1Which type of architecture is traditionally used by Hadoop?
Hadoop Architecture
Easy
A.Peer-to-peer architecture
B.Master-slave architecture
C.Client-only architecture
D.Single-tier architecture
Correct Answer: Master-slave architecture
Explanation:
Traditional Hadoop uses master nodes to coordinate work and slave nodes to store data and execute tasks.
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2Which two components form the core of classic Hadoop architecture?
Hadoop Architecture
Easy
A.HTTP and FTP
B.HTML and XML
C.SQL and NoSQL
D.HDFS and MapReduce
Correct Answer: HDFS and MapReduce
Explanation:
HDFS provides distributed storage, while MapReduce provides distributed data processing.
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3What does HDFS stand for?
Hadoop Storage: HDFS
Easy
A.Hierarchical Data Format Service
B.Hadoop Distributed File System
C.Hadoop Database File Service
D.High-Density File System
Correct Answer: Hadoop Distributed File System
Explanation:
HDFS stands for Hadoop Distributed File System and stores files across multiple machines.
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4How does HDFS normally store a large file?
Hadoop Storage: HDFS
Easy
A.As blocks across multiple nodes
B.As one local memory object
C.As rows in one database
D.As messages in one queue
Correct Answer: As blocks across multiple nodes
Explanation:
HDFS divides a large file into blocks and distributes those blocks across DataNodes.
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5What is the main purpose of block replication in HDFS?
Hadoop Storage: HDFS
Easy
A.To rename stored files
B.To improve fault tolerance
C.To create user accounts
D.To compress every record
Correct Answer: To improve fault tolerance
Explanation:
Replication keeps copies of data blocks on different DataNodes so data remains available after a node failure.
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6Which two main phases are used in the MapReduce programming model?
Hadoop MapReduce paradigm
Easy
A.Map and Reduce
B.Open and Close
C.Read and Write
D.Start and Stop
Correct Answer: Map and Reduce
Explanation:
The Map phase processes input data, and the Reduce phase combines or summarizes intermediate results.
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7What is the primary role of the Map phase?
Hadoop MapReduce paradigm
Easy
A.Produce intermediate key-value pairs
B.Manage DataNode heartbeats
C.Create operating system users
D.Store HDFS block metadata
Correct Answer: Produce intermediate key-value pairs
Explanation:
A mapper reads input records and produces intermediate key-value pairs for later processing.
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8What is the primary role of the Reduce phase?
Hadoop MapReduce paradigm
Easy
A.Install Hadoop on each machine
B.Register nodes in the cluster
C.Divide files into HDFS blocks
D.Combine values associated with keys
Correct Answer: Combine values associated with keys
Explanation:
A reducer receives grouped values for each key and combines them to produce final output.
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9In MapReduce, data is commonly processed in which form?
MapReduce Terminology
Easy
A.Folder-window pairs
B.Row-column screens
C.Key-value pairs
D.Audio-video pairs
Correct Answer: Key-value pairs
Explanation:
MapReduce represents input, intermediate, and output data using key-value pairs.
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10What is the process of transferring mapper output to reducers called?
MapReduce Terminology
Easy
A.Formatting
B.Checkpointing
C.Shuffle
D.Replication
Correct Answer: Shuffle
Explanation:
The shuffle process transfers and groups intermediate mapper output for the reducers.
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11What does a mapper produce before the Reduce phase begins?
MapReduce Terminology
Easy
A.Cluster configuration
B.DataNode heartbeat
C.Intermediate output
D.File system metadata
Correct Answer: Intermediate output
Explanation:
The mapper generates intermediate output that is shuffled, sorted, and sent to reducers.
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12What is the main responsibility of the HDFS NameNode?
Hadoop NameNode
Easy
A.Display command output
B.Execute every map task
C.Store all user records
D.Manage file system metadata
Correct Answer: Manage file system metadata
Explanation:
The NameNode manages metadata such as file names, directories, permissions, and block locations.
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13Which information is maintained by the NameNode?
Hadoop NameNode
Easy
A.Locations of HDFS blocks
B.Passwords of operating systems
C.Contents of every data block
D.Results of every shell command
Correct Answer: Locations of HDFS blocks
Explanation:
The NameNode tracks which DataNodes contain the blocks belonging to each HDFS file.
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14What is the main responsibility of an HDFS DataNode?
Hadoop DataNode
Easy
A.Schedule all cluster jobs
B.Maintain the file namespace
C.Compile MapReduce programs
D.Store actual data blocks
Correct Answer: Store actual data blocks
Explanation:
DataNodes store the actual HDFS blocks and handle client requests to read or write them.
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15What does a DataNode regularly send to the NameNode to show that it is active?
Hadoop DataNode
Easy
A.User password
B.SQL query
C.Java archive
D.Heartbeat
Correct Answer: Heartbeat
Explanation:
A DataNode sends heartbeat messages so the NameNode can monitor its availability.
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16In classic Hadoop MapReduce, what is the main role of the JobTracker?
