Quiz: Advanced Pipeline Patterns

1. Which of the following best describes the purpose of incremental loading in a data pipeline?

2. What is a key benefit of modularizing ETL or ELT pipelines in Python?

3. Which logging level should be used to record unexpected failures in a pipeline?

4. In the context of orchestration, what is the main advantage of using a directed acyclic graph (DAG) to manage pipeline tasks?

5. Which of the following is NOT a best practice for building robust data pipelines?

Which of the following best describes the purpose of incremental loading in a data pipeline?

Select the correct answer

To increase the speed of data extraction by parallelizing tasks

To reload the entire dataset from scratch every time

To transform data after loading it into the destination

To process only new or changed data since the last pipeline run

What is a key benefit of modularizing ETL or ELT pipelines in Python?

Select the correct answer

It helps isolate failures and enables easier maintenance.

It makes code less reusable and harder to test.

It eliminates the need for any logging.

It increases the likelihood of data duplication.

Which logging level should be used to record unexpected failures in a pipeline?

Select the correct answer

DEBUG

INFO

WARNING

ERROR

In the context of orchestration, what is the main advantage of using a directed acyclic graph (DAG) to manage pipeline tasks?

Select the correct answer

It increases the complexity of pipeline execution.

It allows circular dependencies between tasks.

It defines clear dependencies and execution order.

It ensures tasks can be executed in any order.

Which of the following is NOT a best practice for building robust data pipelines?

Select the correct answer

Handling errors and exceptions gracefully

Implementing logging and monitoring

Hard-coding database credentials directly in scripts

Modularizing code for reusability

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Sektion 4. Kapitel 5

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Quiz: Advanced Pipeline Patterns

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1. Which of the following best describes the purpose of incremental loading in a data pipeline?

2. What is a key benefit of modularizing ETL or ELT pipelines in Python?

3. Which logging level should be used to record unexpected failures in a pipeline?

4. In the context of orchestration, what is the main advantage of using a directed acyclic graph (DAG) to manage pipeline tasks?

5. Which of the following is NOT a best practice for building robust data pipelines?

Which of the following best describes the purpose of incremental loading in a data pipeline?

Select the correct answer

To increase the speed of data extraction by parallelizing tasks

To reload the entire dataset from scratch every time

To transform data after loading it into the destination

To process only new or changed data since the last pipeline run

What is a key benefit of modularizing ETL or ELT pipelines in Python?

Select the correct answer

It helps isolate failures and enables easier maintenance.

It makes code less reusable and harder to test.

It eliminates the need for any logging.

It increases the likelihood of data duplication.

Which logging level should be used to record unexpected failures in a pipeline?

Select the correct answer

DEBUG

INFO

WARNING

ERROR

In the context of orchestration, what is the main advantage of using a directed acyclic graph (DAG) to manage pipeline tasks?

Select the correct answer

It increases the complexity of pipeline execution.

It allows circular dependencies between tasks.

It defines clear dependencies and execution order.

It ensures tasks can be executed in any order.

Which of the following is NOT a best practice for building robust data pipelines?

Select the correct answer

Handling errors and exceptions gracefully

Implementing logging and monitoring

Hard-coding database credentials directly in scripts

Modularizing code for reusability

Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

Sektion 4. Kapitel 5