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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Documentation and Lineage	- dbt documentation system 1. Auto-generated docs site 2. Model descriptions and metadata - Data lineage understanding 1. Directed acyclic graph (DAG) 2. Dependency tracking with ref()
Analytics Engineering Foundations	- SQL proficiency for analytics 1. Joins, aggregations, and window functions 2. Data modeling in SQL - Modern data stack concepts (ELT vs ETL) 1. Role of dbt in analytics engineering 2. Warehouse-centric transformation workflows
Deployment and Orchestration	- Environments and workflows 1. Version control with Git 2. Development vs production environments - Running dbt in production 1. CI/CD integration patterns 2. dbt Cloud and job scheduling
Testing and Data Quality	- Built-in and custom tests 1. Generic tests (unique, not null, relationships) 2. Custom SQL tests - Data validation strategies 1. Schema testing practices 2. CI-based validation workflows
dbt Core Concepts	- Models and materializations 1. Views, tables, incremental models 2. Ref and source functions - Project structure and configuration 1. dbt_project.yml configuration 2. Packages and dependencies

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q166-Q171):

NEW QUESTION # 166
A source table is updated with a full overwrite every night. Your dbt job fails due to freshness checks. What's a likely cause and solution?

A. The freshness check is misconfigured- Adjust the count and period settings.
B. The loaded_at field is absent Add the corresponding column or remove the check-
C. You need to schedule the dbt job to run after the nightly update.
D. The source uses an incremental materialization. Change it to a 'table'

Answer: A

Explanation:
Freshness checks designed for incremental updates might fail with full overwrites. Other solutions might be needed in specific scenarios but aligning the check is the first step.

NEW QUESTION # 167
Your model has a contract on it.
When renaming a field, you get this error:
This model has an enforced contract that failed.
Please ensure the name, data_type, and number of columns in your contract match the columns in your model's definition.
| column_name | definition_type | contract_type | mismatch_reason |
|-------------|------------------|----------------|-----------------------|
| ORDER_ID | TEXT | TEXT | missing in definition |
| ORDER_KEY | TEXT | | missing in contract |
Which two will fix the error? Choose 2 options.

A. Add order_key to the model SQL.
B. Remove order_key from the contract.
C. Add order_key to the contract.
D. Remove order_id from the contract.
E. Remove order_id from the model SQL.

Answer: C,D

Explanation:
dbt model contracts enforce that the column names, data types, and number of columns defined in the contract exactly match the columns produced by the compiled SQL. If any column appears in one location (the contract or the SQL) but not in the other, dbt raises an enforcement error.
In this scenario, the error message shows:
* ORDER_ID is missing in the model definition, meaning the SQL no longer contains a column named order_id, but the contract still expects it.
* ORDER_KEY is missing in the contract, meaning the model SQL now contains a new column, but this column has not been added to the contract.
To fix the mismatch, you must remove columns from the contract that no longer exist in the SQL and add to the contract any new columns that now appear in the SQL.
Therefore:
* Option A - Remove order_id from the contract - is correct because the column no longer exists in the model SQL.
* Option D - Add order_key to the contract - is correct because the SQL now produces this column.
Options B and C incorrectly alter the wrong side of the definition, and Option E would create a mismatch in the opposite direction.
Thus, the correct fixes are A and D.

NEW QUESTION # 168
dbt Model Snippet

A. The logic contained within a custom macro.
B. The ref() functions arent correctly resolving dependencies.
C. The presence of filters on the source fct_orders or dim_customers.
D. The underlying data types of o.customer_id and c.customer_id mismatch.

Answer: C

Explanation:
Filters on sources frequently cause missing rows in joins. Check your source definitions and any model- level filtering. While the others could be issues, start with the most common cause.

NEW QUESTION # 169
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 # 170
(Multiple Select)

A. Verifying that a paid_at timestamp column always occurs after a signup_at timestamp column.
B. Checking if values in a specific column fall within a valid range based on business logic.
C. Confirming that the total count of orders in a day matches a rollup value in a pre-calculated summary table.
D. Ensuring a primary key column has no null values.

Answer: A,D

Explanation:
Generic tests (not_null and unique) handle basic structural checks. Options C and D involve domain specific logic or external comparisons better suited for custom tests.

NEW QUESTION # 171
......

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