1What is MapReduce primarily used for in the Hadoop ecosystem?
Introduction to MapReduce
Easy
A.Network routing
B.Distributed data storage
C.Relational database management
D.Distributed data processing
Correct Answer: Distributed data processing
Explanation:
MapReduce is a programming model and software framework designed specifically for writing applications that rapidly process vast amounts of data in parallel on large clusters of compute nodes.
Incorrect! Try again.
2In the MapReduce framework, what is the standard output format of the Map phase?
Map Phase
Easy
A.Key-Value pairs
B.XML files
C.Plain text sentences
D.Relational tables
Correct Answer: Key-Value pairs
Explanation:
The Mapper processes the input data and generates intermediate output exclusively in the form of key-value pairs.
Incorrect! Try again.
3What is the primary function of the Reducer in a MapReduce job?
Reduce Phase
Easy
A.Storing data on disks
B.Managing cluster resources
C.Aggregating and summarizing data
D.Splitting data into smaller blocks
Correct Answer: Aggregating and summarizing data
Explanation:
The Reducer takes the intermediate key-value pairs generated by the Mappers and aggregates or summarizes them to produce the final output.
Incorrect! Try again.
4What does YARN stand for in Big Data processing?
Introduction to YARN
Easy
A.Your Application Resource Network
B.Yield And Resource Network
C.Yet Another Relational Node
D.Yet Another Resource Negotiator
Correct Answer: Yet Another Resource Negotiator
Explanation:
YARN stands for Yet Another Resource Negotiator. It is the architectural center of Hadoop that allows multiple data processing engines to handle data on a single platform.
Incorrect! Try again.
5YARN was introduced in which major version of Hadoop to resolve the scalability bottlenecks of the classic MapReduce architecture?
Introduction to YARN
Easy
A.Hadoop 0.5
B.Hadoop 1.x
C.Hadoop 2.x
D.Hadoop 4.x
Correct Answer: Hadoop 2.x
Explanation:
YARN was introduced in Hadoop 2.x to separate resource management and job scheduling from the data processing layer.
Incorrect! Try again.
6Which component in YARN is considered the master daemon responsible for globally managing and allocating resources across the entire cluster?
YARN Architecture
Easy
A.ApplicationMaster
B.JobTracker
C.NodeManager
D.ResourceManager
Correct Answer: ResourceManager
Explanation:
The ResourceManager is the master node component in YARN that tracks resources across the cluster and allocates them to applications.
Incorrect! Try again.
7In YARN, which component runs on each individual worker node to monitor resource usage (CPU, memory) and report it to the master?
YARN Architecture
Easy
A.ResourceManager
B.NodeManager
C.ApplicationMaster
D.NameNode
Correct Answer: NodeManager
Explanation:
The NodeManager is the per-machine framework agent responsible for managing containers and monitoring resource usage on its specific node.
Incorrect! Try again.
8In YARN, what is the role of the ApplicationMaster?
YARN Architecture
Easy
A.Replacing the DataNode
B.Managing the entire cluster's memory
C.Storing the metadata of HDFS
D.Negotiating resources and tracking application status
Correct Answer: Negotiating resources and tracking application status
Explanation:
The ApplicationMaster is responsible for negotiating appropriate resource containers from the ResourceManager and tracking the status and progress of an individual application.
Incorrect! Try again.
9What term is used in YARN to represent a fraction of cluster resources (like memory, CPU) allocated to execute a specific task?
YARN Architecture
Easy
A.Split
B.Container
C.Pod
D.Block
Correct Answer: Container
Explanation:
A Container is a logical bundle of resources (such as RAM and CPU) allocated by the ResourceManager to execute a specific application task.
Incorrect! Try again.
10Which crucial phase occurs between the Map phase and the Reduce phase to group identical keys together?
MapReduce Execution Flow
Easy
A.Combine and Merge
B.Write and Replicate
C.Split and Read
D.Shuffle and Sort
Correct Answer: Shuffle and Sort
Explanation:
The Shuffle and Sort phase automatically groups the intermediate key-value pairs by key before they are sent to the Reducer.
Incorrect! Try again.
11Which optional component is often called a "mini-reducer" because it performs local aggregation on the Mapper output to reduce network bandwidth?
MapReduce Execution Flow
Easy
A.Combiner
B.Shuffler
C.Partitioner
D.DataNode
Correct Answer: Combiner
Explanation:
A Combiner performs a local reduce operation on the map output to decrease the volume of data that needs to be transferred across the network to the actual Reducer.
Incorrect! Try again.
12What determines which specific Reducer instance will process a given key-value pair in a MapReduce job?
MapReduce Execution Flow
Easy
A.JobTracker
B.Partitioner
C.NameNode
D.Combiner
Correct Answer: Partitioner
Explanation:
The Partitioner divides the key space and assigns specific keys to specific Reducers, usually through a hash function.
Incorrect! Try again.
13In the older Hadoop 1.x architecture, which central daemon was strictly responsible for both resource management and job scheduling?
MapReduce Architecture
Easy
A.JobTracker
B.ApplicationMaster
C.TaskTracker
D.NodeManager
Correct Answer: JobTracker
Explanation:
In Hadoop 1.x, the JobTracker was a single point of failure and bottleneck because it handled both resource management and job execution tracking.
Incorrect! Try again.
14In Hadoop 1.x, which worker daemon accepted tasks from the master daemon and executed the Map and Reduce operations?
