This set of MapReduce MCQ covers advanced concepts of Big Data Technology related to Combiner, Partitioner, Data Locality, Shuffle and Sort, YARN, InputSplit, and distributed task execution. Useful for university semester examinations, and competitive exams.
Topic: Big Data Technology – MapReduce | Set: 2
Difficulty: Medium to Advanced | Total Questions: 15
MapReduce MCQ Questions
Q1. What is a “Combiner” often called?
- A. Global Reducer
- B. Mini-Mapper
- C. Local Reducer
- D. Data Shifter
View Answer & Explanation
Answer: C
Explanation: A Combiner performs reduction locally on the mapper node to reduce the amount of intermediate data transferred across the network.
Q2. What is the purpose of the Partitioner?
- A. To divide the input file into splits
- B. To determine which reducer will receive a specific key-value pair
- C. To sort the values in descending order
- D. To delete duplicate keys
View Answer & Explanation
Answer: B
Explanation: The Partitioner ensures that all values associated with the same key are sent to the same Reducer.
Q3. What is “Data Locality”?
- A. Moving data to the compute node
- B. Moving the Map task to the node where data resides
- C. Storing data in the local rack only
- D. Encrypting data on the local disk
View Answer & Explanation
Answer: B
Explanation: Hadoop schedules tasks on nodes containing the data blocks to reduce network overhead and improve performance.
Q4. Which interface is used for keys to ensure they can be sorted?
- A. Writable
- B. Readable
- C. WritableComparable
- D. Serializable
View Answer & Explanation
Answer: C
Explanation: WritableComparable allows Hadoop to serialize keys and compare them for sorting during Shuffle and Sort.
Q5. What is the difference between an InputSplit and an HDFS Block?
- A. They are identical
- B. Split is a physical storage unit; Block is logical
- C. Block is a physical storage unit; Split is a logical representation for a task
- D. Splits are only used by Reducers
View Answer & Explanation
Answer: C
Explanation: HDFS blocks represent physical storage units, while InputSplits define the logical input boundaries for Map tasks.
Q6. Where is the intermediate output of the Map task stored?
- A. In HDFS
- B. In the NameNode’s RAM
- C. On the local disk of the Map node
- D. In the Reducer’s memory
View Answer & Explanation
Answer: C
Explanation: Intermediate Map outputs are stored on the mapper’s local disk before being transferred during the Shuffle phase.
Q7. What determines the number of Map tasks in a job?
- A. The number of Reducers
- B. The number of InputSplits
- C. The number of DataNodes
- D. The file size in GB
View Answer & Explanation
Answer: B
Explanation: Hadoop creates one Map task for each logical InputSplit.
Q8. What is the “Shuffle” phase?
- A. The process of deleting old data
- B. The transfer of Map outputs to the Reducers
- C. The process of merging HDFS blocks
- D. The random selection of nodes
View Answer & Explanation
Answer: B
Explanation: Shuffle transfers intermediate key-value pairs from mappers to reducers across the cluster network.
Q9. Why would you use a custom Partitioner?
- A. To speed up the NameNode
- B. To ensure a balanced distribution of keys among Reducers
- C. To compress the final output
- D. To increase the block size
View Answer & Explanation
Answer: B
Explanation: Custom Partitioners help prevent data skew by distributing workloads evenly among reducers.
Q10. In YARN, which component is the per-application coordinator?
- A. ResourceManager
- B. NodeManager
- C. ApplicationMaster
- D. Container
View Answer & Explanation
Answer: C
Explanation: ApplicationMaster manages the execution lifecycle of an individual MapReduce application.
Q11. What is a “RecordReader” responsible for?
- A. Writing the final output to HDFS
- B. Converting InputSplits into key-value pairs for the Mapper
- C. Sorting the Reducer’s input
- D. Counting the number of nodes
View Answer & Explanation
Answer: B
Explanation: RecordReader converts raw input data into key-value records understandable by the Mapper.
Q12. What is the output of the Shuffle and Sort phase?
- A. A list of random keys
- B. A single file on the Reducer
- C. A sorted list of values for each unique key
View Answer & Explanation
Answer: C
Explanation: Reducers receive grouped and sorted values corresponding to each unique key.
Q13. If you have a 1000MB file with a 128MB block size, how many Map tasks will run by default?
- A. 1
- B. 10
- C. 8
- D. 5
View Answer & Explanation
Answer: C
Explanation: 1000 ÷ 128 ≈ 7.8, resulting in 8 blocks and therefore 8 Map tasks.
Q14. Can a MapReduce job have zero Reducers?
- A. No, it will fail
- B. Yes, this is called a “Map-only” job
- C. Only if the input is empty
- D. Only in Hadoop 1.x
View Answer & Explanation
Answer: B
Explanation: Map-only jobs are useful for filtering, transformation, and ETL tasks where aggregation is unnecessary.
Q15. What happens if a Combiner’s logic is different from the Reducer’s logic?
- A. The job runs faster
- B. The final output may be incorrect
- C. Hadoop will automatically fix it
- D. The Map phase will fail
View Answer & Explanation
Answer: B
Explanation: The Combiner logic must remain compatible with the Reducer because it may execute multiple times or not at all.
Conclusion
These advanced MapReduce MCQ Questions help strengthen concepts related to Combiner, Partitioner, Shuffle and Sort, Data Locality, YARN, and distributed data processing. These topics are frequently asked in GATE, IBPS IT Officer, university semester examinations, and technical interviews.
For better understanding, also practice concepts related to HDFS, YARN, Hadoop Architecture, and Big Data ecosystem tools.
Fore theory and concepts, refer to Hadoop Map Reduce.