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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: 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. Joins, aggregations, and window functions 2. Data modeling in SQL
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: 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)
Topic 4: Deployment and Orchestration	- Running dbt in production 1. CI/CD integration patterns 2. dbt Cloud and job scheduling - Environments and workflows 1. Version control with Git 2. Development vs production environments
Topic 5: dbt Core Concepts	- Project structure and configuration 1. Packages and dependencies 2. dbt_project.yml configuration - 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 (Q51-Q56):

NEW QUESTION # 51
You need to build a model to identify customers who haven't placed orders in the past 90 days. Performance with this model is critical, as it will trigger a re-engagement campaign. Which strategy is likely to yield the best results?

A. Filter the orders table for dates within the past 90 days, then find customers without corresponding order IDs.
B. Join the customers and orders table, group by customer ID and use a HAVING clause to filter inactive customers.
C. Create a materialized view that contains only active customers and join against the orders table.
D. IJse a window function to rank order dates for each customer and filter those where the most recent order is older than 90 days.

Answer: B

Explanation:
While other options might work, joining and using a HAVING clause tends to be a performant pattern for this use case in most databases. Pre-filtering customers in a materialized view could be useful but depends on how frequently that data changes.

NEW QUESTION # 52
You've implemented the built-in dbt.get_column_values macro to display sample data values from a source column directly on the DAG. However, you notice performance issues when loading the docs. Which optimization strategy could you consider?

A. Utilize a dbt snapshot to materialize the macro results instead of computing them on the fly.
B. Increase the compute resources allocated to your dbt documentation generation process.
C. Replace dbt_get_column_values with a custom macro that only fetches the first few sample values.
D. Modify the macro to only retrieve sample values during development and not for production documentation.

Answer: A,C,D

Explanation:
B: Disabling expensive computations conditionally can boost performance for production environments. C: Snapshots pre-compute values, potentially improving DAG loading times. D: Reducing the amount of data retrieved by the macro could help with performance.

NEW QUESTION # 53
A colleague asks, "Why do we need dbt docs when we already have good database documentation?" Provide a compelling response.

A. dbt docs automatically tracks model testing results.
B. dbt docs is mandatory for version control with dbt projects.
C. dbt docs generates dynamic documentation that directly reflects your project's dependencies and current state.
D. dbt docs primarily serves as a replacement for dbt seed.

Answer: C

Explanation:
Highlight how dbt docs offers up-to-date, project-specific documentation, while traditional database documentation can easily become outdated.

NEW QUESTION # 54
After implementing thorough dbt tests, you confidently deploy changes. Yet, downstream Bl dashboards break, and stakeholders complain about inaccurate dat a. What could have gone wrong?

A. The tests you wrote were too strict, causing false positives.
B. Critical assumptions were made about the data in the Bl layer, independent of dbt models.
C. Your tests were correct, but a bug in dbt prevented them from running successfully.
D. There are logic errors in your dbt models that the tests didn't catch.

Answer: B,D

Explanation:
This emphasizes that models can be internally correct yet still produce unusable output if the source data changes. Tests only cover what they're designed for; the Bl layer may have its own data quality requirements.

NEW QUESTION # 55
You've defined a complex custom singular test. While testing it initially on a development dataset, it times out. Which optimization techniques might be worth trying?

A. Rewrite the test using a dbt macro for better maintainability.
B. Ensure the test SQL uses appropriate indexes in the underlying database.
C. Remove any redundant joins or subqueries from the test logic.
D. Disable the test in production environments and run it only in development.

Answer: B,C

Explanation:
A and C target improving query performance. B deals with code organization, not speed. D sacrifices data quality for speed

NEW QUESTION # 56
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