問題集を購入するなら最新の2026年07月24日 dbt-Analytics-Engineering試験問題と解答PDFで一年間無料更新 [Q108-Q133]

問題集を購入するなら最新の2026年07月24日 dbt-Analytics-Engineering試験問題と解答PDFで一年間無料更新

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質問 # 108
A teammate opens a pull request with numerous changes across many files. To help with the review process, you would suggest that they:

• A. Resubmit the pull request as several smaller, more focused pull requests.
• B. All of the above.
• C. Provide a comprehensive summary of the intent and scope of the changes within the pull request description.
• D. Use detailed commit messages to explain the rationale behind each set of related changes.

正解：C

解説：
A: Smaller PRs allow for more focused and effective reviews. B: Clear commit messages help understand the step-by-step changes within the PR. C: A summary provides a bird's-eye view for the reviewer.

質問 # 109
You're expanding your dbt deployment to support a new regional team based in a different geographic location from your main development team. They'll access the same production data in your centralized data warehouse. Which factors need careful consideration?

• A. Potential network latency affecting performance of dbt runs for the geographically separate team.
• B. Controlling dbt access based on team membership and geographic location to align with security policies.
• C. Data residency regulations that might restrict the flow or storage of data between geographic regions.
• D. All of the above.

正解：D

解説：
A: Data regulations can heavily influence architecture and permissions. B: Neuork performance impacts user experience and execution times_ C: Access control likely needs to consider region-specific restrictions.

質問 # 110
A colleague has made updates to some model descriptions and wants you to quickly regenerate the docs to only include those changes. Which command variation would be the most efficient without a full recompilation?

• A. dbt docs generate -select [model_name]
• B. dbt docs generate --models +[model_namel
• C. It's not possible to partially regenerate dbt docs; you need to run the full dbt docs generate command.
• D. Utilize dbt's incremental model feature along with dbt docs generate -no-compile.

正解：A

解説：
The -select flag limits the documentation generation to the specified model(s). However, for this to work smoothly, dependencies need to be considered. If the updated model depends on others, they might need to be included or recompiled for the docs to be correct.

質問 # 111

正解：

解説：

Explanation:
Information Type
Retrieved From
Singular tests
.sql files
Column data types
Data platform information schema
Generic tests
.yml configuration
SQL code
C. .sql files
Column descriptions
.yml configuration
Model dependencies
.sql files
The dbt docs command compiles metadata about your project by gathering information from three primary sources: your warehouse's information schema, your YAML configuration files, and your SQL model files. Understanding which metadata comes from which source is essential for debugging and for effective documentation practices.
Singular tests live inside .sql files within the /tests directory. Since dbt renders these tests directly from SQL files, their definitions appear in documentation sourced from that location.
Column data types come from the warehouse itself. dbt introspects the data platform information schema to retrieve actual types because dbt does not infer or define column types-only the warehouse does.
Generic tests (e.g., unique, not_null, accepted_values) are declared in .yml files. These YAML definitions contain test configurations, descriptions, and parameters, which dbt uses to document and execute these tests.
SQL code for models is naturally sourced from .sql files where the models are defined. This includes logic such as SELECT statements, CTEs, and transformations.
Column descriptions are written exclusively in .yml files. dbt never extracts descriptions from SQL comments-only from YAML.
Model dependencies come from the ref() and source() calls inside .sql model files, which dbt parses to build the DAG.

質問 # 112
32. You are creating a fct_tasks model with this CTE:
with tasks as (
select * from {{ ref('stg_tasks') }}
)
You receive this compilation error in dbt:
Compilation Error in model fct_tasks (models/marts/fct_tasks.sql)
Model 'model.dbt_project.fct_tasks' (models/marts/fct_tasks.sql) depends on a node named 'stg_tasks' which was not found Which is correct? Choose 1 option.
Options:

• A. stg_tasks is configured as ephemeral.
• B. There is no dbt model called stg_tasks.
• C. A stg_tasks has not been defined in schema.yml.
• D. There is no stg_tasks in the data warehouse.

