dbt-Analytics-Engineering勉強資料 & dbt-Analytics-Engineering日本語版テキスト内容

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Leveraging dbt State	5%	- State management - State-aware operations
Managing Data Pipelines	15%	- Pipeline orchestration - CI/CD integration - Deployment strategies
External Dependencies	10%	- Managing snapshots - External sources integration - Using packages
Debugging and Error Resolution	15%	- Identifying modeling errors - Debugging techniques - Resolving data quality issues
Creating and Maintaining Documentation	10%	- Descriptions and metadata - Generating documentation - Documentation standards
Developing dbt Models	20%	- Model design and structure 1. Ref and source functions 2. Incremental models 3. Materialization types - Model optimization 1. Performance tuning 2. Model contracts
dbt Models Governance	15%	- Version control integration - Naming conventions and standards - Project organization and structure
Implementing dbt Tests	10%	- Custom tests - Built-in tests - Test configuration and execution

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dbt Labs dbt-Analytics-Engineering日本語版テキスト内容、dbt-Analytics-Engineering試験参考書

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dbt Labs dbt Analytics Engineering Certification Exam 認定 dbt-Analytics-Engineering 試験問題 (Q188-Q193):

質問 # 188
You accidentally pushed a large file containing sensitive data to a public remote repository. What immediate actions should you take?

A. Permanently remove the sensitive file from Git history using tools like git filter-branch.
B. Contact the repository hosting service for assistance.
C. Both B and C.
D. Use git reset -hard to revert the commit and remove the file locally.

正解：A、B

解説：
B: You must ensure the sensitive data is completely removed from the history of the repository. C: The hosting service might have additional security measures or be able to assist in preventing the sensitive data from being accessed

質問 # 189
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. Incremental materializations aren't suitable for trend analysis.
C. Views cannot perform complex date manipulation required for YoY calculations.
D. ref() functions can hinder performance when working with historical data.

正解：A

解説：
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.

質問 # 190
A model depends heavily on geospatial calculations, which are computationally expensive. What optimization strategy might be worth exploring?

A. Ensure the underlying data adheres to DRY principles.
B. Pre-calculate common geospatial results as an incremental model.
C. Convert the model to a snapshot to leverage historical data more effectively.
D. Investigate whether the database offers specialized geospatial indexes or functions.

正解：D

解説：
Optimized database functionality is crucial for complex calculations. While option A could be beneficial, it's important to first utilize database-level optimizations.

質問 # 191
(Multiple Select)

A. Preferring correlated subqueries over joins when relationships are one-to-one.
B. Leveraging appropriate indexes on columns used in joins and WHERE clauses.
C. Using SELECT * whenever possible to avoid explicitly specifying needed columns.
D. Avoiding excessive use of UNION operations, especially within loops.

正解：B、D

解説：
UNION operations can be expensive. Indexing is essential for query optimization. SELECT * and correlated subqueries are generally less performant.

質問 # 192
You're setting up CI/CD for your dbt project to automate deployments. Which aspects of your testing strategy might need adaptation for a production-focused CI/CD workflow?

A. Including smoke tests that run in production to verify core functionality after a deployment.
B. Prioritizing tests with strict thresholds to avoid false positives from minor data variations in production.
C. Emphasizing tests with longer run times for thorough checks before deployment.
D. Disabling all tests that rely on creating new tables, to avoid impacting the production database.

正解：A、B

解説：
B ensures the deployed changes actually work in the real environment. C helps maintain CI/CD flow without unnecessary disruptions. A is counterproductive in a time-sensitive CI/CD context. D would overly limit your testing coverage.

質問 # 193
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dbt-Analytics-Engineering日本語版テキスト内容: https://www.topexam.jp/dbt-Analytics-Engineering_shiken.html

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