Exam dbt-Analytics-Engineering Syllabus, New dbt-Analytics-Engineering Exam Name

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

>> Exam dbt-Analytics-Engineering Syllabus <<

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q165-Q170):

NEW QUESTION # 165
You want to display the runtime metrics (e.g., execution time) of each model directly on the DAG node. Which challenges might you face when implementing this?

A. dbt documentation is primarily static; it might not be able to render dynamic metrics that change with each execution.
B. All of the above
C. Adding too much detailed information directly to the DAG could lead to visual clutter and make it overwhelming.
D. Retrieving runtime metrics for each historical run might not be natively supported by dbt

Answer: B

Explanation:
A: You might need to generate static summaries or find workarounds to display dynamic metrics. B. Collecting runtime metrics across all historical runs might require additional logging or integration with dbt artifacts (manifestjson, run_resultsjson)- C: The DAG visualization needs a balance between information and readability.

NEW QUESTION # 166
You want to include a detailed lineage diagram in your project documentation. Which of these approaches would you likely consider?

A. Configure a third-party dbt package designed for enhanced lineage and visualization.
B. Manually create a diagram using a separate tool and add an image reference in a model's description.
C. Use the built-in lineage functionality within dbt's standard documentation generation.
D. Write a custom macro that generates a lineage diagram and include it in relevant model descriptions.

Answer: A,B,D

Explanation:
A: For basic needs, a manual approach might be sufficient C: Custom macros provide flexibility in how the lineage is presented. D: Specialized packages offer advanced lineage visualization capabilities-

NEW QUESTION # 167
You've defined a model as follows:SQL

A. An incorrect materialization strategy causing the wrong data to be populated in a downstream source.
B. A Jinja templating error: Misusing the source() function within the count() function.
C. A pure SQL issue: The count() function isn't supported for the data type of a column in the 'cust_orders' source.
D. A data type mismatch between a column in 'stg_customers' and how the query is using it for the join.

Answer: C

Explanation:
While the others might cause problems, this error message strongly indicates a database- level type incompatibility, not a dbt-specific issue.

NEW QUESTION # 168
In development, you want to avoid having to re-run all upstream models when refactoring part of your project.
What could you do to save time rebuilding models without spending warehouse credits in your next command?

A. Replace your {{ ref() }} functions with hard-coded references.
B. Refer to a manifest and utilize the --defer and --state flags.
C. Clone your upstream models from the production schema to the development schema.
D. Leverage artifacts from a prior invocation by passing only the --state flag.

Answer: B

Explanation:
The correct answer is B: Refer to a manifest and utilize the --defer and --state flags.
According to dbt's official documentation, the --defer flag enables developers to defer the resolution of model references (ref(), source()) to an existing manifest-commonly the one generated from a production run.
When this flag is used along with --state, dbt reads the prior project state and uses the already-built production models instead of rebuilding upstream dependencies. This is essential because production models are typically large and compute-intensive. By deferring to production artifacts, developers can test only the models they have modified, dramatically reducing warehouse costs and speeding up development cycles.
This approach is foundational to dbt's recommended development workflow, especially when working with CI
/CD or large DAGs. It preserves the integrity of the dependency graph while avoiding unnecessary recomputation.
Option A violates dbt best practices by removing dependency awareness. Option C is unnecessary operational overhead and does not integrate with dbt's state management. Option D is incomplete because --state alone does not prevent upstream model execution---defer is required to substitute those models with existing artifacts.

NEW QUESTION # 169
(Multiple Select)

A. Implementing a robust suite of schema tests to validate object references-
B. Setting up a linter for your SQL files, either as part of your IDE or code editor
C. Using the 'defer' option to partially run a DAG and expose errors early
D. Defining source freshness thresholds to catch issues with missing tables or views-

Answer: A,B

Explanation:
Linters improve code hygiene, and schema tests proactively validate assumptions about model dependencies. 'Defer' helps with logic errors, and freshness checks are for source health.

NEW QUESTION # 170
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