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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt-Analytics-Engineering Key Concepts, dbt-Analytics-Engineering Exam Simulations

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q226-Q231):

NEW QUESTION # 226
You discover that an essential dbt macro behaves differently between your development environment and production due to a subtle database version discrepancy. Which debugging approach would be most efficient?

A. Add verbose logging within the macro to track execution differences step by step.
B. Use database-specific functions to query the version information directly from within dbt models.
C. Temporarily introduce a conditional branch into the macro code that is environment-aware.
D. Set up a CIICD test that runs the macro in isolation using both database versions.

Answer: A,B,D

Explanation:
Each of these helps identify the root cause:A provides runtime insight into the issue. C lets you surface the relevant version for comparison. D allows for reproducible testing.

NEW QUESTION # 227
You're considering using dbt's --store-failures flag when running tests. Which of the following are potential implications of using this flag?

A. The overall dbt test command may run faster-
B. This flag alters how dbt calculates test results
C. Successful models will not be rebuilt if downstream dbt tests are marked as failures.
D. Troublesl-ming failing tests will become easier, as data related to the failure is persisted.

Answer: D

Explanation:
A is the primary purpose of -store-failures. B is not how dependencies work in dbt. C is unlikely as additional data storage is involved. D is not the case; it influences preservation of data associated with the test, not its logic.

NEW QUESTION # 228
Which two are true for a dbt retry command?
Choose 2 options.

A. It reuses selectors from the previous command.
B. It retries the previous command if it is not a syntax error in a model.
C. It reads a manifest.json file to identify the models and tests that failed in the last run.
D. It reruns all nodes in your previous invocation statement.
E. It picks up from the error without running all of the upstream dependencies.

Answer: B,C

Explanation:
The correct answers are B and E.
The dbt retry command was introduced to help developers quickly re-run only what failed in the prior dbt invocation. According to dbt documentation, dbt retry examines metadata from the previous execution, including the run results and the manifest.json, to determine which nodes failed. This aligns with Option E, as dbt uses the manifest to identify the models, tests, and other nodes that errored or were not executed successfully.
Option B is also correct because dbt retry works only when the previous run failed due to execution errors (e.
g., a warehouse failure or a logic error in SQL). If the failure was due to a syntax error that prevented dbt from compiling, there will be no valid manifest produced, so retry cannot execute. Thus, dbt retry can only re- run models when the previous failure occurred after successful compilation.
Options A and C are incorrect because retry does not run all nodes nor does it automatically pick up exactly where an execution stopped - instead, it runs failed and downstream unbuilt nodes as determined by the manifest. Option D is also incorrect; retry uses the execution metadata, not selectors, to determine what should run.

NEW QUESTION # 229
You suspect discrepancies in a critical model. To troubleshoot, you need to understand the order of model execution, but the DAG is complex. What would be the BEST strategy to get this information?

A. Manually trace the dependencies in your model files.
B. Run dbt docs generate and inspect the generated documentation.
C. Use dbt Is -select
D. Visually inspect the manifest.json file.

Answer: B

Explanation:
While other options provide some information, dbt docs generate creates comprehensive documentation including a visual DAG to show execution flow clearly.

NEW QUESTION # 230
You inherit a project with a monolithic "super-model" performing many transformations. Why might refactoring this into smaller, interconnected models be beneficial?

A. Reduces the risk of a single point of failure impacting the entire data flow.
B. Enables faster debugging and troubleshooting by isolating issues.
C. Increases potential for parallel execution within your data warehouse.
D. All of the above.

Answer: D

Explanation:
All of these are valid reasons for refactoring overly complex models in a dbt project!

NEW QUESTION # 231
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