dbt-Analytics-Engineering Free Sample Questions | dbt-Analytics-Engineering Valid Test Syllabus

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Leveraging dbt State	5%	- State management - State-aware operations
Debugging and Error Resolution	15%	- Resolving data quality issues - Debugging techniques - Identifying modeling errors
Implementing dbt Tests	10%	- Built-in tests - Custom tests - Test configuration and execution
Managing Data Pipelines	15%	- Deployment strategies - Pipeline orchestration - CI/CD integration
dbt Models Governance	15%	- Naming conventions and standards - Project organization and structure - Version control integration
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
External Dependencies	10%	- External sources integration - Managing snapshots - Using packages
Creating and Maintaining Documentation	10%	- Descriptions and metadata - Generating documentation - Documentation standards

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q190-Q195):

NEW QUESTION # 190
During development, you frequently need to refresh and transform large datasets, leading to long dbt run times. Which techniques could you explore to improve your development workflow?

A. Use development environment settings that incrementally build models only when code changes.
B. All of the above.
C. Temporarily decrease the sample size of data during development, while reverting to full data processing in other environments.
D. Strategically employ ephemeral models in development to avoid materializing intermediate data repeatedly.

Answer: B

Explanation:
A: Incremental models optimize execution when changes are isolated. B: Ephemeral models avoid unnecessary persistence for temporary steps. C: Sampling decreases data volumes, speeding up development runs.

NEW QUESTION # 191
(Multiple Select)

A. Scheduling less critical tests to run during off-peak hours.
B. Running dbt tests in parallel with dbt model builds whenever possible-
C. Designing tests, where feasible, to examine subsets of data rather than full tables.
D. Configuring tests to run only on models that have changed or have upstream changes.

Answer: A,C,D

Explanation:
A targets tests to the most relevant areas, reducing workload. C leverages less busy periods to avoid resource contention. D reduces individual test runtimes through sampling. B is generally inadvisable, as testing and model building might compete for resources.

NEW QUESTION # 192
(Multiple Select)

A. Configuring separate dbt projects for each subdirectory and running them independently.
B. Using dbt run -m to select models using a path pattern.
C. Setting up tags strategically and using the -s flag when executing dbt commands.
D. Defining a custom source in sources.yml that selects tables from the desired subdirectory.

Answer: B,C

Explanation:
A leverages path-based selection. B works if your organization is tag-based. C is unrelated, sources are about data, not model location. D is overly complex for this task.

NEW QUESTION # 193
Your team decides to use separate schemas for development and production within the same data warehouse. Which of the following dbt features are essential for managing this workflow effectively?

A. dbt snapshots to compare data state between environments-
B. dbt seeds to populate test data in your development schema.
C. Macros and Jinja templating to dynamically switch target schemas.
D. Environment variables to store environment-specific configurations.

Answer: C,D

Explanation:
A makes code portable across environments. D allows for environment-specific settings. B is useful, but not specific to multi-schema setups C is important for testing, but not the core deployment environment distinction.

NEW QUESTION # 194
Which two are true for a dbt retry command?
Choose 2 options.

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

Answer: B,D

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 # 195
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