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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Testing and Documentation	20%	- Documentation generation - dbt docs and DAG visualization - Schema tests (unique, not_null, accepted_values, relationships) - Custom data tests
Topic 2: Models	25%	- Writing and managing SQL models - Seeds - Materializations (table, view, ephemeral, incremental) - Sources and references - Snapshots
Topic 3: Data Transformation Techniques	25%	- Jinja templating - Common table expressions and subqueries - Macros and packages - Refactoring and incremental models
Topic 4: Deployment and Orchestration	15%	- CI/CD with dbt Cloud - Git version control integration - Environments (dev, staging, prod) - Jobs and scheduling in dbt Cloud
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 (Q177-Q182):

NEW QUESTION # 177
Type Mismatch

A. Introduce a staging model specifically to perform the type conversion, creating a clean separation.
B. Modify the source table directly in the database to adjust the data type, ensuring backward compatibility.
C. Use a CAST operation within the affected model's SQL, leaving the underlying source unchanged.
D. Handle the type conversion with a custom macro and apply it selectively within your dbt project.

Answer: A

Explanation:
A staging model offers the most control and flexibility. Direct source modification risks disrupting other systems. In-model CASTs work but clutter the core logic. Macros are powerful but add complexity.

NEW QUESTION # 178
(Multiple Select)

A. Introduce redundant columns in source tables to store pre-calculated percentage changes.
B. Pre-calculate frequently used periods (e.g., current month, previous month) and store them as intermediate CTEs.
C. Ensure relevant columns in underlying tables have appropriate indexes.
D. Utilize window functions to efficiently compute the difference without self-joins.

Answer: C,D

Explanation:
Window functions excel at period-over-period calculations. Indexes are crucial for accelerating queries. Options A and B could help but introduce potential maintenance and storage tradeoffs.

NEW QUESTION # 179
You run a new dbt job, but several models don't execute. Logs indicate: "Database Error in model. The error message seems unrelated to your recent model code changes. What's a possible non- SQL cause?

A. You've accidentally deleted a source table that these models depend on.
B. dbt's connection to the database has incorrect credentials or is encountering network issues.
C. There's a configuration error in your project file causing dbt to skip certain models.
D. A macro used in multiple models has an error that prevents the models from compiling correctly.

Answer: B

Explanation:
Sometimes, what appears as a model error has roots in database connectivity. The others could be the cause, but check your project-wide connection setup first.

NEW QUESTION # 180
You're onboarding new developers to your dbt project. To simplify their setup, you want to provide a standard profiles.yml template. What should you consider when doing this?

A. Referencing environment variables to allow for easy customization by developers.
B. Including different target configurations to illustrate switching between development and production.
C. Providing example dbt commands as comments directly within the file to guide new users.
D. Prepopulating common credentials to make the initial setup hassle-free.

Answer: A,B,C

Explanation:
B promotes flexibility and security (avoids storing sensitive data). C demonstrates good practices. D enhances usability A is a major security risk!

NEW QUESTION # 181
When integrating a new data source, you notice many existing models could benefit from it. What's the most maintainable way to incorporate the new data within a DRY approach?

A. Build a snapshot of the new data combined with relevant data from existing models.
B. Establish a naming convention for the new data elements to avoid hardcoding them within model SQL.
C. Add the new data source directly to each relevant model's SQL query.
D. Create a base model that incorporates the new data source and use the ref() function in downstream models.

Answer: D

Explanation:
A base model serves as a modular point of integration for the new data. The ref() function promotes DRY principles and eases maintenance if the new source structure changes.

NEW QUESTION # 182
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