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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q140-Q145):

NEW QUESTION # 140
Regular test runs reveal inconsistent and intermittent test failures, despite no obvious changes to models or source dat a. What debugging strategies might you use?

  • A. Temporarily switch to a more consistent testing environment.
  • B. Consider if external dependencies used within dbt tests are themselves unstable.
  • C. Examine data warehouse performance metrics for resource constraints during test runs.
  • D. Use dbfs logging feature to add detailed output to the test execution.

Answer: B,C,D

Explanation:
Each helps track down potential causes of non-deterministic test behavior. D might be too drastic early on.


NEW QUESTION # 141
(Multiple Select)

  • A. Scheduling less critical tests to run during off-peak hours.
  • B. Running dbt tests in parallel with dbt model builds whenever possible-
  • C. Designing tests, where feasible, to examine subsets of data rather than full tables.
  • D. Configuring tests to run only on models that have changed or have upstream changes.

Answer: A,C,D

Explanation:
A targets tests to the most relevant areas, reducing workload. C leverages less busy periods to avoid resource contention. D reduces individual test runtimes through sampling. B is generally inadvisable, as testing and model building might compete for resources.


NEW QUESTION # 142
You're working with a model that aggregates data across many countries. Which assumption about the data might be especially important to test for and handle in your model logic?

  • A. Time zones and daylight savings rules for timestamps could differ between data sources.
  • B. Encoding formats (UTF-8, etc.) may be inconsistent across source systems.
  • C. The presence of unexpected HTML tags within textual data fields.
  • D. Different regions may use varying currency exchange rates.

Answer: A,B,D

Explanation:
Each of these introduces unique challenges for aggregating data globally. While D can be an issue, it's less central to this specific scenario.


NEW QUESTION # 143
Which two code snippets result in a lineage line being shown in the DAG? Choose 2 options.

  • A. dbt_jsmith.stg_jaffle_shop__customers
  • B. {{ ref('stg_jaffle_shop__customers') }}
  • C. {{ target.schema }}
  • D. {{ source('jaffle_shop.customers') }}
  • E. {{ source('source', 'table') }}

Answer: B,E

Explanation:
In dbt, the Directed Acyclic Graph (DAG) is created from model dependencies, which dbt identifies only through two mechanisms: the ref() function for referencing dbt models and the source() function for referencing declared sources. These functions are explicitly designed to tell dbt how models relate to each other so that dbt can track lineage, enforce build order, and generate documentation.
Option A, {{ source('source', 'table') }}, correctly references a declared source. Any model using source() establishes an upstream dependency on that source table. dbt will therefore draw a lineage line in the DAG from the source to the model.
Option B, {{ ref('stg_jaffle_shop__customers') }}, is the canonical way to reference another dbt model. ref() ensures dbt determines dependencies at compile time, and thus creates a DAG edge between this model and its parent.
The remaining options do not create lineage:
* C directly references a database object, which dbt cannot interpret as a dependency.
* D uses a Jinja variable unrelated to lineage.
* E is invalid, because source() requires two arguments: a source name and table name, not a dotted string.
Therefore, A and B are the only valid answers.


NEW QUESTION # 144
(Multiple Select)

  • A. Using separate dbt profiles for production and development environments
  • B. Scheduling less frequent jobs using dbt Cloud's built-in job scheduler
  • C. Implementing pre-hooks and post-hooks to perform tasks before and after dbt model execution.
  • D. Integrating dbt with your CI/CD tool and using job-specific triggers or filters.

Answer: A,B,D

Explanation:
A enables environment-specific commands. B provides a way to launch different jobs. C handles scheduled tasks within dbt Cloud.


NEW QUESTION # 145
......

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