dbt-Analytics-Engineering専門トレーリング & dbt-Analytics-Engineering資格練習

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

>> dbt-Analytics-Engineering専門トレーリング <<

dbt-Analytics-Engineering資格練習 & dbt-Analytics-Engineering日本語版問題集

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dbt Labs dbt Analytics Engineering Certification Exam 認定 dbt-Analytics-Engineering 試験問題 (Q166-Q171):

質問 # 166
You need to set up a production-like environment for load testing and performance tuning of dbt models. How might you approach the resource allocation and provisioning of this environment?

A. Partner with your data infrastructure team to get guidance based on their experience.
B. Create a CI/CD job that dynamically scales a cloned production environment for the test, then scales it back down.
C. Run performance tests directly against production during a designated off-peak period.
D. Write dbt macros that artificially increase dataset sizes in development for simulating load.

正解：A、B

質問 # 167
Your dbt models create tables, but you notice a consistent "_dbt" suffix appended to all table names. Where does this likely originate from, and how could you modify it?

A. It's a naming convention enforced by the database engine, not directly controlled by dbt
B. This behavior is configurable in your dbt_projectyml settings related to model schemas.
C. Its a default behavior of dbt to help distinguish model names from pre-existing tables.
D. There's likely a custom macro in your codebase adding the suffix

正解：B、C、D

解説：
A is how dbt works by default C could override the names, and D has the power to change the default suffix.

質問 # 168
You've added detailed descriptions to your sources.yml file. Which of the following scenarios would still necessitate adding descriptions directly within your model files?

A. If you want to provide additional context or logic specific to how a source is used within that model.
B. You always need both source and model-level descriptions for complete documentation.
C. If the source table undergoes a schema change, and you want to document these changes historically within the model.
D. If different models utilize the same source table for different purposes.

正解：A、C、D

解説：
A: Model descriptions allow for tailored explanations on how a source table is specifically used within the context of the model. B: Maintaining historical context of changes directly within the model can be valuable for traceability. C: Differing model use cases can warrant distinct descriptions for the same source table.

質問 # 169
You debug a production issue by running a model in a testing environment with additional logging output. Which dbt features or command-line additions would facilitate this?

A. Environment variables to conditionally enable verbose logging macros inside the model.
B. dbt hooks to redirect model-related logs to a centralized logging platform.
C. Custom dbt flags (using -vars) to control the logging level of the SQL execution.
D. The -warn-error flag to treat warnings as errors, making your run fail louder on potential issues.

正解：A、C、D

解説：
These provide control during execution:A changes model behavior. B makes execution more sensitive. C alters dbt's log handling.

質問 # 170
You've defined a model as follows:SQL

A. A data type mismatch between a column in 'stg_customers' and how the query is using it for the join.
B. A Jinja templating error: Misusing the source() function within the count() function.
C. A pure SQL issue: The count() function isn't supported for the data type of a column in the 'cust_orders' source.
D. An incorrect materialization strategy causing the wrong data to be populated in a downstream source.

正解：C

解説：
While the others might cause problems, this error message strongly indicates a database- level type incompatibility, not a dbt-specific issue.

質問 # 171
......

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