Data Engineering Orchestrating Data Workflows Questions and Answers

Question 1

A data engineering team manages a complex data platform.
Job B must run only after Job A completes, and Job C must run after Job B.
If Job B fails, it should be retried three times before an alert is sent.
Which tool is best suited for managing this entire process?

Accepted Answer

A workflow orchestration engine like Apache Airflow

Answer

A stream processing framework like Apache Flink

Answer

A distributed query engine like Trino

Answer

A set of cron jobs on a Linux server

Question 2

In workflow orchestration frameworks, what is the primary role of a Directed Acyclic Graph (DAG)?

Accepted Answer

To define the tasks and their dependencies, showing how they are connected and in what order they should run

Answer

To manage the cluster resources allocated to each task

Answer

To provide a user interface for monitoring workflow execution

Answer

To store the actual data being processed by the workflow

Question 3

Which of the following features is a primary advantage of using a dedicated workflow orchestrator over a series of cron jobs for managing data pipelines?

Accepted Answer

Native support for complex dependency management and failure handling with retries

Answer

Ability to execute scripts on a time-based schedule

Answer

Lower resource consumption for simple, single-task workflows

Answer

Simplified setup for running a single, isolated script

Question 4

A daily data aggregation pipeline failed to run on May 15th due to a temporary network outage. The data for that specific day is now missing from the summary tables. The data engineering team needs to run the pipeline only for May 15th's data. What is this common orchestration practice called?

Accepted Answer

Backfilling

Answer

Checkpointing

Answer

Re-partitioning

Answer

Load balancing

Question 5

When designing a task for a data workflow, the principle of idempotency is crucial for ensuring reliability. What does it mean for a task to be idempotent?

Accepted Answer

The task can be run multiple times with the same input and will produce the same result without causing unintended side effects.

Answer

The task can process data in real-time with sub-second latency.

Answer

The task is guaranteed to complete successfully on the first attempt.

Answer

The task can dynamically scale its resource usage based on the data volume.

Question 6

A data pipeline must be triggered to process new customer reviews immediately after they are submitted and stored as a JSON file in a cloud storage bucket. Which orchestration scheduling pattern is most suitable for this use case?

Accepted Answer

Event-driven scheduling based on a file arrival sensor.

Answer

Time-based scheduling, running every minute.

Answer

Manual triggering by an operator.

Answer

Batch scheduling, running once at the end of the day.