MapReduce Architecture
Easy
A.DataNode
B.TaskTracker
C.ResourceManager
D.NodeManager
Correct Answer: TaskTracker
Explanation:
TaskTrackers ran on worker nodes in Hadoop 1.x to execute the individual Map and Reduce tasks assigned by the JobTracker.
Incorrect! Try again.
15What is the logical representation of a chunk of input data that is processed by a single Mapper instance?
MapReduce Execution Flow
Easy
A.Output Split
B.Input Split
C.HDFS File
D.Data Block
Correct Answer: Input Split
Explanation:
An Input Split is a logical chunk of data created by the InputFormat. Exactly one Mapper is launched to process each Input Split.
Incorrect! Try again.
16Which of the following statements about MapReduce job execution is true?
MapReduce Execution Flow
Easy
A.The Reduce phase starts simultaneously with the Map phase.
B.The Reduce phase cannot start processing data until all Map tasks have completed.
C.Mappers send data directly to HDFS without a Reducer.
D.MapReduce jobs do not require a Map phase.
Correct Answer: The Reduce phase cannot start processing data until all Map tasks have completed.
Explanation:
Because Reducers must aggregate data across all keys, the Reduce function cannot begin its core processing until all Mappers have finished and the shuffle phase is complete.
Incorrect! Try again.
17What is a major advantage of YARN over the classic Hadoop 1.x MapReduce architecture?
Introduction to YARN
Easy
A.It increases the physical block size of HDFS.
B.It removes the need for DataNodes entirely.
C.It requires all programming to be done in Python.
D.It supports diverse processing engines (like Spark) alongside MapReduce.
Correct Answer: It supports diverse processing engines (like Spark) alongside MapReduce.
Explanation:
YARN decoupled resource management from the MapReduce processing model, allowing other distributed computing engines like Spark, Flink, and Storm to run on the same Hadoop cluster.
Incorrect! Try again.
18In Hadoop MapReduce, which standard Java interface must keys and values implement so they can be serialized and sent over the network?
MapReduce Execution Flow
Easy
A.Serializable
B.Externalizable
C.Cloneable
D.Writable
Correct Answer: Writable
Explanation:
Hadoop uses its own optimized serialization format, requiring custom data types to implement the Writable interface (or WritableComparable for keys).
Incorrect! Try again.
19In a standard Hadoop YARN cluster, where does the NodeManager typically run?
YARN Architecture
Easy
A.On the same machines as DataNodes (worker nodes)
B.On the same machine as the NameNode
C.Exclusively on the client machine
D.Outside the Hadoop cluster
Correct Answer: On the same machines as DataNodes (worker nodes)
Explanation:
The NodeManager is a worker daemon in YARN, so it runs on the slave/worker nodes, typically co-located with HDFS DataNodes to support data locality.
Incorrect! Try again.
20How many Output files are typically created by a MapReduce job that is configured to run with Reducers?
Map Phase
Easy
A. files
B.Depends on the number of Mappers
C. files
D.1 combined file
Correct Answer: files
Explanation:
By default, each Reducer task writes its final output to its own distinct file in HDFS, typically named part-r-00000, part-r-00001, up to .
Incorrect! Try again.
21Which of the following scenarios best demonstrates the appropriate use of a Combiner in a MapReduce job?
MapReduce Architecture
Medium
A.Calculating the exact average of a dataset across multiple nodes before the Reducer phase.
B.Performing a local aggregation of word counts on the Mapper node to reduce network bandwidth during the shuffle phase.
C.Sorting the output of the Reducer phase before writing it to HDFS.
D.Splitting a massive file into smaller chunks to increase the number of Map tasks.
Correct Answer: Performing a local aggregation of word counts on the Mapper node to reduce network bandwidth during the shuffle phase.
Explanation:
A Combiner acts as a 'mini-reducer' that processes the output of a single Mapper locally. Its primary application is to aggregate intermediate data (like sums or counts) to minimize the amount of data transferred across the network during the shuffle phase. It cannot be safely used for operations like averages without careful design, as averages are not associative.
Incorrect! Try again.
22If a file is 300 MB and the HDFS block size is 128 MB, how many Map tasks will typically be launched by default, and why?
Map Phase
Medium
A.4 Map tasks, because the framework always allocates an extra Mapper for overhead processing.
B.2 Map tasks, because the framework calculates and rounds down.
C.3 Map tasks, because the data is divided into three InputSplits corresponding to the three HDFS blocks (128 MB, 128 MB, and 44 MB).
D.1 Map task, because a single file always maps to a single Map task to maintain data locality.
Correct Answer: 3 Map tasks, because the data is divided into three InputSplits corresponding to the three HDFS blocks (128 MB, 128 MB, and 44 MB).
Explanation:
By default, one Map task is created for each InputSplit, and an InputSplit typically corresponds to an HDFS block. A 300 MB file will be divided into blocks of 128 MB, 128 MB, and a final block of 44 MB, resulting in 3 Map tasks.
Incorrect! Try again.
23How does a Partitioner operate in a MapReduce job?
Shuffle and Sort
Medium
A.It determines which Mapper processes which HDFS block based on node locality.
B.It sorts the values for a given key in descending order before they reach the Reducer.
C.It filters out null keys from the Mapper output before the shuffle phase begins.
D.It determines which Reducer will receive a specific key-value pair based on the hash of the key.
Correct Answer: It determines which Reducer will receive a specific key-value pair based on the hash of the key.
Explanation:
The Partitioner controls the partitioning of the intermediate map-output keys. The key (or a subset of the key) is used to derive the partition, typically by a hash function. The total number of partitions is the same as the number of reduce tasks for the job.
Incorrect! Try again.
