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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: 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
Topic 2: 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)
Topic 3: 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 4: Analytics Engineering Foundations	- Modern data stack concepts (ELT vs ETL) 1. Warehouse-centric transformation workflows 2. Role of dbt in analytics engineering - SQL proficiency for analytics 1. Data modeling in SQL 2. Joins, aggregations, and window functions
Topic 5: Testing and Data Quality	- Built-in and custom tests 1. Custom SQL tests 2. Generic tests (unique, not null, relationships) - Data validation strategies 1. Schema testing practices 2. CI-based validation workflows

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q215-Q220):

NEW QUESTION # 215
You're building tests for a dbt model that performs a join across multiple large datasets. You notice performance issues - tests are taking a very long time to run. What might be optimization strategies?

A. Refactor model SQL to use incremental logic to avoid full table scans.
B. Accept that tests on complex models will always be slow.
C. Create snapshots of the source tables and test on those snapshots instead.
D. Focus on tests that can validate a subset of the data rather than the entire dataset.

Answer: A,D

Explanation:
B and C target reducing the test's workload with sampling or optimizing the model itself. A might help under specific circumstances, but not always. D is a defeatist approach!

NEW QUESTION # 216
Select the dbt materialization that typically DOES NOT persist its results to disk:

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

Answer: B

Explanation:
Ephemeral materializations are computed within CTEs (Common Table Expressions). Their results exist only during the query's execution and are useful for intermediate steps within complex transformations.

NEW QUESTION # 217
You create tests to validate counts and timestamps against a baseline dataset. The tests fail unexpectedly. What might be possible root causes?

A. There are differences in data types or time zone handling between the model and baseline.
B. Your upstream ETL process has a logic error, leading to incorrect data.
C. The baseline dataset itself has become corrupted
D. Your tests are too strict and need more generous tolerances.

Answer: A,B,C,D

Explanation:
Each scenario could cause a baseline comparison test to fail, covering issues in the data pipeline, the baseline, and the test logic itself.

NEW QUESTION # 218
You've just added some schema tests to a critical model. Which command should you execute to ensure these new tests are run?

A. dbt seed
B. dbt test
C. dbt run
D. dbt compile

Answer: B

Explanation:
The dbt test command is specifically designed to execute the tests you've defined within your models.

NEW QUESTION # 219
You accidentally pushed a large file containing sensitive data to a public remote repository. What immediate actions should you take?

A. Use git reset -hard to revert the commit and remove the file locally.
B. Both B and C.
C. Permanently remove the sensitive file from Git history using tools like git filter-branch.
D. Contact the repository hosting service for assistance.

Answer: C,D

Explanation:
B: You must ensure the sensitive data is completely removed from the history of the repository. C: The hosting service might have additional security measures or be able to assist in preventing the sensitive data from being accessed

NEW QUESTION # 220
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