Dell Data Engineering Optimize
Domain 6Objective 3
Describe the Use of Apache Airflow DATA-ENGINEERING-OPTIMIZE Practice Questions (Page 2)
Part of the Building Data Pipelines with Python domain, which accounts for 20% of the DATA-ENGINEERING-OPTIMIZE exam.
22questions here
5free pages
6concepts
20%of the exam
Questions 6–10
- 6
A data engineering team is deploying Airflow in a Kubernetes cluster. They want to ensure that each task runs in its own isolated container, and they want to be able to scale the number of task instances dynamically based on the workload. Which executor should they use?
Select an answer first - 7
How does Apache Airflow determine when to trigger a new DAG run?
Select an answer first - 8
A data engineering team is setting up Airflow for a small team with limited resources. They want to run tasks in parallel on a single machine, but they do not need distributed execution. Which executor should they choose?
Select an answer first - 9
In an Airflow DAG, there are two upstream tasks: 'task_a' and 'task_b'. The downstream task 'task_c' should run only if at least one of the upstream tasks succeeds. Which trigger rule should be set on 'task_c'?
Select an answer first - 10
In an Airflow DAG, there is a task that should run only if a previous task fails, to handle error scenarios. Which trigger rule should be set on this task?
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