24What is the purpose of Speculative Execution in Hadoop MapReduce?
MapReduce Architecture
Medium
A.To predict the output of a Map task before it finishes to speed up the Reducer.
B.To execute tasks speculatively in memory without writing intermediate data to disk.
C.To execute Map tasks on data that has not yet been written to HDFS.
D.To identify tasks running significantly slower than average and launch duplicate backup tasks on other nodes.
Correct Answer: To identify tasks running significantly slower than average and launch duplicate backup tasks on other nodes.
Explanation:
Speculative execution is a fault-tolerance mechanism. If a node is underperforming (e.g., due to hardware degradation), the framework launches a duplicate task on a faster node. Whichever task finishes first is used, and the other is killed.
Incorrect! Try again.
25What happens if a developer configures a MapReduce job with setNumReduceTasks(0)?
Reduce Phase
Medium
A.The job will fail because a MapReduce job requires at least one Reducer.
B.The job becomes a 'Map-only' job, and the Mapper outputs are written directly to HDFS without sorting or shuffling.
C.The framework will hold the Mapper output in memory until a Reducer becomes available.
D.The ResourceManager will automatically assign exactly one Reducer to prevent data loss.
Correct Answer: The job becomes a 'Map-only' job, and the Mapper outputs are written directly to HDFS without sorting or shuffling.
Explanation:
Setting the number of Reducers to 0 creates a Map-only job. The shuffle, sort, and reduce phases are bypassed, and the output of the Mappers is written directly to the final destination in HDFS.
Incorrect! Try again.
26Which of the following best describes the structural difference between Hadoop 1.x (MRv1) and YARN regarding resource management?
YARN Architecture
Medium
A.YARN separates the dual responsibilities of the JobTracker (resource management and job scheduling/monitoring) into the ResourceManager and ApplicationMaster.
B.YARN combines the JobTracker and TaskTracker into a single unified service.
C.YARN eliminates the need for an ApplicationMaster by allowing the NodeManager to schedule jobs directly.
D.YARN moves resource management to HDFS DataNodes, bypassing the need for a central coordinator.
Correct Answer: YARN separates the dual responsibilities of the JobTracker (resource management and job scheduling/monitoring) into the ResourceManager and ApplicationMaster.
Explanation:
In MRv1, the JobTracker was responsible for both managing resources and tracking job execution. YARN (Yet Another Resource Negotiator) splits these roles: the ResourceManager handles global resource allocation, while a per-application ApplicationMaster manages job scheduling and monitoring.
Incorrect! Try again.
27What are the two main components of the YARN ResourceManager?
ResourceManager
Medium
A.JobTracker and TaskTracker
B.ApplicationMaster and NameNode
C.NodeManager and Container
D.Scheduler and ApplicationsManager
Correct Answer: Scheduler and ApplicationsManager
Explanation:
The ResourceManager contains two main components: the Scheduler (responsible for allocating resources to various running applications) and the ApplicationsManager (responsible for accepting job submissions and negotiating the first container for the ApplicationMaster).
Incorrect! Try again.
28In a YARN cluster, what happens if an ApplicationMaster fails during the execution of a job?
ApplicationMaster
Medium
A.The entire cluster shuts down to prevent data corruption.
B.The NodeManager takes over the role of the ApplicationMaster for that specific job.
C.The ResourceManager immediately fails the application and alerts the client.
D.The ResourceManager restarts the ApplicationMaster in a new container, and the job may recover depending on the framework.
Correct Answer: The ResourceManager restarts the ApplicationMaster in a new container, and the job may recover depending on the framework.
Explanation:
The ApplicationsManager component of the ResourceManager monitors the ApplicationMaster. If it fails, the RM can restart it in a new container. Modern frameworks like MapReduce can often recover previously completed tasks so the job doesn't have to start entirely from scratch.
Incorrect! Try again.
29A YARN Container represents a collection of physical resources. If a task inside a container attempts to use more RAM than allocated, what is the default behavior of YARN?
Containers
Medium
A.The task will pause until memory becomes available in the cluster.
B.The NodeManager will seamlessly allocate more memory from the node's reserve pool.
C.The NodeManager will kill the container for exceeding its physical memory limit.
D.The ApplicationMaster will negotiate an expansion of the container's size with the ResourceManager.
Correct Answer: The NodeManager will kill the container for exceeding its physical memory limit.
Explanation:
YARN tightly monitors container resource usage. By default, if a container exceeds its allocated physical or virtual memory limits, the NodeManager will aggressively kill the container to protect the stability of the node.
Incorrect! Try again.
30Which YARN scheduler allocates a fraction of cluster capacity to multiple organizations, allowing them to utilize unused cluster capacity when available, but restricts them to their guaranteed minimum when the cluster is busy?
YARN Scheduling
Medium
A.Preemptive Scheduler
B.FIFO Scheduler
C.Fair Scheduler
D.Capacity Scheduler
Correct Answer: Capacity Scheduler
Explanation:
The Capacity Scheduler is designed to allow multiple tenants to share a large cluster. Organizations are configured with guaranteed capacity (queues) but can temporarily utilize excess capacity if other queues are empty. When demand returns, they are constrained to their guaranteed capacity.
Incorrect! Try again.
31During the shuffle phase, MapReduce must transfer intermediate data across the network. How is this data managed on the Mapper node before transfer?
MapReduce Architecture
Medium
A.It is written to a circular memory buffer, spilled to local disk when the buffer reaches a threshold, and then merged.
B.It is immediately streamed to the Reducer task without being stored on the Mapper node.
