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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: 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
Topic 2: dbt Core Concepts	- Models and materializations 1. Ref and source functions 2. Views, tables, incremental models - Project structure and configuration 1. Packages and dependencies 2. dbt_project.yml configuration
Topic 3: Testing and Data Quality	- Built-in and custom tests 1. Custom SQL tests 2. Generic tests (unique, not null, relationships) - Data validation strategies 1. CI-based validation workflows 2. Schema testing practices
Topic 4: 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
Topic 5: Documentation and Lineage	- Data lineage understanding 1. Dependency tracking with ref() 2. Directed acyclic graph (DAG) - dbt documentation system 1. Model descriptions and metadata 2. Auto-generated docs site

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q223-Q228):

NEW QUESTION # 223
You discover that a heavily used model is taking significantly longer to materialize. After investigation, you determine that the complexity of the model's logic is a contributing factor. Which of the following actions is LEAST likely to help improve performance?

A. Change the materialization from a table to a view.
B. Consider applying an incremental materialization strategy if suitable.
C. Break down the model into smaller, intermediate models using CTEs.
D. Investigate potential optimizations in the underlying SQL code.

Answer: A

Explanation:
Switching from a table to a view alone doesn't address the core performance issue of complex logic. Other options prioritize breaking down the transformations or directly optimizing the SQL itself.

NEW QUESTION # 224
You want to write a custom test to check for potential anomalies in a time-series dataset. Which of the following would be the MOST important considerations when designing this test?

A. Ensuring the test integrates seamlessly with dbt's docs feature.
B. Defining thresholds for what constitutes an anomaly in your business context.
C. Understanding the expected seasonality or patterns in the data.
D. Identifying the metrics (e.g., average, standard deviation) relevant to your data.

Answer: B,C,D

Explanation:
The test logic itself depends on a deep understanding of your data and what 'unusual' behavior looks like. Integration with documentation (D) is secondary.

NEW QUESTION # 225
Data Quality Issue

A. Inspect any filters or transformations within the model's SQL that might be unintentionally excluding data.
B. Run dbt test on the model and see if existing tests fail, indicating a possible regression.
C. Add specific schema tests to the model, asserting that the column in question should not contain nulls.
D. Run dbt compile followed by dbt run -select model_name to isolate the model and observe its output closely.

Answer: A,B,C,D

Explanation:
This requires a comprehensive approach covering logic errors, regression tests, proactive quality checks, and isolated execution.

NEW QUESTION # 226
You define a new generic test on model customers in a YAML file:
version: 2
models:
- name: customers
columns:
- name: customer_id
tests:
- unique
- not_null
The next time your project compiles you get this error:
Raw Error:
mapping values are not allowed in this context
in "<unicode string>", line 7, column 21
What is the cause of this error?

A. unique and not_null should not be elements in a list
B. unique and not_null should be indented at the same level as tests
C. tests should be a dictionary key, not a list
D. tests should be wrapped in double quotes (")

Answer: B

Explanation:
This error occurs because the YAML structure is incorrectly indented, causing dbt's parser (and YAML itself) to misinterpret the test definitions. In dbt, generic tests must be declared as a list under the tests: key, but YAML is extremely sensitive to indentation levels. In the faulty YAML, unique and not_null are indented incorrectly relative to the tests: key, which produces the error: "mapping values are not allowed in this context." According to the dbt Testing documentation, valid generic test syntax follows this exact pattern:
columns:
- name: id
tests:
- unique
- not_null
The indentation under tests: must be consistent and aligned so that YAML interprets the items as list elements, not as malformed mappings. When indentation is wrong, YAML attempts to parse list entries as key-value mappings, which leads to the error seen during compilation.
dbt does not require generic test names to be quoted, nor does it expect tests to be a dictionary. The test list format is correct-only the indentation is wrong. Therefore, the root cause is incorrect YAML indentation, making Option D correct.

NEW QUESTION # 227
A project stakeholder requests access to an intermediate stage of a dbt transformation pipeline to validate specific calculations.
You need to weigh this request carefully because:

A. Exposing intermediate results can lead to premature analysis based on incomplete data.
B. Raw data, development data, and production data often have different levels of data quality, completeness, and timeliness.
C. All of the above-
D. Granting access to intermediate data may confuse stakeholders who are not familiar with the full transformation process-

Answer: A

Explanation:
A: Intermediate data might be unrefined and not suitable for decision-making. B: Stakeholders might misinterpret the context of intermediary data. C: It's vital to guide stakeholders to final data outputs designed for analysis.

NEW QUESTION # 228
......

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