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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: 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
Topic 2: Deployment and Orchestration	- Running dbt in production 1. dbt Cloud and job scheduling 2. CI/CD integration patterns - Environments and workflows 1. Version control with Git 2. Development vs production environments
Topic 3: Analytics Engineering Foundations	- SQL proficiency for analytics 1. Data modeling in SQL 2. Joins, aggregations, and window functions - Modern data stack concepts (ELT vs ETL) 1. Warehouse-centric transformation workflows 2. Role of dbt in analytics engineering
Topic 4: Documentation and Lineage	- Data lineage understanding 1. Directed acyclic graph (DAG) 2. Dependency tracking with ref() - dbt documentation system 1. Auto-generated docs site 2. Model descriptions and metadata
Topic 5: dbt Core Concepts	- Project structure and configuration 1. dbt_project.yml configuration 2. Packages and dependencies - Models and materializations 1. Ref and source functions 2. Views, tables, incremental models

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q72-Q77):

NEW QUESTION # 72
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 # 73
You're refactoring a complex SQL query into a dbt model. You notice that the model's output has significantly fewer rows than expected. Which of the following are potential causes?

A. Upstream data has quality issues with null values or unexpected joins.
B. Your model has accidental WHERE conditions that are too restrictive.
C. There may be a LIMIT statement leftover from development.
D. An incorrect materialization setting on the model.

Answer: A,B,C

Explanation:
All except D could limit the result set more than expected. Materialization settings impact how tables are built, not the SQL logic itself.

NEW QUESTION # 74
You're setting up a CI/CD pipeline for dbt deployments. Which actions can help maintain data integrity and consistency between your development, staging, and production environments?

A. Implement automated tests that validate data quality and model assumptions across all environments.
B. Utilize dbt logs and artifacts to track any changes made outside the standard deployment pipeline in each environment.
C. Enforce a branching strategy that mirrors environment progression (e.g., dev branch deploys to dev, etc. )
D. All of the above.

Answer: D

Explanation:
A: Tests ensure consistent behavior and catch issues early across environments. B: Structured branching aligns code changes to the environments they affect. C: Tracking unexpected changes is vital for auditing and troubleshooting.

NEW QUESTION # 75
A colleague asks, "Why go through the trouble of using dbt? Can't we just write SQL scripts and schedule them?" Which of the following is the most compelling argument for using dbt?

A. dbt automatically generates user-friendly data lineage visualizations.
B. dbt promotes modularity, reusable code, and testing within data transformation pipelines.
C. dbt offers native support for advanced scheduling and orchestration features.
D. dbt models are typically more performant than hand-written SQL scripts.

Answer: B

Explanation:
While other options have benefits, dbt's core strength lies in enforcing structure and maintainability in data transformation processes.

NEW QUESTION # 76
You establish a process using dbt tests and snapshots to track data quality changes over time. To maximize the utility of this historical record for troubleshooting, what additional step would be beneficial?

A. Automatically purge snapshots older than a certain date to save storage resources.
B. Convert the dbt test results and snapshots into time-series tables for easier trend analysis.
C. Schedule regular alerts whenever a test result deviates significantly from previous runs.
D. Integrate metadata about dbt model versions and schema changes alongside the snapshots.

Answer: B,C,D

Explanation:
These enhance the historical context and make the data actionable for resolving issues- A might discard valuable information prematurely

NEW QUESTION # 77
......

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