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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: Testing and Data Quality	- Data validation strategies 1. CI-based validation workflows 2. Schema testing practices - Built-in and custom tests 1. Custom SQL tests 2. Generic tests (unique, not null, relationships)
Topic 2: Documentation and Lineage	- Data lineage understanding 1. Dependency tracking with ref() 2. Directed acyclic graph (DAG) - dbt documentation system 1. Model descriptions and metadata 2. Auto-generated docs site
Topic 3: Deployment and Orchestration	- Environments and workflows 1. Version control with Git 2. Development vs production environments - Running dbt in production 1. dbt Cloud and job scheduling 2. CI/CD integration patterns
Topic 4: dbt Core Concepts	- Models and materializations 1. Views, tables, incremental models 2. Ref and source functions - Project structure and configuration 1. Packages and dependencies 2. dbt_project.yml configuration
Topic 5: Analytics Engineering Foundations	- SQL proficiency for analytics 1. Data modeling in SQL 2. Joins, aggregations, and window functions - Modern data stack concepts (ELT vs ETL) 1. Role of dbt in analytics engineering 2. Warehouse-centric transformation workflows

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
You run a dbt job that heavily utilizes CT Es (Common Table Expressions). In your development environment, it works flawlessly. However, in production (a different database technology), it throws errors. Which potential causes should you investigate?

A. The production database has limitations or different syntax conventions related to CTEs.
B. Your production environment has resource constraints that impact the execution of complex queries.
C. This indicates a bug in dbt's cross-database compatibility layer.
D. CTE performance optimizations are automatically applied in development, but not in production.

Answer: A,B

Explanation:
Acan lead to different behavior under load. B different dialects have nuances about CTE support C is unlikely, D less probable than a dialect-specific issue.

NEW QUESTION # 18
You have complex dbt models involving incremental logic based on a timestamp column. To debug a production issue, you want to temporarily force a full rebuild of a specific incremental model. Which dbt command-line options could help?

A. Manually update the timestamp column in the incremental model's output table.
B. Write a macro within the model to switch behavior based on an environment variable.
C. Use dbt artifacts or the compilation context to access the current run's timestamp and override model logic.
D. Add the -full-refresh flag to the dbt run command.

Answer: C,D

Explanation:
A overrides the incremental setting project-wide. C allows the model to be 'tricked" into a full refresh. B is valid, but more work. D is risky and violates dbts workflow.

NEW QUESTION # 19
You assume two columns of type 'numeric' will always align in terms of precision and scale (number of decimal places). What's a key way to design your models and tests to be resilient even if this assumption changes?

A. Avoid relying on exact equality when comparing values from these columns.
B. Always store all numeric values as strings to prevent these issues.
C. Use explicit casting functions to enforce matching precision during calculations.
D. Write tests to compare the column metadata rather than their contents.

Answer: A,C

Explanation:
These strategies allow for flexibility with numeric comparisons, making your models less sensitive to changes in precision. C is a good idea but not a full solution. D is generally bad practice.

NEW QUESTION # 20
You define several sources. However, some are frequently updated, while others rarely change. How can you optimize your dbt run command to account for this difference?

A. Employ the defer option if downstream models permit and ensure freshness checks are adjusted.
B. Group sources based on update frequency and create separate dbt run commands for each group.
C. Set up dbt schedules within your dbt_project.yml with different intervals for each group.
D. Include the -exclude flag along with less-frequently updated source names.

Answer: B

Explanation:
Separating dbt run commands offers immediate control without using advanced features like schedules or deferring. The other options have merit in specific scenarios.

NEW QUESTION # 21
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, model_c, model_e
B. model_a, model_c
C. model_a, model_c, model_d, model_e, model_f
D. model_a

Answer: B

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 # 22
......

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