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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q222-Q227):

NEW QUESTION # 222
(Multiple Select)

A. database
B. test-paths
C. model-paths
D. materialized: view

Answer: B,C

Explanation:
model-paths and test-paths specify project directory structures- Options C would be within a model configuration, and D could be in a profile.

NEW QUESTION # 223
(Multiple Select)

A. dbt will issue a warning but continue processing.
B. Downstream models relying on the staging model will continue to function.
C. The staging model run will fail.
D. The dbt run will succeed, but the staging model will have null values due to the missing data.

Answer: C,D

Explanation:
A and C are correct. A missing dependency will cause SQL errors, and nulls would likely occur where the dropped table's data was used. B is incorrect. Downstream models will often fail if an upstream model fails. D is incorrect. dbt's strict dependency handling is likely to stop the process and throw errors.

NEW QUESTION # 224
You need a model to track weekly cohort retention, with the ability to slice data across multiple dimensions. The underlying data is voluminous. Which approach lays a good foundation for scalability?

A. Pre-calculate all possible cohort and dimension combinations as an incremental model.
B. Introduce a JSON column to store dimensions dynamically, reducing the number of columns.
C. A wide table with columns for each potential filtering combination (country, product category, acquisition channel, etc.).
D. Normalize the cohort data and dimensions, using joins for analysis.

Answer: D

Explanation:
Normalization aids scalability. Options A and C suffer from combinatorial explosion. While JSON can offer flexibility, query performance can be unpredictable.

NEW QUESTION # 225
A pull request includes changes to both dbt models and configuration files in your dbt_project.yml file. During the merge, conflicts arise in the configuration file. How would you approach resolving these conflicts?

A. Give priority to the model changes and manually edit the configuration file after.
B. Ask the author of the pull request to rebase their changes and resolve the configuration conflict in their branch.
C. Utilize Git's merge conflict markers to carefully combine changes in the configuration file.
D. Request a separate, smaller pull request dedicated to the configuration changes to isolate the conflict.

Answer: B,C,D

Explanation:
B: Git conflict markers are essential to identify and carefully resolve the conflict. C: Asking the PR author to resolve the conflict keeps changes consolidated on their branch. D: Isolating configuration changes can simplify conflict resolution, especially if they involve complex adjustments.

NEW QUESTION # 226
You introduce a set of schema tests on source tables. These tests run successfully, but upon investigation, you find subtle data quality issues are still being missed. What are potential reasons for this?

A. Schema tests shouldn't be relied on for comprehensive data quality assurance.
B. There's a bug in dbt's schema test implementation.
C. Schema tests only check basic structure and not the specific content of the data.
D. Schema tests only run once per day by default.

Answer: A,C

Explanation:
Schema tests are a good first line of defense but don't replace the need for more nuanced checks that examine the data's actual content.

NEW QUESTION # 227
......

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