C.It is written directly to an HDFS block so it can be replicated for fault tolerance.
D.It is kept entirely in RAM until the Reducer requests it via an RPC call.
Correct Answer: It is written to a circular memory buffer, spilled to local disk when the buffer reaches a threshold, and then merged.
Explanation:
Map tasks write their output to a circular memory buffer. When the buffer reaches a certain threshold (e.g., 80%), a background thread begins spilling the contents to the node's local disk (not HDFS). These spills are later merged into a single partitioned and sorted file.
Incorrect! Try again.
32What is the primary role of the Heartbeat mechanism between the NodeManager and the ResourceManager in YARN?
NodeManager
Medium
A.To inform the ResourceManager of the NodeManager's health and available resources, and to receive container execution commands.
B.To update HDFS block locations to the NameNode.
C.To negotiate resource limits directly with the ApplicationMaster.
D.To transfer MapReduce job output data from the worker node to the master node.
Correct Answer: To inform the ResourceManager of the NodeManager's health and available resources, and to receive container execution commands.
Explanation:
NodeManagers send periodic heartbeats to the ResourceManager to confirm they are alive and to report their resource usage. In response, the ResourceManager can send commands, such as instructions to start or kill containers.
Incorrect! Try again.
33In the context of the Fair Scheduler in YARN, what does 'preemption' allow the scheduler to do?
YARN Scheduling
Medium
A.Predict which jobs will run longest and place them at the end of the queue.
B.Bypass the ApplicationMaster and schedule tasks directly on NodeManagers.
C.Allocate memory dynamically beyond the node's physical limits.
D.Kill containers from a queue that is over its fair share to free up resources for a starved queue.
Correct Answer: Kill containers from a queue that is over its fair share to free up resources for a starved queue.
Explanation:
Preemption in the Fair Scheduler (and Capacity Scheduler) allows the scheduler to kill containers of applications that are using more than their fair share of resources, in order to give those resources back to applications in queues that are starved of their guaranteed/fair share.
Incorrect! Try again.
34In a standard MapReduce job, what is the input format received by the reduce() function?
Reduce Phase
Medium
A.A single key and a single value, e.g., (Key, Value).
B.An array of key-value pairs representing the entire dataset.
C.A single key and an Iterable collection of values, e.g., (Key, Iterable<Value>).
D.A list of keys and a single aggregated value, e.g., (List<Key>, Value).
Correct Answer: A single key and an Iterable collection of values, e.g., (Key, Iterable<Value>).
Explanation:
After the shuffle and sort phases, all values associated with a specific key are grouped together. The reduce() function is called once per unique key, receiving the key and an Iterator/Iterable of all values associated with that key.
Incorrect! Try again.
35Why is 'Data Locality' a critical optimization in the MapReduce framework?
MapReduce Architecture
Medium
A.It forces all data to be stored on local node disks rather than in HDFS.
B.It guarantees that Reducers are always placed on the same rack as the client submitting the job.
C.It ensures that intermediate shuffle data is encrypted locally before network transfer.
D.It schedules Map tasks on the exact same nodes where the required HDFS blocks reside, minimizing network congestion.
Correct Answer: It schedules Map tasks on the exact same nodes where the required HDFS blocks reside, minimizing network congestion.
Explanation:
Data Locality is the principle of moving the computation to the data rather than moving data to the computation. MapReduce attempts to launch Map tasks on the very nodes where the HDFS blocks are stored, vastly reducing network I/O overhead.
Incorrect! Try again.
36To achieve High Availability (HA) for the YARN ResourceManager, an Active/Standby architecture is used. What component typically manages the state and leader election to handle automatic failover?
ResourceManager
Medium
A.JobHistoryServer
B.Secondary NameNode
C.Apache Zookeeper
D.HDFS JournalNodes
Correct Answer: Apache Zookeeper
Explanation:
YARN ResourceManager HA uses Apache ZooKeeper for leader election and state storage. If the Active ResourceManager fails, ZooKeeper helps elect the Standby ResourceManager to become the new Active master without manual intervention.
Incorrect! Try again.
37Which interface allows a MapReduce application to broadcast read-only files (like lookup tables or dictionaries) to all worker nodes before tasks execute?
MapReduce Architecture
Medium
A.InputFormat
B.DistributedCache
C.Combiner
D.Partitioner
Correct Answer: DistributedCache
Explanation:
The DistributedCache is a facility provided by the MapReduce framework to cache files (text, archives, jars) needed by applications. It copies the files to the worker nodes just before the tasks are executed, making them available locally.
Incorrect! Try again.
38When an ApplicationMaster requires resources to run tasks, how does it specify its request to the ResourceManager?
ApplicationMaster
Medium
A.By commanding the NodeManager to allocate a certain percentage of its local disk.
B.By sending a ResourceRequest containing memory, CPU requirements, preferred nodes/racks, and priority.
C.By editing the yarn-site.xml file dynamically during runtime.
D.By requesting specific HDFS block locations directly from the NameNode.
Correct Answer: By sending a ResourceRequest containing memory, CPU requirements, preferred nodes/racks, and priority.
Explanation:
The ApplicationMaster negotiates with the ResourceManager by sending a ResourceRequest. This request specifies the amount of memory and vCores needed, priority, and locality preferences (node-local, rack-local, or anywhere).
Incorrect! Try again.
39What is the defining characteristic of a YARN Application?
YARN Architecture
Medium
A.It must always consist of exactly one Map phase and one Reduce phase.
B.It is the global queue managed by the Capacity Scheduler.
