Big Data Analytics MCQs

Namespace operations are written to the edit log for durability.
They are later merged with the namespace image during checkpointing.

The framework groups mapper output by key before reduction.
The reducer aggregates or transforms each key's values.

Map-only jobs are suitable when records can be processed independently.
Mapper output is written directly as final output.

Each mapper loads the small table and joins it with its input partition.
This avoids reducer-side network transfer for the large dataset.

Local top-N lists can be produced by mappers and merged globally.
This reduces the amount of data sent to the final stage.

Stream processing evaluates events as they arrive or in short windows.
It supports monitoring, alerts, and near-real-time analytics.

Big Data systems distribute storage and computation across multiple machines.
This allows workloads to scale beyond the limits of one server.

Latency measures responsiveness.
Interactive and streaming applications usually require lower latency than batch jobs.

Volume describes the scale of a dataset.
Large volume motivates distributed storage and parallel processing.

Throughput measures processing capacity over time.
Batch platforms often optimize for high throughput.

Velocity concerns data generation, ingestion, and response speed.
Streaming systems are often used when low-latency processing is required.

Variety captures differences in format, schema, and media type.
Examples include tables, logs, JSON, images, and text.