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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Deployment and Orchestration	15%	- Jobs and scheduling in dbt Cloud - Environments (dev, staging, prod) - CI/CD with dbt Cloud - Git version control integration
Topic 2: dbt Fundamentals	15%	- dbt project structure - dbt Core vs dbt Cloud - dbt workflow and best practices
Topic 3: Models	25%	- Materializations (table, view, ephemeral, incremental) - Writing and managing SQL models - Seeds - Sources and references - Snapshots
Topic 4: Data Transformation Techniques	25%	- Macros and packages - Common table expressions and subqueries - Refactoring and incremental models - Jinja templating
Topic 5: Testing and Documentation	20%	- Custom data tests - Schema tests (unique, not_null, accepted_values, relationships) - Documentation generation - dbt docs and DAG visualization

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q119-Q124):

NEW QUESTION # 119
Your dbt project contains models that process data with varying levels of sensitivity. Which of the following might be reasons to utilize different schemas or even different targets within your data warehouse for these data sensitivity levels?

A. To optimize query performance, as certain models might benefit from specific data warehouse configurations based on their workload.
B. All of the above-
C. To comply with data governance regulations that mandate separation of different data types.
D. To implement stricter access controls limiting which users or roles can interact with sensitive data-

Answer: B

Explanation:
A: Separate schemas or targets enhance access control granularity. B: Some data workloads might have different performance requirements- C: Compliance often necessitates structured data organization-

NEW QUESTION # 120
You're working with a development data warehouse meant to closely mirror production. To make code more portable between environments, you opt to use environment variables for connection details. What's the best way to manage these?

A. Define environment variables as part of your CI/CD tool's job configuration.
B. Store sensitive credentials in plain text in the dbt_project.yml file, relying on file system permissions.
C. Use a dedicated secrets manager integrated with your dbt project.
D. Set environment variables manually in your shell session prior to running dbt commands.

Answer: A,C

Explanation:
B provides the most secure and easily auditable solution. D is essential for automating deployments. A is insecure practice. C is cumbersome and error-prone.

NEW QUESTION # 121
You're debugging a runtime error. Analyzing the compiled SQL alone doesn't provide enough clues to find the root cause. What's your next debugging step?

A. Modify the compiled SQL directly and re-run the query against your warehouse.
B. Disable custom macros to see if one of them is interfering-
C. Compare compiled SQL from a previous successful run to the current failing version-
D. Examine the dbt logs for more context about the model's execution and dependencies.

Answer: D

Explanation:
Logs often provide additional error messages and context Comparing SQL versions can be helpful but might not illuminate the root cause. Never modify compiled SQL directly

NEW QUESTION # 122
You've created a new model ref_country_codes. It references a raw table in your data warehouse raw_web_events. To ensure this dependency is valid and up-to-date, you should include which element in your model definition?

A. A custom macro to validate data in raw web events
B. A schema test on ref_country_codes
C. A source freshness test on raw web events
D. A dbt seed file populating ref_country_codes

Answer: C

Explanation:
Verifying raw object dependencies is about data freshness. A source freshness test on the upstream source raw_web_events will alert you if that table becomes stale, causing potential issues in your ref_country_codes model.

NEW QUESTION # 123
Select the dbt materialization that typically DOES NOT persist its results to disk:

A. Table
B. Incremental
C. Snapshot
D. Ephemeral

Answer: D

Explanation:
Ephemeral materializations are computed within CTEs (Common Table Expressions). Their results exist only during the query's execution and are useful for intermediate steps within complex transformations.

NEW QUESTION # 124
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