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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
dbt Models Governance	15%	- Naming conventions and standards - Project organization and structure - Version control integration
Developing dbt Models	20%	- Model optimization 1. Performance tuning 2. Model contracts - Model design and structure 1. Materialization types 2. Ref and source functions 3. Incremental models
Implementing dbt Tests	10%	- Built-in tests - Custom tests - Test configuration and execution
External Dependencies	10%	- Managing snapshots - External sources integration - Using packages
Creating and Maintaining Documentation	10%	- Descriptions and metadata - Documentation standards - Generating documentation
Debugging and Error Resolution	15%	- Resolving data quality issues - Identifying modeling errors - Debugging techniques
Managing Data Pipelines	15%	- Deployment strategies - CI/CD integration - Pipeline orchestration
Leveraging dbt State	5%	- State management - State-aware operations

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q160-Q165):

NEW QUESTION # 160
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. The generated documentation site might be cached, preventing the updated descriptions from appearing.
C. Dynamically generated descriptions are incompatible with DAG visualizations in dbt
D. Custom macros always require explicit configuration in your dbt_project.yml file to be rendered in the DAG-

Answer: A,B,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 # 161
You have written this new agg_completed_tasks dbt model:
with tasks as (
select * from {{ ref('stg_tasks') }}
)
select
user_id,
{% for task in tasks %}
sum(
case
when task_name = '{{ task }}' and state = 'completed'
then 1
else 0
end
) as {{ task }}_completed
{% endfor %}
from tasks
group by 1
The dbt model compiles to:
with tasks as (
select * from analytics.dbt_user.stg_tasks
)
select
user_id,
from tasks
group by 1
The case when statement did not populate in the compiled SQL. Why?

A. Because there is no {% set tasks %} statement in the model defining the tasks variable.
B. Because there is not a {% if not loop.last %}{% endif %} to compile a valid case when statement.
C. Because there is not a task_name column in stg_tasks.
D. Because the Jinja for-loop should be written with {{ }} instead of {% %}.

Answer: A

Explanation:
In dbt, Jinja runs at compile time and operates only on Python objects that exist in the Jinja context (variables, lists, dictionaries, etc.). The tasks inside your with clause:
with tasks as (
select * from {{ ref('stg_tasks') }}
)
defines a SQL CTE named tasks, not a Jinja variable. Jinja cannot iterate over a SQL CTE; it can only loop over a Python iterable that has been created or passed into the template. Because there is no Jinja variable named tasks defined with something like:
{% set tasks = ['task_a', 'task_b', 'task_c'] %}
the for task in tasks loop has nothing to iterate over. As a result, the entire loop body is effectively skipped during compilation, and the compiled SQL only contains:
select
user_id,
with no generated case when expressions.
Option A is incorrect because loop.last is only needed for formatting (e.g., commas), not for the loop to render. Option B is wrong because Jinja control structures correctly use {% %}, while {{ }} is for output.
Option D is irrelevant to compilation; missing columns would cause a runtime database error, not an empty compiled block.
Therefore, the problem is that no Jinja variable tasks was defined, making C the correct answer.

NEW QUESTION # 162
You're setting up baseline comparisons with historical dat
a. Which factors are crucial to consider when selecting or generating the baseline dataset?

A. If using data, the baseline might need sanitization to remove PII-
B. The data volume in the baseline should be large enough to identify statistical patterns.
C. The baseline needs to align in structure (schemas) with the dbt models being tested.
D. The baseline should cover a representative time range relevant to your analysis.

Answer: A,C,D

Explanation:
Each of these directly impacts the validity and usefulness of the baseline for comparison. B, while good, is less universally critical.

NEW QUESTION # 163
Which two dbt commands work with dbt retry?
Choose 2 options.

A. debug
B. deps
C. parse
D. snapshot
E. run-operation

Answer: D,E

Explanation:
The correct answers are A: run-operation and E: snapshot.
According to dbt's retry documentation, the dbt retry command works by examining the artifacts of a previous invocation (manifest, run results) and then re-running failed nodes for commands that generate node executions. Only commands that write run results and maintain execution state can be retried. These include dbt run, dbt test, dbt seed, dbt snapshot, and dbt run-operation when the operation executes nodes or macros that generate runtime artifacts.
Option A is correct because run-operation supports retry when the macro being run triggers execution tracked in run results. Option E is correct because snapshots execute SQL against the warehouse and record stateful results, meaning dbt can retry failed snapshots using the retry mechanism.
Options B (parse), C (debug), and D (deps) do not create runnable nodes or execution results; they simply validate or install project resources. These commands do not produce retryable artifacts, so dbt retry cannot operate on them.
Thus, the only options that work with dbt retry from the list provided are run-operation and snapshot.

NEW QUESTION # 164
You discover a flaw in a data cleaning process applied early in your raw data transformation. What implications does this have for the rest of your dbt pipeline and the distinction between production vs. development?

A. There's a risk that incorrect data exists in both development and production, leading to unreliable modeling.
B. You might need to re-run models downstream of the fix across both development and production to ensure data integrity.
C. It highlights the importance of testing and validation at each transformation stage, not just in the final reporting layer.
D. All of the above.

Answer: D

Explanation:
A: Errors in early stages propagate downstream, affecting both environments. B: Fixing the flaw requires reprocessing potentially significant amounts of data. C: Thorough testing at each step is crucial for preventing data quality Issues.

NEW QUESTION # 165
......

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