正解：B

解説：
The dbt compilation error explicitly states that the model fct_tasks depends on a node named stg_tasks, but dbt cannot find any resource with that name in the project. dbt resolves ref('stg_tasks') at compile time by searching for a model, seed, or source named stg_tasks. When no such model exists, dbt raises the exact error shown: "depends on a node named 'stg_tasks' which was not found." This error occurs before execution and is unrelated to whether the table exists in the warehouse. For dbt, the existence of a warehouse table is irrelevant during compilation-only the presence of a declared dbt resource matters. Therefore, option C (no table in the warehouse) cannot cause this error.
Option A is incorrect because ephemeral models still count as dbt models, and dbt can resolve ref() to them without problem.
Option D is wrong because defining a model in schema.yml is optional and unrelated to dbt's ability to find the model. Tests and documentation require YAML entries, but model definitions do not.
Thus, the only correct explanation is that no model file named stg_tasks.sql exists under /models, making Option B the correct choice.

質問 # 113
(Multiple Select)

• A. Your dbt tests are invalid.
• B. A ref function referencing another model is incorrect.
• C. You made a typo when defining the column name in your model.
• D. An upstream source table schema was updated without updating the dbt model.

正解：B、C、D

解説：
All but option D could lead to this specific error. Tests wouldn't directly cause a column mismatch, but they might help you catch the error.

質問 # 114
Which two configuration items can be defined under models: in your dbt_project.yml file?
Choose 2 options.

• A. test
• B. tags
• C. schema
• D. target
• E. source

正解：B、C

解説：
The correct answers are A: schema and C: tags.
In dbt, the dbt_project.yml file is the central configuration file that defines model-level settings. Under the models: section, you can specify a wide range of model configurations such as schema, materialized, tags, alias, and custom meta fields. The schema configuration allows you to control which database schema a model should be built in, giving analytics engineers the flexibility to organize models by domain or environment. The tags configuration is also valid under models: and is widely used to group models logically for selection, documentation, or orchestration workflows.
Option B (source) is incorrect because sources are defined under YAML files in the sources: section, not under models: in dbt_project.yml. Option D (test) is incorrect because tests must be defined in model or source YAML files, not inside the project configuration. Option E (target) is not a configuration that applies to models; rather, it refers to dbt runtime environments and cannot be configured under the models: block.
dbt's project configuration system ensures that model-level behavior is managed centrally and consistently, and schema and tags are two of the officially supported configuration keys under models:.

質問 # 115
What must happen before you can build models in dbt?
Choose 1 option.

• A. Underlying data must be accessible on your data platform.
• B. Sources must have been defined in your dbt project.
• C. Raw data must be cleaned.
• D. You must have created a service account in your data platform.

正解：A

解説：
The correct answer is C: Underlying data must be accessible on your data platform.
dbt does not perform data ingestion or data loading. Instead, dbt operates after raw data is already available in your warehouse. This means that before dbt can build any models-whether staging, intermediate, or mart- layer models-the underlying source data must already exist and be accessible in the connected data platform (Snowflake, BigQuery, Redshift, Databricks, etc.). dbt uses SQL to transform existing relations; therefore, if the data platform cannot access the underlying tables or external sources, model execution will fail.
Option A is incorrect because sources do not need to be defined before building models. Models can be built without using sources at all. Source definitions are optional metadata and lineage declarations, not prerequisites.
Option B is incorrect because service accounts are not required; dbt can connect through any credential mechanism supported by the warehouse (OAuth, user accounts, tokens, etc.).
Option D is incorrect because dbt itself performs transformations on raw data-cleaning raw data beforehand is not required; in fact, that is one of dbt's main responsibilities.
Thus, the only true prerequisite is that the warehouse must contain accessible underlying data.

質問 # 116
A model depends heavily on geospatial calculations, which are computationally expensive. What optimization strategy might be worth exploring?

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

正解：A

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 # 117
Which command materializes my_model and only its first-degree parent model(s) in the data platform?
Choose 1 option.

