80 multiple-choice questions provides an in-depth exploration of core Big Data technologies in the Hadoop ecosystem. Covering MapReduce for parallel data processing, HDFS for scalable distributed storage, and YARN for efficient resource management, these MCQs are designed to test and reinforce foundational knowledge for aspiring data engineers and analysts.
80 Big Data: MapReduce, HDFS, and YARN - MCQs
✅ Correct Answer: a) Hadoop Distributed File System
📝 Explanation:
HDFS is the primary storage system used by Hadoop for storing large datasets across multiple machines in a distributed manner.
✅ Correct Answer: b) 128 MB
📝 Explanation:
The default block size in HDFS is 128 MB, which allows for efficient storage and processing of large files by dividing them into manageable chunks.
✅ Correct Answer: b) NameNode
📝 Explanation:
The NameNode maintains the file system namespace and metadata, controlling access to files and directories in HDFS.
✅ Correct Answer: a) NameNode
📝 Explanation:
DataNodes perform read/write operations on blocks as directed by the NameNode and report their status periodically.
✅ Correct Answer: b) It periodically checkpoints the fsimage and edits log files
📝 Explanation:
The Secondary NameNode merges the fsimage and edit logs to create a new checkpoint, reducing recovery time for the NameNode.
✅ Correct Answer: a) Low-cost commodity hardware
📝 Explanation:
HDFS is built to tolerate frequent hardware failures and operate on inexpensive, commodity hardware for scalability.
✅ Correct Answer: c) 3
📝 Explanation:
HDFS maintains three replicas of each block by default to ensure high availability and fault tolerance.
✅ Correct Answer: a) To optimize data locality and fault tolerance
📝 Explanation:
Rack Awareness places replicas across different racks to minimize network traffic and improve resilience against rack failures.
✅ Correct Answer: a) hdfs dfs -mkdir
📝 Explanation:
The 'hdfs dfs -mkdir' command creates directories in the HDFS namespace, similar to the Unix mkdir command.
✅ Correct Answer: c) Manual recovery from edit logs is required, which can be time-consuming
📝 Explanation:
Without checkpoints, the NameNode recovery involves replaying the entire edit log, which can delay cluster availability.
✅ Correct Answer: a) High-throughput, low-latency access
📝 Explanation:
HDFS is optimized for batch processing with high-throughput streaming access to large files, not low-latency random access.
✅ Correct Answer: b) Unlimited (limited by available storage)
📝 Explanation:
HDFS can theoretically handle petabyte-scale files, constrained only by the total cluster storage capacity.
✅ Correct Answer: a) POSIX-like permissions
📝 Explanation:
HDFS implements a permission model similar to POSIX, with owner, group, and others categories for read/write/execute.
✅ Correct Answer: c) DataNode
📝 Explanation:
DataNodes store HDFS blocks as regular files on their local file systems and handle I/O operations for them.
✅ Correct Answer: a) A persistent checkpoint of the file system metadata
📝 Explanation:
The fsimage is a serialized representation of the NameNode's in-memory namespace and block data.
✅ Correct Answer: a) Separate namespaces
📝 Explanation:
HDFS Federation enables horizontal scaling by allowing multiple independent NameNodes, each managing its own namespace.
✅ Correct Answer: a) hdfs dfs -ls
📝 Explanation:
'hdfs dfs -ls' displays the list of files and directories in the specified HDFS path.
✅ Correct Answer: a) Recording namespace modifications between checkpoints
📝 Explanation:
The edit log captures every change to the file system metadata since the last fsimage snapshot.
✅ Correct Answer: a) Write-once, read-many-times
📝 Explanation:
HDFS appends data to files and supports multiple reads, making it ideal for batch processing workloads.
✅ Correct Answer: a) To distribute data evenly across DataNodes
📝 Explanation:
The HDFS Balancer tool rebalances data blocks to ensure even distribution and prevent hotspots.
✅ Correct Answer: a) Block replication
📝 Explanation:
Replication ensures multiple copies of data blocks, allowing the system to recover from node failures seamlessly.
✅ Correct Answer: a) 50070
📝 Explanation:
The NameNode's web interface runs on port 50070 for monitoring cluster status.
✅ Correct Answer: a) Yes, they occupy the full block space
📝 Explanation:
Small files in HDFS still reserve a full block, which can lead to inefficiency in name space usage.
✅ Correct Answer: a) To reduce storage overhead compared to replication
📝 Explanation:
Erasure Coding encodes data into parity blocks, providing fault tolerance with less storage than triple replication.
