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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
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. Role of dbt in analytics engineering 2. Warehouse-centric transformation workflows
Testing and Data Quality	- Data validation strategies 1. Schema testing practices 2. CI-based validation workflows - Built-in and custom tests 1. Custom SQL tests 2. Generic tests (unique, not null, relationships)
Documentation and Lineage	- dbt documentation system 1. Auto-generated docs site 2. Model descriptions and metadata - Data lineage understanding 1. Dependency tracking with ref() 2. Directed acyclic graph (DAG)
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
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 (Q131-Q136):

NEW QUESTION # 131
(Multiple Select)

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

Answer: A,B,C

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 # 132
You write a macro to dynamically create a filter string and apply it within a model as follows:SQL

A. You cannot dynamically apply filters using Jinja; they must be hardcoded in your model's SQL.
B. The database doesn't support comparison of the data type of the region column with a string value.
C. The macro templating incorrectly nests single and double quotes.
D. The generate_filter macro should be placed within a separate macros directory, not inline in the model.

Answer: C

Explanation:
Nesting quotes within Jinja can be tricky. Review the macro's output for unintended formatting. The others are unlikely to cause this specific error.

NEW QUESTION # 133
Performance Bottleneck

A. Run EXPLAIN or equivalent on the compiled query to pinpoint the most expensive operations.
B. Implement database-level indexes on columns involved in the joins, if appropriate.
C. Introduce CTEs to pre-calculate aggregated data or common subexpressions, especially if reused repeatedly.
D. Add -full-refresh to dbt run on the model to ensure downstream models don't cause errors.

Answer: A,C

Explanation:
EXPLAIN diagnoses the query plan, while CTEs can restructure for optimization. Indexes are database-level changes. A full refresh is unnecessary and disregards incremental logic.

NEW QUESTION # 134
(Multiple Select)

A. Add -full-refresh flag to dbt run to ensure all models are rebuilt from scratch.
B. Write custom macros to log intermediate data for each model.
C. IJse the -select flag with dbt run to execute models incrementally, starting with the most upstream dependency.
D. Run dbt docs generate and dbt docs serve to inspect the lineage graph.

Answer: C,D

Explanation:
Visualization and incremental execution aid debugging. Custom logging might be necessary but is more time-consuming to implement. A full refresh isn't targeted for debugging.

NEW QUESTION # 135
Consider this DAG for a dbt project. You have configured your environment to use one thread.
When running dbt run, you determine that model_d fails to materialize.

How will changing the command from dbt run to dbt run --fail-fast impact the execution of dbt run when model_d fails to materialize? Choose 1 option.
Options:

A. dbt will stop building any additional nodes in the DAG.
B. dbt will attempt to materialize everything else except for model_f.
C. dbt will drop the existing version of model_d in the data platform.
D. dbt will attempt to materialize the rest of the models.

Answer: A

Explanation:
The --fail-fast flag changes dbt's execution behavior by immediately stopping the scheduling of any further models as soon as the first failure occurs. This is true regardless of the number of threads; in this case, the environment uses one thread, meaning models run strictly in sequence based on dependency order.
In the DAG provided, model_d is downstream of model_b, and model_f depends on model_d. The execution sequence when using a single thread would be something like:
* model_a runs
* model_c runs
* model_e runs
* model_b runs
* model_d runs # fails
Once model_d fails:
* With normal dbt run, dbt would attempt to continue running any unrelated models (though in this DAG, there are no more independent nodes left anyway).
* With --fail-fast, dbt immediately stops scheduling any additional work, halting the run.
Therefore, no further models-including model_f or any other-will run. dbt does not drop models, retry them, or attempt related branches. It simply stops.

NEW QUESTION # 136
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