Valid dbt-Analytics-Engineering Exam Question, Customizable dbt-Analytics-Engineering Exam Mode

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

>> Valid dbt-Analytics-Engineering Exam Question <<

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q217-Q222):

NEW QUESTION # 217
You have a dbt project with models defined as views. After deploying to a production database, the view objects are present, but no data is displayed when querying them. What might be the root cause?

A. Permissions granted in production don't allow reading data from the underlying tables.
B. Dbt view materializations behave differently in production.
C. There's a bug in dbt's SQL generation that specifically affects views in certain environments.
D. Your dbt job didn't run successfully, so the views were created empty.

Answer: A

Explanation:
B is a common security-related cause of 'missing' data. The others are less likely; views, logs, and dbt core logic tend to be consistent.

NEW QUESTION # 218
A newly-hired analyst needs to update a downstream model that depends on a chain of intermediate models. To minimize the potential for disruption, how can modularity help them?

A. Snapshots ensure that historical data won't be impacted unintentionally.
B. By using macros, modifications are limited to a single location.
C. With clear dependencies, they can pinpoint which upstream models to test.
D. Incremental materializations will help verify that the changes are successful.

Answer: C

Explanation:
Modularity and well-defined dependencies make it easier to isolate the impact of a change, simplifying updates and testing.

NEW QUESTION # 219
You introduce performance optimizations to a subset of models. You need to benchmark both old and new versions in an isolated environment before deployment. How might you structure your dbt commands for this comparison?

A. Within a model, conditionally write to different schemas based on an environment variable, preserving both versions.
B. Use feature branches and switch between them with different dbt profiles for old and new code.
C. Add dbt hooks to temporarily archive the original output tables before overwriting them during benchmark runs.
D. Utilize dbt seed to populate separate datasets representing the old and new model outputs for side-by-side analysis.

Answer: A,B

Explanation:
A facilitates testing versions in a clean way. D allows comparison within the database- B is for static data, not model outputs. C is less elegant, risking accidental data loss-

NEW QUESTION # 220
A source table is updated with a full overwrite every night. Your dbt job fails due to freshness checks. What's a likely cause and solution?

A. The source uses an incremental materialization. Change it to a 'table'
B. You need to schedule the dbt job to run after the nightly update.
C. The freshness check is misconfigured- Adjust the count and period settings.
D. The loaded_at field is absent Add the corresponding column or remove the check-

Answer: C

Explanation:
Freshness checks designed for incremental updates might fail with full overwrites. Other solutions might be needed in specific scenarios but aligning the check is the first step.

NEW QUESTION # 221
Your dbt project contains sensitive dat
a. You need to load seed data (e.g., lookup tables) while ensuring it's not accidentally committed to version control. How can you best achieve this?

A. Employ a secrets management tool and integrate it with your dbt deployment.
B. Write the seed data into log files, excluding them from version control.
C. Utilize environment variables to store seed data values.
D. Create separate seed files and add them to your .gitignore or equivalent.

Answer: D

Explanation:
Seed files designed for local use should be kept out of version control. While the other options have merits, they don't directly prevent sensitive data from being committed.

NEW QUESTION # 222
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