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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q139-Q144):

NEW QUESTION # 139
You need to run a dbt job that performs a long-running data cleansing operation. The job occasionally times out. Which of the following are potential solutions?

A. Schedule the job to run concurrently with other dbt models to maximize resource usage-
B. Break the cleansing logic into multiple models, chaining them with dependencies-
C. Increase the timeout setting in your CI/CD tool or dbt job configuration.
D. Use a dbt macro to retry the cleansing operation with a backoff strategy within the model itself-

Answer: B,C,D

Explanation:
Each tackles the timeout issue differently:A reduces the work done in a single step. B provides more time directly . C adds retry resilience within the logic. D would likely worsen performance due to resource contention.

NEW QUESTION # 140
A CTE within a dbt model is incorrectly casting a string column to a numeric type.

A. A source table has inconsistent data, mixing true numeric values with strings.
B. A Jinja macro meant to filter out non-numeric strings has a logic flaw.
C. A snapshot is incorrectly merging new data with incompatible types into an existing table.

Answer: A,B

Explanation:
This error usually originates from mismatched data types at the database level. CTE logic or source data quality are frequent culprits.

NEW QUESTION # 141
You want to experiment with a new column in an existing model without risking production changes. Which configuration strategy in your dbt_project.yml would support this safely?

A. Modify the model SQL using the config.get() macro to dynamically control the column's behavior
B. Add a -select flag when using dbt run to execute only the modified model.
C. Utilize git branches and have different dbt_project.yml files for your production and experimental branches.
D. Define separate configurations for the column under the model and use the enabled property.

Answer: D

Explanation:
Model configurations with the enabled property offer a structured way to experiment within the project file.

NEW QUESTION # 142
You're collaborating with others on a dbt project. A new feature branch introduces unexpected errors. Which command combination helps analyze potential issues before merging into the main branch?

A. dbt seed to populate any new source tables.
B. dbt deps followed by dbt compile
C. dbt run followed by dbt test on both the feature branch and the main branch.
D. dbt docs generate on the new feature branch.

Answer: C

Explanation:
Running models and tests on both branches aids comparison and debugging. While 'docs generate' is helpful, it isn't sufficient for pinpointing runtime errors.

NEW QUESTION # 143
You discover a downstream model, dim products, references an outdated raw dataset, raw_product_archive, intended for historical analysis and not active products. What's the most appropriate corrective action?

A. Add a schema test to dim_products to catch potential errors.
B. Update the dim_products model to reference the active product dataset.
C. Delete the raw_product_archive source, so it can't be used accidentally.
D. Leave it as-is since it's not actively causing any errors currently.

Answer: B

Explanation:
Directly addressing the root cause ensures your model uses the correct source. Schema tests are safeguards but don't resolve the underlying dependency issue. Be cautious about deleting sources unless there's a very strong reason.

NEW QUESTION # 144
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