• A. dbt run --select +my_model
• B. dbt compile --select +my_model
• C. dbt compile --select +1 my_model
• D. dbt run --select +1 my_model
• E. dbt run --select 1+my_model

正解：D

解説：
The correct answer is D: dbt run --select +1 my_model.
In dbt's selection syntax, the + operator is used to include parents (upstream) and/or children (downstream) of a given node. When used as +my_model or my_model+, it means "include all ancestors" or "all descendants" respectively, with no limit on depth.
To limit how many levels of parents or children are included, dbt allows a numeric depth between the + and the resource name. +1 my_model (or my_model+1) means:
* Select my_model
* Plus only its first-degree parents (direct dependencies), and no further ancestors.
Because you want to materialize the models, you must use dbt run, not dbt compile. dbt compile only compiles SQL and does not create or update relations in the data platform.
So:
* Option A (dbt run --select +my_model) would include all upstream ancestors, not just first-degree parents.
* Options B and E use compile, so they don't materialize.
* Option C is just syntactically wrong.
Therefore, the only command that both runs models and limits selection to my_model plus its first-degree parents is dbt run --select +1 my_model.

質問 # 118
You encounter the following error when running dbt compile:Runtime Error in model customer _ churn (models/transformations/customer_churn.sql)

• A. There's a typo in how you're referencing the fct_orders object in your SQL.
• B. The fct_orders model depends on other models that haven't compiled successfully.
• C. You need to add a schema test to validate the existence of the fct orders table.
• D. The fct_orders source is not configured correctly in your dbt_project.yml file.

正解：A

解説：
Explanation: This is a runtime error indicating incorrect usage of fct_orders. Options A and C could cause errors but of a different nature. Schema tests won't fix a referencing error.

質問 # 119
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. This behavior is configurable in your dbt_projectyml settings related to model schemas.
• B. Its a default behavior of dbt to help distinguish model names from pre-existing tables.
• C. There's likely a custom macro in your codebase adding the suffix
• D. It's a naming convention enforced by the database engine, not directly controlled by dbt

正解：A、B、C

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 # 120
Which types of transformations are often best suited for raw data as an initial step in your dbt project?

• A. Casting and typecasting of columns to ensure consistent data types-
• B. Both A and C-
• C. Applying business logic and complex aggregations for reporting purposes.
• D. Renaming columns and performing basic data cleaning

正解：B

解説：
A: Consistent data types across sources are essential for further modeling. C Initial cleaning and renaming improve the usability of raw data.

質問 # 121
You identify the need to refactor several models with similar transformations. Which of the following is the first step to increase modularity and promote the DRY philosophy?

• A. Use a text editor's "find and replace" function to update repetitive code blocks.
• B. Test individual models thoroughly to establish a baseline before making changes.
• C. Create CTEs to segment logical steps within the existing model SQL.
• D. Analyze commonalities and differences in the transformations to determine refactoring potential.

正解：D

解説：
Understanding the shared and unique aspects of the transformations is key to designing an effective modularization strategy.

質問 # 122
You want to configure dbt to prevent tests from running if one or more of their parent models is unselected.
Which test command should you execute?
Choose 1 option.

• A. dbt test --select "orders" --indirect-selection=buildable
• B. dbt test --select "orders" --indirect-selection=cautious
• C. dbt test --select "orders" --indirect-selection=empty
• D. dbt test --select "orders"

正解：C

質問 # 123
Which of the following scenarios might warrant a git rebase instead of a standard git merge when integrating your changes into the main branch?

• A. You only want to include a subset of your feature branch commits in the main branch.
• B. You need to resolve complex merge conflicts that arise during a standard merge.
• C. Your branch has diverged significantly from the main branch with unrelated changes.
• D. You want to avoid a merge commit and keep a linear project history.

正解：C、D

解説：
A Rebasing creates a more linear history, making it easier to trace changes over time. D. Rebasing allows for a cleaner integration when a feature branch contains intermingled changes that shouldn't all be merged.

質問 # 124
A non-technical user needs to access a specific table generated by your dbt project. Which of the following is the LEAST suitable option?

• A. Grant them access to the output from dbt docs generate.
• B. Create a dashboard in a Bl tool connected to your data warehouse.
• C. Share query results as a CSV file.
• D. Set up a read-only connection to the database and guide them with SQL queries.

正解：D

解説：
Providing direct database access to non-technical users carries risks. Options A, C, and D provide information without exposing the user to unnecessary complexity.