C.It is a single job or a DAG of jobs coordinated by a single ApplicationMaster.
D.It refers strictly to a daemon process running permanently on a NodeManager.
Correct Answer: It is a single job or a DAG of jobs coordinated by a single ApplicationMaster.
Explanation:
In YARN, an 'Application' is an abstraction that can represent a single job (like a MapReduce job), a DAG of tasks (like Tez or Spark), or a long-running service. It is defined by having a dedicated ApplicationMaster to manage its lifecycle.
Incorrect! Try again.
40If two keys, and , yield the exact same hash code from the Partitioner, what is the consequence in the MapReduce pipeline?
Shuffle and Sort
Medium
A. and will be sent to the same Reducer task, where they will be sorted and grouped separately.
B.The Partitioner will automatically assign to a random Reducer to balance the load.
C. and will be merged into a single key by the Combiner.
D.The Map task will fail with a HashCollisionException.
Correct Answer: and will be sent to the same Reducer task, where they will be sorted and grouped separately.
Explanation:
The Partitioner uses the hash code to determine the Reducer. If two distinct keys have the same hash code (a hash collision), they map to the same partition and are sent to the same Reducer. However, the Reducer will still sort and group them correctly because they are distinct keys.
Incorrect! Try again.
41In a MapReduce job, what happens if an InputSplit boundary occurs in the middle of a logical record (e.g., a line in a text file)?
MapReduce Execution Framework
Hard
A.The Map task skips the partial record entirely, resulting in data loss unless custom error handling is implemented.
B.The Map task reads past the end of its InputSplit block into the next block to finish the record, while the adjacent Map task skips the first partial record.
C.The Map task processes the partial record and raises an exception for the next Map task.
D.The JobTracker/ResourceManager automatically realigns the block boundaries in HDFS before launching the Map tasks.
Correct Answer: The Map task reads past the end of its InputSplit block into the next block to finish the record, while the adjacent Map task skips the first partial record.
Explanation:
To ensure no records are split or missed, the LineRecordReader will read past the end of its assigned block to find the next newline character. The Map task assigned to the subsequent block will skip data until it encounters the first newline, ensuring each record is processed exactly once.
Incorrect! Try again.
42A developer writes a MapReduce job to calculate the global average of values associated with a key. They use the same Reducer implementation as the Combiner to optimize network traffic. Which of the following describes the outcome of this decision?
Combiners and Partitioners
Hard
A.The framework will throw an execution error because a Reducer cannot logically be used as a Combiner in MRv2.
B.The job will execute faster and produce the correct global average because averages are commutative.
C.The job will execute faster but produce incorrect results because calculating a mean is not an associative and commutative operation.
D.The job will produce correct results only if all map tasks emit exactly the same number of records per key.
Correct Answer: The job will execute faster but produce incorrect results because calculating a mean is not an associative and commutative operation.
Explanation:
A Combiner must implement a commutative and associative operation (like sum or max) to guarantee correct results, because the framework does not guarantee if or how many times a Combiner will be called. Calculating the average of averages yields incorrect results unless the counts of elements are also tracked and weighted appropriately.
Incorrect! Try again.
43During the shuffle phase, Reducers must fetch Map outputs from various NodeManagers. How does a Reducer task efficiently determine the locations of the completed Map outputs?
Shuffle and Sort Phase
Hard
A.The NodeManagers actively push the partitioned data to the Reducer containers as soon as the Map tasks complete.
B.It broadcasts a request to all NodeManagers in the cluster asking for map outputs associated with its partition.
C.It queries the HDFS NameNode, which tracks the temporary spilled map outputs.
D.It periodically queries the ApplicationMaster, which receives task completion reports and physical locations from the completed Map tasks.
Correct Answer: It periodically queries the ApplicationMaster, which receives task completion reports and physical locations from the completed Map tasks.
Explanation:
In MapReduce on YARN, the ApplicationMaster keeps track of the state and location of all completed Map tasks. Reducers spawn fetcher threads that periodically poll the ApplicationMaster via RPC to discover where their corresponding partition data resides.
Incorrect! Try again.
44Under the YARN Fair Scheduler using Dominant Resource Fairness (DRF), consider a cluster with 100 CPUs and 1000 GB of RAM. App A requests containers with 2 CPUs and 10 GB of RAM, while App B requests containers with 1 CPU and 20 GB of RAM. How are the dominant shares calculated?
YARN Scheduling
Hard
A.App A's dominant resource is CPU (2%); App B's dominant resource is CPU (1%) due to normalization.
B.App A's dominant resource is CPU (2%); App B's dominant resource is Memory (2%).
C.Both applications have CPU as their dominant resource because CPU scheduling inherently supersedes Memory scheduling in YARN.
D.App A's dominant resource is Memory (1%); App B's dominant resource is CPU (1%).
Correct Answer: App A's dominant resource is CPU (2%); App B's dominant resource is Memory (2%).
Explanation:
DRF calculates the share of each resource type a container requests relative to the cluster's total. For App A, CPU share is and RAM share is ; the dominant resource is CPU (). For App B, CPU is and RAM is ; the dominant resource is RAM ().
Incorrect! Try again.
45A MapReduce job performs a complex data transformation and inserts the output records directly into an external non-idempotent relational database from within the Map tasks. Speculative execution is enabled by default. What critical issue will arise in this scenario?
Fault Tolerance and Speculative Execution
Hard
A.The MapReduce job will fail because YARN cannot serialize database connection objects across the cluster.
B.The external database will reject the connections due to Kerberos ticket mismatches generated by speculative containers.
