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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q13-Q18):

NEW QUESTION # 13
While onboarding new data sources to your dbt project, you discover some have unexpectedly high null rates in key columns. How would you integrate tests for this into your dbt workflow?

A. Designate some nulls as acceptable by extending the not_null test with the OR condition.
B. Include a data quality monitoring step in your upstream ETL process before data reaches dbt
C. Create not_null tests on the raw source tables in a separate dbt project dedicated to source validation.
D. Write tests within the initial staging models that calculate and check the null percentage.

Answer: B,D

Explanation:
B ensures data entering dbt is checked at the first opportunity. C pushes quality checks as far upstream as possible- A is valid, but less integrated if sources are managed outside dbt's purview D masks the problem rather than addresses it.

NEW QUESTION # 14
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. Data warehouses might not be optimized for period-over-period comparisons.
B. ref() functions can hinder performance when working with historical data.
C. Incremental materializations aren't suitable for trend analysis.
D. Views cannot perform complex date manipulation required for YoY calculations.

Answer: A

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 # 15
(Multiple Select)

A. Write a custom Python script to parse dbt models.
B. Jinja templating
C. Snapshots
D. Manually examine the compiled SQL files.
E. ref() and source() functions
F. Inspect the manifest.json file.
G. dbt test
H. Use the dbt debug command.
I. dbt docs

Answer: B,E,I

Explanation:
The manifest.json file, generated by dbt compile, contains comprehensive metadata about your project, including a detailed breakdown of model dependencies.

NEW QUESTION # 16
You discover an intermittent issue where your models sometimes produce incorrect results. After investigation, the problem seems to occur when the source data contains a particular combination of unexpected values. How would you best address this?

A. Write a custom dbt test to explicitly identify the bad data scenario.
B. Modify the model to ignore the rows causing the issue.
C. Raise an issue with the team responsible for the upstream data source to fix the error at its origin.
D. Add pre-processing logic within the model to clean or filter out the problematic data.

Answer: A,C

Explanation:
B helps reliably prevent bad data from affecting output. C seeks a long-term solution by fixing the root cause. A might be a temporary workaround, but not ideal. D is risky and hides the issue.

NEW QUESTION # 17
(Multiple Select)

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

Answer: B,C,D

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

NEW QUESTION # 18
......

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