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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q335-Q340):

NEW QUESTION # 335
You run the command:
dbt test --select 'test_type:singular'
What will the command run?
Options shown:

A. furniture_customers_test
macro_stg_tpch_orders_assert_pos_price
macro_stg_tpch_suppliers_assert_pos_acct_bal
stg_tpch_orders_assert_positive_price
B. furniture_customers_test
stg_tpch_orders_assert_positive_price
Choose 1 option.
C. furniture_customers_test
D. macro_stg_tpch_orders_assert_pos_price
macro_stg_tpch_suppliers_assert_pos_acct_bal
stg_tpch_orders_assert_positive_price

Answer: D

Explanation:
In dbt, singular tests are custom SQL tests that live as standalone .sql files inside the root /tests directory, not inside /tests/generic. A singular test returns rows that indicate failure, and dbt runs the SQL directly as written. Generic tests, on the other hand, live inside the /tests/generic folder and are YAML-based macro- driven tests.
From your test folder structure (shown in the original screenshot), the following SQL files exist:
Inside /tests/generic/
* furniture_customers_test.sqlThis file is a generic test, not singular.
Inside the root /tests directory:
* macro_stg_tpch_orders_assert_pos_price.sql
* macro_stg_tpch_suppliers_assert_pos_acct_bal.sql
* stg_tpch_orders_assert_positive_price.sql
These are singular tests, because they are standalone SQL files in the tests root folder.
When you run:
dbt test --select 'test_type:singular'
dbt filters for only tests classified as singular. It ignores generic tests entirely.
Therefore, only the following 3 tests will run:
* macro_stg_tpch_orders_assert_pos_price
* macro_stg_tpch_suppliers_assert_pos_acct_bal
* stg_tpch_orders_assert_positive_price
This matches Option C, making it the correct answer.

NEW QUESTION # 336
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. Incorporate a log function into your macro.
B. Check your SQL to see if you quoted something incorrectly.
C. Take a look at the compiled code.
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 # 337
"Isn't debugging harder with many smaller models in a DAG?" How would you address this concern expressed by a colleague?

A. "We'll rely extensively on snapshots to trace errors back to their origin."
B. "Our DAG will be simple, so debugging model dependencies won't be a concern."
C. "Smaller models are easier to test individually, leading to faster issue identification."
D. "Debugging is slightly harder, but the benefits outweigh the added effort."

Answer: C

Explanation:
Emphasize how modularity isolates problems and enables targeted testing. While the other options have some truth, option B presents the most convincing argument.

NEW QUESTION # 338
You've defined development, staging, and production targets in your dbt project. You want to prevent accidental execution of destructive model materialization strategies (like drop or truncate) on the production target. How could you achieve this?

A. Implement a CI/CD process that includes checks to prevent specific dbt commands from running in the production environment
B. Write a custom macro that only executes such operations on non-production targets.
C. Rely purely on thorough code reviews to prevent execution of these commands in production.
D. Create a separate profiles.yml file for production that disables dangerous materialization strategies.

Answer: A,B

Explanation:
A Macros can provide conditional logic for model execution based on environments- C: Automated checks in CI/CD pipelines add a layer of protection. D: Relying solely on code reviews is prone to human error.

NEW QUESTION # 339
You're collaborating with others on a dbt project. A new feature branch introduces unexpected errors. Which command combination helps analyze potential issues before merging into the main branch?

A. dbt seed to populate any new source tables.
B. dbt deps followed by dbt compile
C. dbt docs generate on the new feature branch.
D. dbt run followed by dbt test on both the feature branch and the main branch.

Answer: D

Explanation:
Running models and tests on both branches aids comparison and debugging. While 'docs generate' is helpful, it isn't sufficient for pinpointing runtime errors.

NEW QUESTION # 340
......

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