質問 # 125
you're working with a dbt project where a central model fct_orders is refactored. Several downstream models rely on this fact table. Which of the following is the MOST efficient way to determine which downstream models might be affected by this change?

• A. Use the dbt Is command to generate a lineage graph.
• B. Run dbt test to see which tests might fail.
• C. Utilize the dbt docs generate and dbt docs serve commands.
• D. Execute dbt run and observe for errors.
• E. Manually review the code of all downstream models.

正解：C

解説：
While options B, D, and E can indirectly hint at broken models, they're not the most efficient methods. The dbt docs commands allow you to directly visualize and explore dependencies within your dbt project, making it the ideal choice for this scenario.

質問 # 126
You want to include a multi-paragraph description for a source. Which of the following approaches is the most suitable?

• A. Reference an external Markdown file within the source description.
• B. Utilize YAML's block scalar syntax (using l) to maintain formatting for multi-line descriptions.
• C. dbt documentation does not support multi-paragraph descriptions for sources.
• D. Write a longer description directly within the YAML file, breaking it across multiple lines.

正解：B

解説：
The block scalar syntax in YAML allows for writing multi-line text blocks that preserve the original formatting

質問 # 127
A pull request targeting your main branch has been approved in code review but requires changes after automated tests fail. To address this, you should:

• A. Disable the automated tests temporarily, merge, and re-enable tests later.
• B. Merge the pull request despite the failing tests, resolving the issues in a subsequent fix.
• C. Ask the branch author to add new commits fixing the issues. Then merge when tests pass.
• D. Directly modify the branch by pushing commits to fix the tests.

正解：C

解説：
The branch author should update their pull request with fixes. Merging should typically only happen once tests pass, indicating code stability.

質問 # 128
Collaborative Fix

• A. Add clear commit messages that concisely summarize the purpose of the fix.
• B. All of the above.
• C. Include updated model documentation (dbt docs) outlining the logic and reasons for adjustment.
• D. Open a pull request with detailed comments explaining the rationale for the changes and any potential side-effects.

正解：B

解説：
Changes need context! I-Jse comments, updated docs, and informative commit messages.

質問 # 129
You discover that a heavily used model is taking significantly longer to materialize. After investigation, you determine that the complexity of the model's logic is a contributing factor. Which of the following actions is LEAST likely to help improve performance?

• A. Investigate potential optimizations in the underlying SQL code.
• B. Consider applying an incremental materialization strategy if suitable.
• C. Break down the model into smaller, intermediate models using CTEs.
• D. Change the materialization from a table to a view.

正解：D

解説：
Switching from a table to a view alone doesn't address the core performance issue of complex logic. Other options prioritize breaking down the transformations or directly optimizing the SQL itself.

質問 # 130
(Multiple Select)

• A. You're trying to optimize the performance of a particularly complex transformation.
• B. You suspect that filters in a source definition arent being applied correctly.
• C. You encounter an error message that references a specific line number in the compiled code.
• D. Data type conversions in your models consistently fail during execution.

正解：A、B

解説：
Compiled SQL helps verify applied logic and is useful for query optimization. Line numbers in errors often point to compiled SQL- Data type errors are typically less related to the compiled structure.

質問 # 131
You're working on a dbt project in a development environment where compute resources are limited. You want to prototype a new model quickly without incurring high materialization overhead. Which materialization would be most suitable for this?

• A. Snapshot
• B. Table
• C. Ephemeral
• D. Incremental

正解：C

解説：
Ephemeral materializations are ideal for rapid development. Since they don't persist results, you can iterate on your model's logic without worrying about storage and the compute cost of rebuilding.

質問 # 132
You need to gather detailed metrics about how your dbt models are used (who runs them, frequency, common failure points, etc.). What kind of tooling would help with this?

• A. Specialized data observability platforms
• B. Version control system metadata (e.g., Git history)
• C. dbt Cloud's built-in job monitoring features, if applicable
• D. Custom logging built into your models

正解：A、C

解説：
B is relevant if you use dbt Cloud. C is suitable, as many observability platforms have features for tracking operational data, including dbt job metrics. A and D, while containing information, are likely too granular or require a lot of manual work.

質問 # 133
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