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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
dbt Core Concepts	- Models and materializations 1. Views, tables, incremental models 2. Ref and source functions - Project structure and configuration 1. Packages and dependencies 2. dbt_project.yml configuration
Documentation and Lineage	- Data lineage understanding 1. Dependency tracking with ref() 2. Directed acyclic graph (DAG) - dbt documentation system 1. Auto-generated docs site 2. Model descriptions and metadata
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
Deployment and Orchestration	- Environments and workflows 1. Version control with Git 2. Development vs production environments - Running dbt in production 1. dbt Cloud and job scheduling 2. CI/CD integration patterns
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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q125-Q130):

NEW QUESTION # 125
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. Create a separate profiles.yml file for production that disables dangerous materialization strategies.
C. Write a custom macro that only executes such operations on non-production targets.
D. Rely purely on thorough code reviews to prevent execution of these commands in production.

Answer: A,C

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 # 126
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. Yes
B. No

Answer: A

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 # 127
(Multiple Select)

A. not_null and unique tests on primary key columns.
B. Tests comparing the latest data against historical baselines.
C. Schema tests to confirm the existence of tables and columns.
D. Tests checking for row count discrepancies between source systems and target tables.

Answer: A,C,D

Explanation:
A, B, and C focus on verifying basic data transfer and structural integrity. D is important, but comes after making sure the data is there at all_

NEW QUESTION # 128
You've defined a unique test on a key column within a model. When running dbt test, you encounter a surprising failure. Which of the following might be the cause?

A. The unique test is too strict and doesn't align with the business definition of uniqueness.
B. Upstream data was updated unexpectedly, leading to previously non-existent duplicates.
C. There are truly duplicate values in the column.
D. An incorrect join in your model is inadvertently creating duplicate rows.

Answer: A,B,C,D

Explanation:
All of these could cause a previously passing test to fail, highlighting the interplay between data, model logic, and test definitions.

NEW QUESTION # 129
You've made several commits to your feature branch. Before merging into the main branch, you want to consolidate the changes into a single, clear commit. What Git technique would help achieve this?

A. Use git rebase -i (interactive rebase) to squash commits together.
B. Manually copy your files into a new branch, commit, and issue a pull request.
C. git cherry-pick specific commits from your branch and reapply them.
D. Run git rebase main followed by git commit -amend.

Answer: A

Explanation:
Interactive rebasing (git rebase -i) provides fine-grained control to squash, edit, or rearrange commits, creating a cleaner commit history

NEW QUESTION # 130
......

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