What is the primary purpose of checkpointing in Apache Flink?

To allow multiple jobs to share the same state

To improve the performance of the job by reducing memory usage

To provide fault tolerance by periodically snapshotting the state of the job

To store the results of the job in a durable storage system

A Flink job is submitted and the JobManager creates an execution graph. What does this execution graph represent?

The configuration of the TaskManagers

The data flow between sources and sinks only

The physical tasks that will run on TaskManagers

The logical operators and their connections

A Flink job has a stateful operator with parallelism 4. The state is keyed by user ID. How is the state distributed across the TaskManagers?

Each parallel subtask holds the state for a subset of keys

The state is stored in an external database

Each TaskManager holds a copy of all keys

The state is stored only on the JobManager

A Flink job has a source with parallelism 2, a keyed aggregation with parallelism 4, and a sink with parallelism 2. The team wants to minimize network shuffling. What should they do?

Set the parallelism of the sink to 4

Set the parallelism of the source to 4

Use a keyed aggregation with parallelism 2

A Flink job uses keyed state and checkpointing. The team wants to scale the parallelism of the job from 4 to 8. What is required to preserve the state?

The state will automatically redistribute without any action

The state will be stored on the JobManager and can be rescaled

The state will be lost because scaling is not supported

Take a savepoint, rescale, and restore from the savepoint

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Domain 5Objective 5

Describe Apache Flink and Its Architecture DATA-ENGINEERING-OPTIMIZE Practice Questions (Page 6)

Part of the Processing Streaming and IoT Data domain, which accounts for 20% of the DATA-ENGINEERING-OPTIMIZE exam.

30questions here

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Questions 26–30

26foundation · easy
What is the primary purpose of checkpointing in Apache Flink?
Select an answer first
27application · medium
A Flink job is submitted and the JobManager creates an execution graph. What does this execution graph represent?
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28application · medium
A Flink job has a stateful operator with parallelism 4. The state is keyed by user ID. How is the state distributed across the TaskManagers?
Select an answer first
29expert · hard
A Flink job has a source with parallelism 2, a keyed aggregation with parallelism 4, and a sink with parallelism 2. The team wants to minimize network shuffling. What should they do?
Select an answer first
30expert · hard
A Flink job uses keyed state and checkpointing. The team wants to scale the parallelism of the job from 4 to 8. What is required to preserve the state?
Select an answer first

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