dbt-Analytics-Engineering信息資訊 - dbt-Analytics-Engineering題庫

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Managing Data Pipelines	15%	- Pipeline orchestration - Deployment strategies - CI/CD integration
Topic 2: Implementing dbt Tests	10%	- Built-in tests - Test configuration and execution - Custom tests
Topic 3: Leveraging dbt State	5%	- State management - State-aware operations
Topic 4: Debugging and Error Resolution	15%	- Debugging techniques - Identifying modeling errors - Resolving data quality issues
Topic 5: Developing dbt Models	20%	- Model design and structure 1. Incremental models 2. Materialization types 3. Ref and source functions - Model optimization 1. Performance tuning 2. Model contracts
Topic 6: dbt Models Governance	15%	- Naming conventions and standards - Project organization and structure - Version control integration
Topic 7: External Dependencies	10%	- External sources integration - Using packages - Managing snapshots
Topic 8: Creating and Maintaining Documentation	10%	- Documentation standards - Generating documentation - Descriptions and metadata

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最新的 Analytics Engineers dbt-Analytics-Engineering 免費考試真題 (Q29-Q34):

問題 #29
Which two code snippets result in a lineage line being shown in the DAG? Choose 2 options.

A. {{ source('jaffle_shop.customers') }}
B. dbt_jsmith.stg_jaffle_shop__customers
C. {{ ref('stg_jaffle_shop__customers') }}
D. {{ source('source', 'table') }}
E. {{ target.schema }}

答案：C,D

解題說明：
In dbt, the Directed Acyclic Graph (DAG) is created from model dependencies, which dbt identifies only through two mechanisms: the ref() function for referencing dbt models and the source() function for referencing declared sources. These functions are explicitly designed to tell dbt how models relate to each other so that dbt can track lineage, enforce build order, and generate documentation.
Option A, {{ source('source', 'table') }}, correctly references a declared source. Any model using source() establishes an upstream dependency on that source table. dbt will therefore draw a lineage line in the DAG from the source to the model.
Option B, {{ ref('stg_jaffle_shop__customers') }}, is the canonical way to reference another dbt model. ref() ensures dbt determines dependencies at compile time, and thus creates a DAG edge between this model and its parent.
The remaining options do not create lineage:
* C directly references a database object, which dbt cannot interpret as a dependency.
* D uses a Jinja variable unrelated to lineage.
* E is invalid, because source() requires two arguments: a source name and table name, not a dotted string.
Therefore, A and B are the only valid answers.

問題 #30
You encounter an unexpected dbt run error. Rather than re-running all models, you want to resume from the point of failure. Which of the following, if any, might allow for this?

A. dbt unfortunately lacks native support for resumable jobs.
B. The --fail-fast flag instructs dbt to only execute successful models, making restarts unnecessary
C. dbt Cloud's job retry features for automatically resuming failed job executions.
D. Using dbt snapshots to track model states, allowing you to rollback changes as needed

答案：C

解題說明：
C can retry in event of transient errors, but not fundamental code problems. The rest are incorrect dbt typically isn't resumable mid-job.

問題 #31
A new stakeholder needs to receive an email report generated from the results of a specific dbt model. Unfortunately, your Bl tool doesn't directly integrate with dbt. How could you address this?

A. Develop a separate script, triggered after the dbt job, to handle report generation and emailing-
B. Within the model's SQLi write logic to insert the output data into a dedicated reporting table-
C. Write a post-hook that queries the model, formats the results, and sends the email.
D. Modify the model to output a CSV file and schedule a task to email the file as an attachment

答案：A,B,C

解題說明：
Each provides a means to get the data outA does it within dbt workflow B uses an external tool for the non- dbt task. C leaves the data ready for other tools. D is clumsy and adds storage overhead.

問題 #32
After configuring a dbt package, you notice its models depend on a source table absent from your warehouse. How do you typically address this?

A. Update your source definitions to include the required table.
B. File an issue or submit a pull request to the package's repository.
C. Rewrite the package's model from scratch within your project to remove the dependency.
D. Override the package model's SQL query to reference an existing table in your warehouse.

答案：D

解題說明：
Start by fulfilling the package's dependencies. If the table is truly generic, then customizing the SQL (B) might be the next step.

問題 #33
A marketing team needs a model to calculate the 30-day rolling average of new customer signups. The raw signups table is very large and updated hourly. Which approach strikes the best balance between accuracy and computational efficiency?

A. An incremental model that updates the rolling average calculations only for new signup records.
B. A table materialization that completely rebuilds the rolling average daily.
C. A view materialization that recalculates the 30-day rolling average for each query.
D. An ephemeral model that calculates the rolling average within a series of complex CTEs.

答案：A

解題說明：
Incremental models are designed for metrics like this. They minimize processing overhead while ensuring the rolling average stays up-to-date. Options A and B are computationally expensive, while D doesn't address storage for reuse.

問題 #34
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