C.Data duplication will occur because speculative tasks will insert duplicate records before the framework kills the slower task.
D.The ApplicationMaster will deadlock because it cannot lock the external database rows.
Correct Answer: Data duplication will occur because speculative tasks will insert duplicate records before the framework kills the slower task.
Explanation:
Speculative execution launches duplicate tasks for stragglers. If tasks have external, non-idempotent side effects (like INSERTs into an RDBMS), multiple task attempts will write the same data, leading to duplication. Speculative execution should be disabled in such cases.
Incorrect! Try again.
46In a High Availability (HA) YARN cluster, a 'split-brain' scenario occurs where two ResourceManagers (RM1 and RM2) both believe they are active. How does YARN's architectural design prevent cluster corruption in this specific scenario?
YARN Architecture Components
Hard
A.The ActiveStandbyElector uses ZooKeeper to maintain an active lock, and YARN implements fencing where the active RM's epoch number is validated by the ZooKeeper-based state store before any state changes are committed.
B.The NodeManagers utilize a Paxos protocol to vote on which RM to send heartbeats to, ostracizing the minority RM.
C.The ApplicationMasters implement exponential backoff and will fail over to a pre-configured third Resource Manager (Witness RM).
D.The Timeline Server acts as an arbiter and forcefully terminates the JVM of the RM with the oldest startup timestamp.
Correct Answer: The ActiveStandbyElector uses ZooKeeper to maintain an active lock, and YARN implements fencing where the active RM's epoch number is validated by the ZooKeeper-based state store before any state changes are committed.
Explanation:
YARN HA relies on ZooKeeper for leader election. To prevent split-brain (where a falsely active RM makes conflicting writes), YARN utilizes fencing via the State Store (like ZKRMStateStore), which checks the RM's epoch number. A 'zombie' RM with an older epoch is denied write access.
Incorrect! Try again.
47To achieve a total global ordering of output data in MapReduce, a developer decides to use the TotalOrderPartitioner. Which of the following prerequisites is strictly necessary for TotalOrderPartitioner to function efficiently without causing extreme data skew?
Combiners and Partitioners
Hard
A.The number of Reducers must be set strictly equal to the number of Map tasks.
B.A sampling phase must be executed prior to the job to determine partition boundaries, creating a partition file loaded into the Distributed Cache.
C.All keys must be mapped to exactly the same data type size (e.g., exactly 64-bit integers).
D.The input dataset must be pre-sorted in HDFS before the Map phase begins.
Correct Answer: A sampling phase must be executed prior to the job to determine partition boundaries, creating a partition file loaded into the Distributed Cache.
Explanation:
To balance the load across reducers while maintaining a total order, TotalOrderPartitioner relies on a partition file that defines the key boundaries for each reducer. This file is typically generated by a pre-job sampling process (e.g., InputSampler) and distributed to all nodes via the Distributed Cache.
Incorrect! Try again.
48If the ApplicationMaster (AM) container fails in YARN, what is the exact sequence of recovery initiated by the framework?
YARN Architecture Components
Hard
A.The NodeManager restarts the AM on the same node, maintaining all active task containers without interruption.
B.The job fails immediately, and the client application must resubmit the entire MapReduce job from scratch.
C.The ResourceManager launches a new AM; the new AM can recover the state of already completed tasks if application state recovery is enabled, avoiding full task re-execution.
D.The ResourceManager allocates a new container for the AM, which must then request all previously completed map outputs again, as all intermediate data is purged.
Correct Answer: The ResourceManager launches a new AM; the new AM can recover the state of already completed tasks if application state recovery is enabled, avoiding full task re-execution.
Explanation:
When an AM fails, the ResourceManager detects the missing heartbeats and allocates a new container for a new AM attempt (up to a configured max attempts). In modern YARN (MRv2), the new AM can recover the job state using the job history/state store, allowing it to preserve completed tasks and only resume pending ones.
Incorrect! Try again.
49During the Map phase, output records are buffered in memory before being spilled to disk. The spill threshold is defined by mapreduce.map.sort.spill.percent. What happens when the buffer usage reaches this threshold?
Shuffle and Sort Phase
Hard
A.The Map task pauses all record processing until the background thread completes spilling the buffer to HDFS.
B.A background thread begins sorting and spilling the contents to the local disk, while the map task continues writing to the remaining space in the buffer.
C.The NodeManager dynamically allocates more heap memory to the map container to prevent disk I/O bottlenecks.
D.The framework immediately preempts the map task, sending the partially completed buffer over the network directly to the Reducer.
Correct Answer: A background thread begins sorting and spilling the contents to the local disk, while the map task continues writing to the remaining space in the buffer.
Explanation:
When the memory buffer reaches the spill threshold (typically 80%), a background thread is spawned to sort and spill the data to the local disk. The map task is not blocked and continues to write to the remaining 20% of the buffer. If the buffer fills completely before the spill finishes, the map thread will block.
Incorrect! Try again.
50A MapReduce developer is implementing a secondary sort to sort values arriving at the reducer. They configure a custom WritableComparable as the Map output key and write a custom Partitioner. What third component MUST be heavily customized to ensure the Reducer receives all values for a given logical key in a single reduce() call?
MapReduce Execution Framework
Hard
A.A custom Combiner must be provided to pre-sort the values in the NodeManager's RAM.
B.The InputFormat class must be overridden to chunk data based on the logical key.
C.A custom GroupingComparator must be configured to group the composite keys based solely on their logical key portion.
D.The OutputCommitter must be configured to merge partial files based on the secondary sort keys.
