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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Leveraging dbt State	5%	- State-aware operations - State management
Topic 2: Developing dbt Models	20%	- Model design and structure 1. Materialization types 2. Incremental models 3. Ref and source functions - Model optimization 1. Performance tuning 2. Model contracts
Topic 3: External Dependencies	10%	- Using packages - External sources integration - Managing snapshots
Topic 4: dbt Models Governance	15%	- Version control integration - Project organization and structure - Naming conventions and standards
Topic 5: Creating and Maintaining Documentation	10%	- Descriptions and metadata - Generating documentation - Documentation standards
Topic 6: Debugging and Error Resolution	15%	- Debugging techniques - Resolving data quality issues - Identifying modeling errors
Topic 7: Managing Data Pipelines	15%	- CI/CD integration - Pipeline orchestration - Deployment strategies
Topic 8: Implementing dbt Tests	10%	- Custom tests - Test configuration and execution - Built-in tests

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q175-Q180):

NEW QUESTION # 175
You typically use dbt run to execute your project. However, you want to trigger a refresh of only models that depend on newly updated source dat a. How could you achieve this?

A. Add dbt run to the post-hook of your upstream ETL process, so it triggers upon data arrival.
B. Manually determine dependent models, then use dbt run -m
C. Leverage dbt snapshots to identify changed sources and derive the dependent model list.
D. Configure your sources with the freshness property and run dbt source freshness.

Answer: C,D

Explanation:
B automates discovery of what's stale. D uses snapshots for the same purpose. A is tedious, C runs all models, not selectively

NEW QUESTION # 176
You discover a vulnerability in a third-party lineage-enhancing dbt package you use. Where might you first look for information and potential fixes?

A. Only the authors of the dbt package would be able to address the vulnerability.
B. Centralized dbt documentation or a discussion forum.
C. Directly in the manifest.json or packages.yml files within your project
D. The GitHub repository or documentation of the dbt package.

Answer: D

Explanation:
The package maintainer is the primary source for updates and vulnerability fixes. However, other resources like A & C might provide additional information or community solutions.

NEW QUESTION # 177
Which types of transformations are often best suited for raw data as an initial step in your dbt project?

A. Both A and C-
B. Renaming columns and performing basic data cleaning
C. Casting and typecasting of columns to ensure consistent data types-
D. Applying business logic and complex aggregations for reporting purposes.

Answer: A

Explanation:
A: Consistent data types across sources are essential for further modeling. C Initial cleaning and renaming improve the usability of raw data.

NEW QUESTION # 178
Your development environment uses a smaller, sampled dataset compared to production for efficiency. When a new performance-sensitive model is deployed, users report significant slowdowns. What might be some of the potential causes?

A. All of the above-
B. The smaller dataset in development didn't reveal performance issues that become apparent at the scale of production data
C. Your data warehouse might have different resource configurations or indexing in the production environment.
D. Performance optimizations made in development might not translate directly to the larger production data volumes.

Answer: A

Explanation:
A: Sampled data obscures performance problems related to data volume. B: Optimizations designed for small data might not be effective at scale. C: Differences between environments can drastically affect performance.

NEW QUESTION # 179
Which two mechanisms allow dbt to write DRY code by reusing logic, preventing writing the same code multiple times?
Choose 2 options.

A. Using dbt packages
B. Creating singular tests
C. Changing a model materialization from view to ephemeral
D. Copy/pasting folders containing multiple models
E. Writing and using dbt macros

Answer: A,E

Explanation:
The correct answers are B: writing and using dbt macros and D: using dbt packages.
dbt strongly encourages DRY (Don't Repeat Yourself) principles, and two of the core mechanisms that support reusable logic are macros and packages. Macros allow you to write Jinja-powered reusable functions that can generate SQL statements dynamically, reducing duplication across models, tests, and project logic.
Macros can encapsulate filters, joins, auditing logic, timestamps, and more-allowing developers to centralize logic in one place while referencing it across many models.
Packages extend this concept even further by allowing entire sets of macros, models, tests, and utilities to be imported into a project. Packages like dbt-utils contain widely used generic macros that help standardize transformations and testing. Using packages ensures consistent logic across teams and eliminates the need to rewrite common transformations.
Option A contradicts DRY principles because copy/pasting increases maintenance burden. Option C is not a mechanism for reusing logic; singular tests validate logic but do not reduce duplication. Option E simply changes a model's materialization and does not support code reuse.
Thus, macros and packages are the only correct dbt mechanisms that provide reusable, modular, DRY logic.

NEW QUESTION # 180
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