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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q83-Q88):

NEW QUESTION # 83
You have a dbt project with models defined as views. After deploying to a production database, the view objects are present, but no data is displayed when querying them. What might be the root cause?

A. Your dbt job didn't run successfully, so the views were created empty.
B. There's a bug in dbt's SQL generation that specifically affects views in certain environments.
C. Permissions granted in production don't allow reading data from the underlying tables.
D. Dbt view materializations behave differently in production.

Answer: C

Explanation:
B is a common security-related cause of 'missing' data. The others are less likely; views, logs, and dbt core logic tend to be consistent.

NEW QUESTION # 84
28. Consider this DAG:
* model_a # model_c # model_e
* model_b # model_d # model_f
(With model_c and model_d both feeding into the final layer.)
You execute:
dbt run --fail-fast
in production with 2 threads. During the run, model_b and model_c are running in parallel when model_b returns an error.
Assume there are no other errors in the model files, and model_c was still running when model_b failed.
Which model or models will successfully build as part of this dbt run? Choose 1 option.

A. model_a
B. model_a, model_c, model_e
C. model_a, model_c, model_d, model_e, model_f
D. model_a, model_c

Answer: D

Explanation:
The --fail-fast flag tells dbt to stop scheduling any new nodes as soon as one node fails. Importantly, dbt does not kill models that are already running; in-flight nodes are allowed to finish.
Here's what happens step by step with 2 threads:
* Roots model_a and model_b start first.
* model_a finishes successfully. That makes model_c eligible to run.
* dbt now runs model_b and model_c in parallel.
* While they are running, model_b fails.
* Because --fail-fast is set, dbt immediately stops scheduling any additional models (like model_d, model_e, or model_f).
* model_c was already running when model_b failed, so it is allowed to complete successfully.
Downstream models of either branch (model_d, model_e, and model_f) never start, because fail-fast prevents any further nodes from being queued after the first failure.
So, the only models that successfully build during this run are:
* model_a (completed before model_b failed)
* model_c (already running at the time of failure and allowed to finish) Hence the correct choice is B: model_a, model_c.

NEW QUESTION # 85
You need to gather detailed metrics about how your dbt models are used (who runs them, frequency, common failure points, etc.). What kind of tooling would help with this?

A. dbt Cloud's built-in job monitoring features, if applicable
B. Specialized data observability platforms
C. Custom logging built into your models
D. Version control system metadata (e.g., Git history)

Answer: A,B

Explanation:
B is relevant if you use dbt Cloud. C is suitable, as many observability platforms have features for tracking operational data, including dbt job metrics. A and D, while containing information, are likely too granular or require a lot of manual work.

NEW QUESTION # 86
You try updating a column description using its corresponding YAML file. After regenerating the docs, the update doesn't appear. Which of the following might be the cause?

A. Your browser is aggressively caching the documentation site.
B. There could be a syntax error within the updated description that dbt is silently ignoring.
C. Changes in column descriptions only update the database schema, not the project documentation.
D. The column description might need additional configuration with a test for it to render in the docs.

Answer: A,B

Explanation:
A: Aggressive browser caching can sometimes prevent updates from being reflected. C: Even small syntax errors in YAML can lead to dbt ignoring certain elements during the documentation generation process.

NEW QUESTION # 87
(Multiple Select)

A. Your development profile in dbt_projectyml has incorrect connection details.
B. Environment variables used for sensitive credentials are not set correctly in production.
C. Your production database has stricter data type constraints than development.
D. You have accidentally granted write permissions to the production data source during testing.

Answer: B,C

Explanation:
Explanation: A could reveal issues only present when interacting with the real production data. C is a common source of environment-specific errors. B would likely cause errors everywhere. D is a serious issue, but wouldn't cause a regular dbt run to fail differently.

NEW QUESTION # 88
......

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