Correct Answer: A custom GroupingComparator must be configured to group the composite keys based solely on their logical key portion.
Explanation:
In secondary sorting, the key emitted by the Mapper is a composite key (LogicalKey + SortKey). Since MapReduce groups by the entire key by default, a custom GroupingComparator (or RawComparator) is required to tell the framework to group records into a single reduce() call based only on the LogicalKey part.
Incorrect! Try again.
51In the YARN Capacity Scheduler, what is the effect of configuring yarn.scheduler.capacity.<queue-path>.maximum-capacity lower than 100% for a specific queue?
YARN Scheduling
Hard
A.It permanently throttles the CPU clock speed of all containers running in that queue to the specified percentage.
B.It restricts the queue from utilizing idle cluster resources beyond that percentage, preventing it from overtaking the entire cluster during elastic expansion.
C.It forces the queue to strictly pre-empt containers from other queues to guarantee its minimum capacity.
D.It dictates the maximum percentage of a single node's resources that a container in this queue can request.
Correct Answer: It restricts the queue from utilizing idle cluster resources beyond that percentage, preventing it from overtaking the entire cluster during elastic expansion.
Explanation:
Capacity Scheduler queues are elastic by default and can consume up to 100% of the cluster if other queues are empty. Setting maximum-capacity imposes a hard ceiling, preventing a queue from consuming more than that percentage of cluster resources, even if there are idle resources available.
Incorrect! Try again.
52Consider a scenario where you have two large datasets, A and B. You want to perform a Map-side join. Which of the following conditions is strictly necessary to implement a standard Map-side join efficiently using the framework's CompositeInputFormat?
MapReduce Execution Framework
Hard
A.Both datasets must be partitioned using the same logic, sorted by the join key, and have exactly the same number of partitions.
B.The MapReduce job must be configured to run with zero reducers and the Distributed Cache disabled.
C.Both datasets must be compressed using a splittable codec such as bzip2 or LZO.
D.Dataset B must be small enough to fit entirely into the RAM of a single NodeManager.
Correct Answer: Both datasets must be partitioned using the same logic, sorted by the join key, and have exactly the same number of partitions.
Explanation:
A standard Map-side join (often implemented via CompositeInputFormat) requires that both datasets are identically partitioned and sorted by the join key, allowing the Mapper to sequentially read parallel partitions and join them linearly without shuffling. (If one is small, a Broadcast/Distributed Cache join is used instead, which is technically a different pattern).
Incorrect! Try again.
53What happens in YARN if a NodeManager experiences a transient network partition and fails to send heartbeats to the ResourceManager for a duration exceeding the yarn.resourcemanager.nm.liveness-monitor.expiry-interval-ms?
YARN Architecture Components
Hard
A.The ApplicationMaster running on that node assumes ResourceManager duties until the network partition heals.
B.The ResourceManager immediately deletes all HDFS blocks residing on that node to prevent data corruption.
C.The NodeManager automatically shuts down its local operating system to fence the node from the cluster.
D.The ResourceManager marks the node as DEAD, considers all containers on it as failed, and notifies the respective ApplicationMasters to re-schedule those tasks.
Correct Answer: The ResourceManager marks the node as DEAD, considers all containers on it as failed, and notifies the respective ApplicationMasters to re-schedule those tasks.
Explanation:
If a NodeManager's heartbeats time out, the ResourceManager declares the node DEAD. Any containers (including AMs) running on that node are marked as killed/failed. The RM informs the relevant ApplicationMasters (if they are on other nodes) so they can request new containers to re-execute the lost tasks.
Incorrect! Try again.
54Uber mode (or Uber task optimization) in MapReduce v2 (YARN) is designed to optimize execution for small jobs. How does it alter the standard execution model?
MapReduce Execution Framework
Hard
A.It executes all map and reduce tasks sequentially within the ApplicationMaster's JVM, avoiding the overhead of requesting and launching separate containers.
B.It bypasses the ResourceManager completely and launches tasks directly using the HDFS DataNode daemon.
C.It utilizes GPU acceleration on the NodeManagers to execute map and reduce tasks in parallel threads.
D.It runs only Map tasks and forcefully streams their outputs back to the client submitting the job, skipping the Reduce phase.
Correct Answer: It executes all map and reduce tasks sequentially within the ApplicationMaster's JVM, avoiding the overhead of requesting and launching separate containers.
Explanation:
Uber mode is an optimization for very small jobs. Instead of the ApplicationMaster negotiating containers from the ResourceManager and suffering the latency of JVM startup for each task, the AM executes the map and reduce tasks sequentially within its own JVM.
Incorrect! Try again.
55In the context of MapReduce job commitment, what is the primary role of the OutputCommitter class's two-phase commit protocol?
MapReduce Execution Framework
Hard
A.To synchronize the ZooKeeper transaction logs before signaling the JobTracker of success.
B.To allow tasks to write output to a temporary location and gracefully promote it to the final destination only if the task, and subsequently the job, successfully completes.
C.To commit the final output data to an external RDBMS without holding long-lived database locks.
D.To securely sign the output data blocks in HDFS with Kerberos tokens.
Correct Answer: To allow tasks to write output to a temporary location and gracefully promote it to the final destination only if the task, and subsequently the job, successfully completes.
Explanation:
The OutputCommitter uses a two-phase commit: tasks write to a temporary working directory. When a task succeeds, it does a task commit (commitTask()) to promote its temporary output. When the entire job succeeds, a job commit (commitJob()) promotes the task outputs to the final job output directory, ensuring dirty reads or partial files from failed tasks are hidden.
