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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q59-Q64):

NEW QUESTION # 59
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. All of the above-
B. To implement stricter access controls limiting which users or roles can interact with sensitive data-
C. To optimize query performance, as certain models might benefit from specific data warehouse configurations based on their workload.
D. To comply with data governance regulations that mandate separation of different data types.

Answer: A

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 # 60
You write a macro to dynamically create a filter string and apply it within a model as follows:SQL

A. The generate_filter macro should be placed within a separate macros directory, not inline in the model.
B. The macro templating incorrectly nests single and double quotes.
C. You cannot dynamically apply filters using Jinja; they must be hardcoded in your model's SQL.
D. The database doesn't support comparison of the data type of the region column with a string value.

Answer: B

Explanation:
Nesting quotes within Jinja can be tricky. Review the macro's output for unintended formatting. The others are unlikely to cause this specific error.

NEW QUESTION # 61
You frequently need to spin up short-lived development environments on demand for testing and experimentation. Which dbt feature would streamline this process, especially when it involves custom configuration?

A. Using dbt tags to selectively execute parts of your project.
B. Creating multiple deployment environments in dbt Cloud.
C. dbt's -vars flag to override variable values.
D. dbt profiles defined in your dbt_project.yml file.

Answer: C,D

Explanation:
A encapsulates environment settings in reusable blocks- B allows for ad-hoc adjustments when running dbt- C is relevant for cloud-based setups, but less flexible for on-the-fly changes- D relates to model selection, not environment setup.

NEW QUESTION # 62
Which two statements about Exposures are true?
Choose 2 options.

A. You can run, test, and list resources that feed into your Exposure.
B. Exposures are defined in .sql files.
C. Models, sources, and metrics are downstream from Exposures.
D. Exposures describe a downstream use of your dbt project.
E. Exposures are materialized in the database.

Answer: A,D

Explanation:
The correct answers are C: Exposures describe a downstream use of your dbt project and E: You can run, test, and list resources that feed into your Exposure.
Exposures in dbt are documentation constructs that describe how the outputs of your dbt project are used downstream-such as dashboards, machine learning models, applications, reporting layers, or external tools.
They exist to provide visibility into the final layer of the analytics workflow, making it easier to track lineage from raw data # models # downstream consumers.
Option C is correct because the official dbt documentation explicitly states that exposures define the downstream use cases of dbt models. They create transparency about dependencies outside the dbt DAG itself.
Option E is also correct. Using commands like dbt ls --select +exposure:<name> or dbt run --select +exposure:
<name>, dbt enables users to run, test, and inspect the upstream resources feeding into the exposure. This is extremely valuable for impact analysis and CI workflows.
Option A is incorrect because exposures are downstream, not upstream-meaning models feed into exposures, not the other way around.
Option B is incorrect because exposures are not materialized in the warehouse; they are metadata stored in YAML.
Option D is incorrect because exposures are defined in YAML files, not SQL files.
Thus, C and E are the correct statements.

NEW QUESTION # 63

Answer:

Explanation:

Explanation:
Drops and recreates a definition of a query which is executed at the time it's referenced
# view
Inserts or updates records of an existing object in the warehouse using a delta
# incremental
Code belonging to the model is inserted into the places the model is referenced
# ephemeral
Drops and recreates an object in the warehouse as stored results when using dbt run
# table

NEW QUESTION # 64
......

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