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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q156-Q161):

NEW QUESTION # 156
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. Increase the timeout setting in your CI/CD tool or dbt job configuration.
C. Break the cleansing logic into multiple models, chaining them with dependencies-
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 # 157
You've defined a unique test on a key column within a model. When running dbt test, you encounter a surprising failure. Which of the following might be the cause?

A. An incorrect join in your model is inadvertently creating duplicate rows.
B. Upstream data was updated unexpectedly, leading to previously non-existent duplicates.
C. There are truly duplicate values in the column.
D. The unique test is too strict and doesn't align with the business definition of uniqueness.

Answer: A,B,C,D

Explanation:
All of these could cause a previously passing test to fail, highlighting the interplay between data, model logic, and test definitions.

NEW QUESTION # 158
You're considering using dbt's --store-failures flag when running tests. Which of the following are potential implications of using this flag?

A. Troublesl-ming failing tests will become easier, as data related to the failure is persisted.
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. The overall dbt test command may run faster-

Answer: A

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 # 159
dbt Model Snippet:SQL

A. Validating that the column names generated by the Jinja loop would be valid SQL identifiers.
B. Checking that the date_part() function is compatible with your database-
C. Making sure the source table fct_orders contains the expected columns.
D. Ensuring the grains variable is defined in dbt_project.yml or a macro.

Answer: D

Explanation:
Compilation errors often mean undefined variables or incorrect Jinja syntax. The rest are potential issues but less likely to break at the compilation stage.

NEW QUESTION # 160
A colleague has introduced a new sensitive column into a source table. You need to update the dbt project documentation accordingly. Which of the following actions should you take?

A. Add a clear description of the sensitive column in its corresponding YAML file.
B. Consult your organization's data governance policies and consider additional measures beyond simple documentation.
C. Update the source documentation and add references to that documentation in the appropriate model's YAML file
D. Add the column description and tag it using a custom sensitive tag in the YAML file.

Answer: B,C,D

Explanation:
B). Tagging sensitive data helps categorize it, allowing for potential filtering or access control within the documentation. C: Handling sensitive data goes beyond simple documentation. Its crucial to follow your organization's data security and governance protocols. D: Referencing source documentation provides full context and traceability of the sensitive data's origin.

NEW QUESTION # 161
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