Incorrect! Try again.
56During the Reduce phase, the execution fundamentally consists of three sub-phases: Copy (Shuffle), Sort (Merge), and Reduce. Which of the following accurately describes a critical operation during the Sort (Merge) phase?
Shuffle and Sort Phase
Hard
A.The framework performs a full external Quicksort on the raw key-value pairs fetched from the mappers.
B.The Reducer merges the already-sorted map output files fetched from various NodeManagers to maintain a single, totally ordered stream of keys.
C.The Reducer pushes the data back to HDFS temporarily because the memory buffer is cleared for the reduce() function.
D.The Reducer invokes the Partitioner again to ensure that keys were not routed to the wrong node due to network errors.
Correct Answer: The Reducer merges the already-sorted map output files fetched from various NodeManagers to maintain a single, totally ordered stream of keys.
Explanation:
The Sort phase in the Reducer is actually a Merge phase. Because the map outputs are already sorted by key during the map-side spill, the Reducer does not need to sort them from scratch. Instead, it performs a multi-pass merge (often using a priority queue) of these sorted segments to present an ordered stream of keys to the reduce() method.
Incorrect! Try again.
57YARN Federation addresses the scalability limits of a single ResourceManager cluster. How does YARN Federation manage a single application that requires more resources than a single sub-cluster can provide?
YARN Architecture Components
Hard
A.It allows the ApplicationMaster to request resources from ResourceManagers of multiple sub-clusters simultaneously using a global policy.
B.It cannot manage this; YARN Federation strictly requires an application to fit entirely within the capacity of a single sub-cluster.
C.It statically provisions resources by mapping the application's user ID strictly to one master sub-cluster.
D.It automatically splits the MapReduce code into multiple distinct JARs and submits them independently.
Correct Answer: It allows the ApplicationMaster to request resources from ResourceManagers of multiple sub-clusters simultaneously using a global policy.
Explanation:
YARN Federation scales the cluster by tying together multiple independent YARN sub-clusters. The ApplicationMaster can be aware of the federation environment and negotiate resources transparently across multiple sub-cluster ResourceManagers via the Federation State Store and router policies.
Incorrect! Try again.
58If you submit a MapReduce job with the configuration mapreduce.job.reduces=0, what happens to the output data?
MapReduce Execution Framework
Hard
A.The job fails with an IllegalStateException because every MapReduce job requires at least one reducer.
B.The mappers write their outputs to the local disk of the NodeManager, where it remains until a subsequent reduce job is manually started.
C.The framework bypasses the shuffle and sort phases, and the map tasks write their output directly to HDFS in the final output directory.
D.The mappers process the data, but no output is written to HDFS because the OutputFormat is exclusively bound to the Reducer.
Correct Answer: The framework bypasses the shuffle and sort phases, and the map tasks write their output directly to HDFS in the final output directory.
Explanation:
Setting the number of reducers to zero creates a Map-only job. The shuffle, sort, and reduce phases are skipped entirely. The Map tasks directly write their final outputs to HDFS using the configured OutputFormat.
Incorrect! Try again.
59In YARN, an ApplicationMaster operates within a container and must authenticate with the ResourceManager to request further resources. Which security mechanism does the AM use to securely communicate with the RM in a Kerberized cluster?
YARN Architecture Components
Hard
A.It uses an X.509 client certificate hardcoded into the NodeManager truststore.
B.It uses the client's original Kerberos Ticket Granting Ticket (TGT), forwarded over RPC.
C.It uses a short-lived AMRMToken (ApplicationMaster-ResourceManager Token) issued by the RM during AM launch.
D.It generates a public/private keypair dynamically and registers the public key in ZooKeeper.
Correct Answer: It uses a short-lived AMRMToken (ApplicationMaster-ResourceManager Token) issued by the RM during AM launch.
Explanation:
In a secure YARN cluster, the AM does not use Kerberos directly to talk to the RM. Instead, when the RM launches the AM, it provides a specialized Hadoop delegation token called the AMRMToken. The AM uses this token for authentication on subsequent RPC calls to negotiate resources, and it is periodically rolled/renewed.
Incorrect! Try again.
60A developer writes a custom Partitioner for a MapReduce job to route records based on an 'AccountID' string. The logic uses (accountID.hashCode() % numReducers). Under what circumstance will this custom partitioner cause a severe job failure?
Combiners and Partitioners
Hard
A.If there is a massive data skew where one AccountID has 90% of the data.
B.If accountID contains special characters that cannot be hashed.
C.If numReducers is set to 1.
D.If accountID.hashCode() evaluates to Integer.MIN_VALUE.
Correct Answer: If accountID.hashCode() evaluates to Integer.MIN_VALUE.
Explanation:
In Java, Math.abs(Integer.MIN_VALUE) returns a negative number (Integer.MIN_VALUE itself, due to two's complement overflow). If the hash code happens to be Integer.MIN_VALUE, modulo numReducers will yield a negative partition index, which causes an ArrayIndexOutOfBoundsException or IllegalStateException in the framework. It must be masked using (accountID.hashCode() & Integer.MAX_VALUE) % numReducers.
Incorrect! Try again.
Did this save you a night before the exam?
LPU Notes is free, and it stays free. Ads cover part of the server bill.
The rest comes out of a student's own pocket: the domain, the storage,
and keeping the site up through the weeks everyone needs it at once.
The payment button didn't load. An ad blocker or a filtered network is the usual reason.
to try again.
Nothing here is ever locked, and nothing unlocks. Chip in only if it was worth it.
What it pays for →