Crowdly

What is the main drawback of reduce-side joins?

No fault tolerance

High network cost due to shuffle

Limited scalability

No sorting

In MapReduce projection, what is done in the Mapper?

Join datasets

Remove unwanted columns

Count values

Sort records

Which issue commonly occurs in MapReduce JOIN when one table is very large?

Skewed join keys

Data duplication

Syntax error

Memory overflow

In MapReduce-based JOIN, what is the Mapper’s role?

Sort entire dataset

Aggregate final results

Assign keys to records based on join attribute

Perform join directly

A MapReduce job is failing due to skewed data (few keys dominate). What is the best solution?

Remove reducer

Increase mappers

Use single node

Use custom partitioner

What will the Reducer output in a Word Count program?

(word, sorted_values)

(word, total_count)

(1, word)

(word, list_of_ones)

Which of the following is TRUE about Reducers?

They process data independently without grouping

They run before mappers

They receive sorted key-value pairs

They only process one key

Which phase ensures that all values of the same key go to the same reducer?

Mapping

Splitting

Combining

Shuffle and Sort

In a Word Count MapReduce program, what does the Mapper emit?

(word, index)

(word, total_count)

(1, word)

(word, 1)