✅ Correct Answer: a) hdfs dfs -put
📝 Explanation:
'hdfs dfs -put' uploads files from the local file system to HDFS.
✅ Correct Answer: c) Both
📝 Explanation:
HDFS allows configuration of both symmetric (same replicas everywhere) and asymmetric (different counts) replication.
✅ Correct Answer: a) 3 seconds
📝 Explanation:
DataNodes send heartbeats every 3 seconds to indicate liveness and report block reports every 6 seconds.
✅ Correct Answer: a) Periodic verification of block integrity
📝 Explanation:
Block scanning checks the checksums of stored blocks to detect corruption.
✅ Correct Answer: a) dfs.replication
📝 Explanation:
The 'dfs.replication' property in hdfs-site.xml defines the default number of replicas for data blocks.
✅ Correct Answer: a) Active and Standby NameNodes with JournalNodes
📝 Explanation:
HA setup includes shared storage via JournalNodes for seamless failover between NameNodes.
✅ Correct Answer: a) To check file system health and find corrupt blocks
📝 Explanation:
'hdfs fsck' performs a file system check, reporting under-replicated, missing, or corrupt blocks.
✅ Correct Answer: a) TCP/IP
📝 Explanation:
HDFS uses TCP sockets for reliable data streaming between clients and DataNodes.
✅ Correct Answer: a) A programming model for parallel processing
📝 Explanation:
MapReduce is a framework that allows distributed processing of large data sets on clusters using Map and Reduce functions.
✅ Correct Answer: a) To process input data and produce key-value pairs
📝 Explanation:
The Map function takes input data, processes it in parallel, and emits intermediate key-value pairs.
✅ Correct Answer: a) Final aggregated results
📝 Explanation:
The Reduce function receives grouped key-value pairs and produces the final output for the job.
✅ Correct Answer: a) A logical division of input data for parallel processing
📝 Explanation:
InputSplits define how input data is divided among Map tasks for distributed execution.
✅ Correct Answer: a) JobTracker
📝 Explanation:
The JobTracker oversees the entire MapReduce job lifecycle, assigning tasks to TaskTrackers.
✅ Correct Answer: a) To perform local aggregation before shuffle and sort
📝 Explanation:
Combiners reduce the amount of data transferred during the shuffle phase by aggregating locally on mapper nodes.
✅ Correct Answer: a) Map and Reduce
📝 Explanation:
Shuffle and sort groups and sorts the intermediate outputs from Mappers before sending them to Reducers.
✅ Correct Answer: a) TextInputFormat
📝 Explanation:
TextInputFormat treats each line of input as a key-value pair, with offset as key and line as value.
✅ Correct Answer: a) Task retry and speculative execution
📝 Explanation:
Failed tasks are retried, and speculative execution runs duplicates of slow tasks to ensure timely completion.
✅ Correct Answer: a) To decide which Reducer receives which key
📝 Explanation:
The Partitioner determines the mapping of intermediate keys to Reducers based on a hash function.
✅ Correct Answer: a) Straggler tasks that slow down the job
📝 Explanation:
Speculative execution launches duplicate tasks for slow-running ones, using the first to complete.
✅ Correct Answer: a) Mapper
📝 Explanation:
Developers extend the Mapper class and override the map() method to implement custom logic.
✅ Correct Answer: a) To control the output of Reduce tasks
📝 Explanation:
OutputFormat defines how and where the final output from Reducers is written, e.g., to HDFS.
✅ Correct Answer: a) In parallel across a cluster
📝 Explanation:
MapReduce enables parallel processing by distributing tasks across multiple nodes in a cluster.
✅ Correct Answer: a) To track job progress and custom metrics
📝 Explanation:
Counters collect statistics during job execution, such as bytes processed or custom application metrics.
✅ Correct Answer: a) Slave nodes
📝 Explanation:
TaskTrackers execute Map and Reduce tasks on worker (slave) nodes under JobTracker supervision.
✅ Correct Answer: a) Custom Mappers and Reducers
📝 Explanation:
Joins are achieved by emitting join keys in Map and aggregating matching records in Reduce.
✅ Correct Answer: a) Java, Python, C++ via Hadoop Streaming
📝 Explanation:
Hadoop Streaming allows MapReduce jobs in non-Java languages using standard input/output.
✅ Correct Answer: a) To cache small files on all nodes for efficient access
📝 Explanation:
DistributedCache distributes read-only files like lookup tables to all nodes before job start.
