigorous access controls and auditing procedures.
B. Potentially running on a different data warehouse or with different database settings.
C. Using credentials with appropriate permissions (likely read-only) to access production data sources.
D. Scheduling models to run outside normal business hours to reduce resource contention.

Answer: A,B,C

Explanation:
A reflects the increased security needs of production environments. B allows scaling or optimization based on workload. C prevents accidental changes in production. D is a strategy, but not a fundamental difference.

NEW QUESTION # 261
......

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