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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q220-Q225):

NEW QUESTION # 220
You want to display the runtime metrics (e.g., execution time) of each model directly on the DAG node. Which challenges might you face when implementing this?

A. dbt documentation is primarily static; it might not be able to render dynamic metrics that change with each execution.
B. Adding too much detailed information directly to the DAG could lead to visual clutter and make it overwhelming.
C. All of the above
D. Retrieving runtime metrics for each historical run might not be natively supported by dbt

Answer: C

Explanation:
A: You might need to generate static summaries or find workarounds to display dynamic metrics. B. Collecting runtime metrics across all historical runs might require additional logging or integration with dbt artifacts (manifestjson, run_resultsjson)- C: The DAG visualization needs a balance between information and readability.

NEW QUESTION # 221
You define a new generic test on model customers in a YAML file:
version: 2
models:
- name: customers
columns:
- name: customer_id
tests:
- unique
- not_null
The next time your project compiles you get this error:
Raw Error:
mapping values are not allowed in this context
in "<unicode string>", line 7, column 21
What is the cause of this error?

A. unique and not_null should be indented at the same level as tests
B. unique and not_null should not be elements in a list
C. tests should be a dictionary key, not a list
D. tests should be wrapped in double quotes (")

Answer: A

Explanation:
This error occurs because the YAML structure is incorrectly indented, causing dbt's parser (and YAML itself) to misinterpret the test definitions. In dbt, generic tests must be declared as a list under the tests: key, but YAML is extremely sensitive to indentation levels. In the faulty YAML, unique and not_null are indented incorrectly relative to the tests: key, which produces the error: "mapping values are not allowed in this context." According to the dbt Testing documentation, valid generic test syntax follows this exact pattern:
columns:
- name: id
tests:
- unique
- not_null
The indentation under tests: must be consistent and aligned so that YAML interprets the items as list elements, not as malformed mappings. When indentation is wrong, YAML attempts to parse list entries as key-value mappings, which leads to the error seen during compilation.
dbt does not require generic test names to be quoted, nor does it expect tests to be a dictionary. The test list format is correct-only the indentation is wrong. Therefore, the root cause is incorrect YAML indentation, making Option D correct.

NEW QUESTION # 222
Collaborative Fix

A. All of the above.
B. Add clear commit messages that concisely summarize the purpose of the fix.
C. Open a pull request with detailed comments explaining the rationale for the changes and any potential side-effects.
D. Include updated model documentation (dbt docs) outlining the logic and reasons for adjustment.

Answer: A

Explanation:
Changes need context! I-Jse comments, updated docs, and informative commit messages.

NEW QUESTION # 223
Business requirements call for a report ranking products based on year-over-year (YoY) revenue growth. Historical data must be preserved. What's a potential issue in implementing this directly in a dbt model?

A. Incremental materializations aren't suitable for trend analysis.
B. ref() functions can hinder performance when working with historical data.
C. Data warehouses might not be optimized for period-over-period comparisons.
D. Views cannot perform complex date manipulation required for YoY calculations.

Answer: C

Explanation:
YoY calculations can strain database performance. Consider strategies like pre-aggregating data at the month level. Others issues are less likely to be a bottleneck.

NEW QUESTION # 224
You're setting up baseline comparisons with historical dat
a. Which factors are crucial to consider when selecting or generating the baseline dataset?

A. The baseline needs to align in structure (schemas) with the dbt models being tested.
B. The baseline should cover a representative time range relevant to your analysis.
C. The data volume in the baseline should be large enough to identify statistical patterns.
D. If using data, the baseline might need sanitization to remove PII-

Answer: A,B,D

Explanation:
Each of these directly impacts the validity and usefulness of the baseline for comparison. B, while good, is less universally critical.

NEW QUESTION # 225
......

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