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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
dbt Core Concepts	- Models and materializations 1. Ref and source functions 2. Views, tables, incremental models - Project structure and configuration 1. Packages and dependencies 2. dbt_project.yml configuration
Documentation and Lineage	- Data lineage understanding 1. Directed acyclic graph (DAG) 2. Dependency tracking with ref() - dbt documentation system 1. Auto-generated docs site 2. Model descriptions and metadata
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
Deployment and Orchestration	- Running dbt in production 1. dbt Cloud and job scheduling 2. CI/CD integration patterns - Environments and workflows 1. Development vs production environments 2. Version control with Git
Analytics Engineering Foundations	- Modern data stack concepts (ELT vs ETL) 1. Warehouse-centric transformation workflows 2. Role of dbt in analytics engineering - SQL proficiency for analytics 1. Data modeling in SQL 2. Joins, aggregations, and window functions

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q163-Q168):

NEW QUESTION # 163
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. Comparing the dbt-generated SQL against the raw table's SELECT statement.
B. Examining any source freshness warnings or errors in recent dbt runs.
C. Reviewing the source definition within your dbt_project.yml .
D. Checking if any custom macros or tests are modifying the source data.

Answer: B

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 # 164
You discover that a heavily used model is taking significantly longer to materialize. After investigation, you determine that the complexity of the model's logic is a contributing factor. Which of the following actions is LEAST likely to help improve performance?

A. Consider applying an incremental materialization strategy if suitable.
B. Investigate potential optimizations in the underlying SQL code.
C. Break down the model into smaller, intermediate models using CTEs.
D. Change the materialization from a table to a view.

Answer: D

Explanation:
Switching from a table to a view alone doesn't address the core performance issue of complex logic. Other options prioritize breaking down the transformations or directly optimizing the SQL itself.

NEW QUESTION # 165
You add a new seed file. Here's a snippet of your dbt_project.yml:YAML

A. You cannot enable a seed file using the enabled property.
B. The "+" is incorrect for nested elements in this context
C. Specifying column_types directly under seeds is invalid in a dbt_project_yml.
D. The syntax within the column_types section is incorrect

Answer: C

Explanation:
column_types config applies to individual seeds, not within the general seeds section. The others might be errors in different scenarios but less likely in this case-

NEW QUESTION # 166
You have just executed dbt run on this model:
select * from {{ source("{{ env_var('input') }}", 'table_name') }}
and received this error:
Compilation Error in model my_model
expected token ':', got '}'
line 14
{{ source({{ env_var('input') }}, 'table_name') }}
How can you debug this?

A. Check your SQL to see if you quoted something incorrectly.
B. Take a look at the compiled code.
C. Incorporate a log function into your macro.
D. Check your Jinja and see if you nested your curly brackets.

Answer: D

Explanation:
This error is caused by invalid Jinja syntax, specifically by nesting {{ }} blocks inside another Jinja expression. The expression:
{{ source("{{ env_var('input') }}", 'table_name') }}
compiles to:
{{ source({{ env_var('input') }}, 'table_name') }}
Here, Jinja sees {{ inside another {{ ... }} block. Jinja does not allow nested print statements like this; instead, functions should be called directly inside a single pair of curly braces. The parser encounters an unexpected } where it expects part of a valid expression (hence "expected token ':', got '}'"), which is a classic symptom of mismatched or nested curly braces.
The correct usage is:
select * from {{ source(env_var('input'), 'table_name') }}
In this form, env_var('input') is evaluated first, and its result is passed as the first argument to source() within one Jinja expression.
Option C is therefore the correct debugging approach: inspect your Jinja and look for incorrectly nested curly brackets. Options A and D are generic and don't address the root cause, while B talks about quoting in SQL, which is not the problem-the error arises before SQL compilation, at Jinja parse time.

NEW QUESTION # 167
Your team uses a custom macro to generate model descriptions dynamically. However, these descriptions don't appear in the DAG visualization within the documentation. Which of the following might cause this issue?

A. The macro might not be returning the description in the standard format that dbt documentation expects.
B. Dynamically generated descriptions are incompatible with DAG visualizations in dbt
C. Custom macros always require explicit configuration in your dbt_project.yml file to be rendered in the DAG-
D. The generated documentation site might be cached, preventing the updated descriptions from appearing.

Answer: A,C,D

Explanation:
A Some macros might require additional settings to be integrated correctly into the documentation. B- dbt expects descriptions in specific formats for DAG rendering. Your macro output needs to adhere to these. C: Caching can sometimes mask the latest changes in the documentation.

NEW QUESTION # 168
......

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