Big Data Analytics MCQs

Processing time depends on ingestion delay and system load.
It is easier to use but can produce inconsistent windows for late data.

Regular heartbeats carry resource and status information.
The ResourceManager uses it for scheduling decisions.

Memory and CPU controls support predictable multi-tenant operation.
Without isolation, a faulty task could monopolize a worker.

The operation repeatedly merges two elements into one result.
For parallel use, associative operations are especially important.

Without yield, a for-comprehension is commonly used for side effects.
With yield, it transforms input elements into a new collection.

Range partitioning ensures reducer outputs cover consecutive key intervals.
Concatenating part files in reducer order yields global order.

The Scheduler decides which applications receive containers.
It does not directly monitor the internal logic of tasks.

Metrics such as CPU, memory, disk, and network usage guide tuning.
Monitoring also supports capacity planning and incident response.

Distributed engines can aggregate partitions in varying tree shapes.
Associativity ensures the grouping does not change the answer.

Classes can extend multiple traits.
Traits support interfaces and shared implementation.

Samples approximate the key distribution.
Good boundaries balance reducer loads while preserving global ranges.

Queues receive guaranteed capacity while unused resources may be shared.
This supports predictable multi-tenant cluster use.