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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q166-Q171):

NEW QUESTION # 166
You're merging changes from a feature branch into your main branch. Which of the following Git commands or strategies help keep a clean and easy-to-follow project history?

A. Employing git squash merge for condensing feature branch commits.
B. Using git merge to create a merge commit.
C. All of the above can be valid strategies, each with different trade-offs.
D. Utilizing git rebase main before merging to linearize the history.

Answer: C

Explanation:
A Merge commits preserve branch structure but can make history more complex. B: Rebasing creates a linear history, making it simpler to follow C: Squash merges combine multiple commits, simplifying the history of major features. The best approach depends on your team's preferences and the feature's complexity

NEW QUESTION # 167
Several models perform similar filtering and aggregation on a frequently used source table. What's the most effective way to streamline this process and improve maintainability?

A. Replicate the filtering and aggregation logic directly within each model.
B. Create an ephemeral model to prepare the data in a common format, referenced by downstream models.
C. Create a table materialization to hold the pre-processed data.
D. Create a snapshot to capture the pre-processed state of the source table.

Answer: B

Explanation:
Ephemeral models within CTEs allow for reusable intermediate steps in data transformation and are perfect for avoiding logic duplication across multiple models.

NEW QUESTION # 168
(Multiple Select)

A. You need to grant access to a model's results to users with limited permissions.
B. The model involves complex transformations that are computationally expensive.
C. Long-term storage of the model's output isn't a primary requirement.
D. The model's data needs to be frequently joined with other datasets for analysis.

Answer: A,C

Explanation:
Views are useful for access control and when the results don't necessarily require physical storage. They're not ideal for computationally heavy models as they are re-computed every time they're queried.

NEW QUESTION # 169
You're refactoring a complex SQL query into a dbt model. You notice that the model's output has significantly fewer rows than expected. Which of the following are potential causes?

A. An incorrect materialization setting on the model.
B. Your model has accidental WHERE conditions that are too restrictive.
C. There may be a LIMIT statement leftover from development.
D. Upstream data has quality issues with null values or unexpected joins.

Answer: B,C,D

Explanation:
All except D could limit the result set more than expected. Materialization settings impact how tables are built, not the SQL logic itself.

NEW QUESTION # 170
The dataset you work with contains personally identifiable information (PII). Which types of additional tooling might be necessary for your project beyond a core dbt implementation?

A. Advanced statistical modeling tools.
B. Data masking or tokenization solutions.
C. Automated ETL pipelines
D. Enhanced data access governance and auditing tools.

Answer: B,D

Explanation:
Data masking/tokenization protects sensitive data, and access governance/auditing ensures compliance when handling PII, which are distinct challenges from the core dbt workflow.

NEW QUESTION # 171
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