Practice dbt-Analytics-Engineering Mock & dbt-Analytics-Engineering Lab Questions

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt-Analytics-Engineering Lab Questions & dbt-Analytics-Engineering Latest Dumps

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q229-Q234):

NEW QUESTION # 229
Outdated Macro

A. Create a new source table specifically for testing the macro without impacting production data.
B. Run dbt Is to identify all locations where the macro is used and ensure compatibility.
C. Write unit tests to validate the macro's output with a variety of input scenarios, including edge cases.
D. Deploy the modified macro to a staging environment and run a representative subset of dbt models.

Answer: C,D

Explanation:
Unit tests isolate the macro's logic, while a staging environment allows for real-world impact assessment. A separate source table might be overkill; dbt Is focuses on dependencies, not functionality.

NEW QUESTION # 230
Unexpected Results

A. Use diff tools or version control history to compare the refactored logic against the previous version.
B. Revert the changes in your version control system to restore the model to its previous working state.
C. Implement targeted profiling to measure the execution time of specific query sections within the model.

Answer: B

Explanation:
Start by gaining situational awareness (logs), pinpointing bottlenecks (profiling), and then analyzing the code change itself (diff). Revert if needed but only after some investigation.

NEW QUESTION # 231
(Multiple Select)

A. Tools specializing in schema change detection.
B. Data integration testing frameworks (e.g., Great Expectations)
C. Anomaly detection platforms.
D. Continuous Integration (Cl) tools.

Answer: A,B,C

Explanation:
All of these address different aspects of advanced data validation that complement the basic tests within dbt. Cl tools (D) are involved in running these checks but are not the data validation tools themselves.

NEW QUESTION # 232
A performance-critical model depends on multiple complex transformations that would benefit from being pre-computed. Direct querying of this model should be as fast as possible. Which materialization is likely the best choice?

A. Incremental
B. Ephemeral
C. Table
D. View

Answer: C

Explanation:
Explanation: Tables store materialized results on disk. When pre-calculated, they offer the fastest query response time, making them suitable for performance-sensitive models.

NEW QUESTION # 233

Answer:

Explanation:

Explanation:
Update int_order_items by replacing:
* {{ source('tpch', 'orders') }} # {{ ref('stg_tpch_orders') }}
* {{ source('tpch', 'lineitem') }} # {{ ref('stg_tpch_line_items') }}
In dbt's recommended modeling pattern, sources should be referenced only in the staging layer. Downstream models (intermediate and mart/fact models) should reference staging models via ref(), not the raw source() directly. This keeps all raw-to-clean logic centralized in the staging layer and makes refactoring and documentation easier.
In the original DAG, int_order_items depends directly on tpch.orders and tpch.lineitem via source(), and staging models also depend on those same sources. That creates parallel paths from the sources and breaks the clean layered architecture.
By updating the int_order_items SQL to use:
from {{ ref('stg_tpch_orders') }}
join {{ ref('stg_tpch_line_items') }} ...
instead of:
from {{ source('tpch', 'orders') }}
join {{ source('tpch', 'lineitem') }} ...
you ensure that int_order_items sits entirely downstream of the staging layer. The new DAG becomes linear: sources # staging # intermediate (int_order_items) # any further marts. This improves modularity, reusability of cleaned logic, and makes the DAG easier to reason about and maintain.

NEW QUESTION # 234
......

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