✅ Correct Answer: a) Processing data on the node where it is stored
📝 Explanation:
Data locality minimizes network I/O by scheduling tasks on nodes holding the data.
✅ Correct Answer: a) 1
📝 Explanation:
By default, MapReduce sets one Reducer unless specified otherwise via job configuration.
✅ Correct Answer: a) Map-only job
📝 Explanation:
Jobs can be configured with zero reducers to perform only mapping and write intermediate output.
✅ Correct Answer: a) Yet Another Resource Negotiator
📝 Explanation:
YARN is Hadoop's resource management framework that decouples resource allocation from job execution.
✅ Correct Answer: a) Global resource allocation and job scheduling
📝 Explanation:
The ResourceManager arbitrates resources across the cluster and schedules applications.
✅ Correct Answer: a) Per-node agents that manage containers
📝 Explanation:
NodeManagers monitor resources on their host and launch containers as directed by the ResourceManager.
✅ Correct Answer: a) Per-application manager for negotiating resources and coordinating tasks
📝 Explanation:
Each application gets its own ApplicationMaster to handle resource requests and task execution.
✅ Correct Answer: a) An abstract unit of allocation including CPU, memory, etc.
📝 Explanation:
Containers represent allocated resources (CPU, memory, disk) for running application components.
✅ Correct Answer: a) Separating resource management from job-specific logic
📝 Explanation:
YARN generalizes the architecture to support multiple processing engines beyond MapReduce.
✅ Correct Answer: a) Capacity Scheduler
📝 Explanation:
The Capacity Scheduler allows queues with guaranteed shares and supports hierarchical queues.
✅ Correct Answer: a) ApplicationMaster requesting specific resources from ResourceManager
📝 Explanation:
ResourceRequest specifies locality preferences, priority, and resource requirements for allocation.
✅ Correct Answer: a) Capacity Scheduler
📝 Explanation:
YARN defaults to the Capacity Scheduler for multi-tenancy support in production environments.
✅ Correct Answer: a) Spark, Tez, and others
📝 Explanation:
YARN's generic interface allows diverse frameworks like Spark and Tez to run on Hadoop clusters.
✅ Correct Answer: a) ResourceManager
📝 Explanation:
NodeManagers send periodic heartbeats to the ResourceManager to report available resources.
✅ Correct Answer: a) To store and retrieve application history information
📝 Explanation:
The Timeline Server collects generic application history for monitoring and debugging.
✅ Correct Answer: a) Multiple ResourceManagers for scaling
📝 Explanation:
YARN Federation enables sub-clusters with separate ResourceManagers for large-scale deployments.
✅ Correct Answer: a) The ResourceManager restarts it
📝 Explanation:
YARN supports ApplicationMaster recovery by restarting it with the same state.
✅ Correct Answer: b) All components
📝 Explanation:
YARN uses container launch tokens and other security tokens for authentication across components.
✅ Correct Answer: a) Low-priority containers that use idle resources
📝 Explanation:
Opportunistic mode allows flexible resource usage for bursty workloads without guarantees.
✅ Correct Answer: a) Hierarchical queues with fair share allocation
📝 Explanation:
It dynamically allocates resources fairly among queues and jobs within them.
✅ Correct Answer: a) Submitting applications and managing cluster
📝 Explanation:
The 'yarn' CLI submits jobs, lists applications, and kills running ones.
✅ Correct Answer: a) Configurable, default 1 GB
📝 Explanation:
yarn.scheduler.minimum-allocation-mb defaults to 1024 MB for container memory.
✅ Correct Answer: a) CPU vcores and memory MB
📝 Explanation:
YARN allocates resources in terms of virtual CPU cores and memory in megabytes.
✅ Correct Answer: a) To allocate resources to applications based on policies
📝 Explanation:
The Scheduler component of ResourceManager decides resource grants to ApplicationMasters.
✅ Correct Answer: a) Flow-level aggregation of application events
📝 Explanation:
ATS v2 enables entity-level history storage for better querying and visualization.
✅ Correct Answer: a) Zero-downtime version updates
📝 Explanation:
Rolling upgrades allow updating nodes incrementally without stopping the cluster.
✅ Correct Answer: a) 8088
📝 Explanation:
The ResourceManager's web interface is accessible on port 8088 for cluster monitoring.
✅ Correct Answer: a) Node, rack, and any
📝 Explanation:
Applications can request resources with preferences for specific nodes, racks, or anywhere.
✅ Correct Answer: a) To tag nodes for access control and scheduling
📝 Explanation:
Node labels allow partitioning the cluster logically for different workloads or users.
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