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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q298-Q303):

NEW QUESTION # 298
(Multiple Select)

A. Utilize a YAML validator tool independently of dbt to check for general formatting errors.
B. Refer to the dbt documentation for a comprehensive list of valid properties within schema files.
C. Introduce a global persist_docs setting within your dbt_project.yml file to resolve schema inconsistencies.
D. If available, compare your schema.yml to a previous working version to spot problematic changes.

Answer: A,B

Explanation:
Understanding valid schema properties and general YAML linting are key. Version comparison can be useful, while persist_docs is less relevant to initial compilation errors.

NEW QUESTION # 299
Given this dbt_project.yml:
name: "jaffle_shop"
version: "1.0.0"
config-version: 2
profile: "snowflake"
model-paths: ["models"]
macro-paths: ["macros"]
snapshot-paths: ["snapshots"]
target-path: "target"
clean-targets:
- "logs"
- "target"
- "dbt_modules"
- "dbt_packages"
models:
jaffle_shop:
orders:
materialized: table
When executing a dbt run your models build as views instead of tables:
19:36:14 Found 1 model, 0 tests, 0 snapshots, 0 analyses, 179 macros, 0 operations, 0 seed files, 0 sources, 0 exposures, 0 metrics
19:36:16 Concurrency: 1 threads (target='default')
19:36:17 Finished running 1 view model in 3.35s.
19:36:17 Completed successfully
19:36:17 Done. PASS=1 WARN=0 ERROR=0 SKIP=0 TOTAL=1
Which could be a root cause of why the model was not materialized as a table?
The target-path is incorrectly configured.

A. No
B. Yes

Answer: B

Explanation:
The behavior described-dbt running the orders model as a view despite being explicitly configured as a table
-indicates that dbt is not correctly detecting or applying the model-level configuration during compilation.
dbt relies heavily on the target-path directory to write compiled SQL, manifest files, and run artifacts. If the target-path is misconfigured, pointing to a location that dbt does not handle correctly or that overlaps with another folder used internally, dbt may fail to load the correct configuration from the merged project settings.
When dbt cannot locate the compiled configuration for a model, it defaults to its standard materialization type, which is view. This explains why the logs show:
"Finished running 1 view model"
even though the dbt_project.yml clearly declares:
materialized: table.
Additionally, the logs indicate no warnings or parsing errors, meaning dbt ran successfully but with incorrect settings-another indicator of configuration metadata being overridden or misplaced due to an incorrect target- path.
By resolving the target-path issue, dbt will successfully load the model configuration and materialize the orders model as a table as intended.

NEW QUESTION # 300
Which of the following is NOT a direct benefit of thorough source, table, and column descriptions in dbt?

A. Faster dbt model compilation and execution times.
B. Better data quality due to the automated enforcement of column descriptions
C. Improved lineage and data traceability within the documentation.
D. Increased discoverability for other team members working on the project

Answer: A

Explanation:
Explanation: While good descriptions have numerous benefits, they don't inherently speed up model compilation and execution within dbt-

NEW QUESTION # 301
You've created a macro to display upstream dependencies differently from downstream dependencies in the DAG. However, after re-generating the documentation, the visualization doesn't seem to have changed. What should you investigate?

A. The built-in DAG functionality might have limitations preventing this type of customization.
B. Verify that your web browser supports the CSS and JavaScript used for custom DAG styling.
C. Ensure the macro is named correctly and located in the appropriate directory (macros folder) of your dbt project.
D. Update the cache expiration settings of your web browser.

Answer: A,B,C

Explanation:
B: dbt needs to be able to find your macro for it to be used. C: Browser compatibility can sometimes impact the rendering of custom visualizations. D: It's possible there are constraints on how far you can modify the built-in DAG.

NEW QUESTION # 302
You encounter a runtime error: "Column 'order_date' not found in the target relation". You examine your model and see:SQL

A. Review the fct_orders model's SQL logic to see if it unintentionally removes or renames the 'order_date' column.
B. Verify that your Jinja templating in other models isn't interfering with column names.
C. Ensure there are no downstream models that could be altering the 'fct_orders' table.
D. Check if the fct_orders model or source actually includes an 'order_date' column.

Answer: D

Explanation:
Start with the basics! This error typically indicates a schema mismatch between your assumption and the actual data structure.

NEW QUESTION # 303
......

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