Hadoop JobTracker
Easy
A.Manage file permissions only
B.Edit input text files
C.Coordinate and schedule jobs
D.Store HDFS data blocks
Correct Answer: Coordinate and schedule jobs
Explanation:
The JobTracker accepts jobs, schedules tasks, and monitors their execution in classic Hadoop MapReduce.
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17Which component submits progress information to the JobTracker in classic Hadoop?
Hadoop JobTracker
Easy
A.Secondary NameNode
B.NameNode
C.TaskTracker
D.HDFS client
Correct Answer: TaskTracker
Explanation:
TaskTrackers report task progress and status to the JobTracker.
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18What does a TaskTracker do in classic Hadoop MapReduce?
Hadoop TaskTracker
Easy
A.Stores only file metadata
B.Runs map and reduce tasks
C.Creates Hadoop user accounts
D.Maintains the HDFS namespace
Correct Answer: Runs map and reduce tasks
Explanation:
A TaskTracker executes map and reduce tasks assigned by the JobTracker on a worker node.
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19Which command pattern correctly runs a Hadoop word-count JAR?
Word count on command line
Easy
A.hdfs run app.jar WordCount input output
B.hadoop start app.jar WordCount input output
C.java hdfs app.jar WordCount input output
D.hadoop jar app.jar WordCount input output
Correct Answer: hadoop jar app.jar WordCount input output
Explanation:
The hadoop jar command runs a Hadoop application, followed by its class name and arguments.
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20Which command can display the first reducer output file from an HDFS word-count result directory named output?
Word count on command line
Easy
A.hdfs dfs -cat output/part-r-00000
B.hdfs dfs -put output/part-r-00000
C.hdfs dfs -rm output/part-r-00000
D.hdfs dfs -mkdir output/part-r-00000
Correct Answer: hdfs dfs -cat output/part-r-00000
Explanation:
The -cat command displays an HDFS file, and part-r-00000 is a common reducer output file name.
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21A Hadoop cluster must process a large dataset while minimizing network traffic. Which architectural decision best supports this goal?
Hadoop Architecture
Medium
A.Send every intermediate record to the NameNode
B.Move all blocks to the node running the client
C.Move computation to nodes storing the required blocks
D.Store each input file on a single DataNode
Correct Answer: Move computation to nodes storing the required blocks
Explanation:
Hadoop uses data locality by scheduling computation near HDFS blocks, reducing network transfers.
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22A client submits a MapReduce job that reads a file from HDFS. Which sequence best describes the main interaction?
Hadoop Architecture
Medium
A.The client gets block locations from the NameNode and tasks read from DataNodes
B.The client gets file data from the JobTracker and tasks write to the NameNode
C.The client gets block locations from DataNodes and tasks read from the NameNode
D.The client gets file data from the NameNode and tasks write to DataNodes
Correct Answer: The client gets block locations from the NameNode and tasks read from DataNodes
Explanation:
The NameNode supplies metadata and block locations, while the actual file blocks are read from DataNodes.
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23An HDFS file is divided into 5 blocks and stored with a replication factor of 3. Assuming every block is fully replicated, how many block replicas are stored in total?
Hadoop Storage: HDFS
Medium
A.20 block replicas
B.15 block replicas
C.10 block replicas
D.8 block replicas
Correct Answer: 15 block replicas
Explanation:
The total is block replicas.
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24A 300 MB file is stored in HDFS using a block size of 128 MB. How many HDFS blocks are required?
Hadoop Storage: HDFS
Medium
A.2 blocks
B.4 blocks
C.3 blocks
D.5 blocks
Correct Answer: 3 blocks
Explanation:
Two blocks hold 256 MB, and the remaining 44 MB requires a third block.
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25A DataNode containing one replica of an HDFS block becomes unavailable. Other replicas remain accessible. What is the expected immediate result?
Hadoop Storage: HDFS
Medium
A.The file becomes unavailable until the node returns
B.The client must upload the entire file again
C.The NameNode reconstructs the missing bytes itself
D.The file remains readable from another replica
Correct Answer: The file remains readable from another replica
Explanation:
HDFS clients can read an available replica, while the NameNode arranges re-replication if needed.
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26A MapReduce program calculates total sales for each product. The mapper emits (product, saleAmount). What should the reducer do?
Hadoop MapReduce paradigm
Medium
A.Sum all sale amounts received for each product
B.Store every input record without grouping
C.Replicate each sale amount across all products
D.Sort product names by their character length
Correct Answer: Sum all sale amounts received for each product
Explanation:
The shuffle groups values by product, allowing the reducer to calculate each product's total.
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27A job has many repeated mapper keys and performs an associative sum. Which feature can reduce the amount of data transferred during shuffle?
Hadoop MapReduce paradigm
Medium
A.An input format that creates smaller records
B.A partitioner that sends all keys to one node
C.A reducer that duplicates intermediate values
D.A combiner that performs local partial sums
Correct Answer: A combiner that performs local partial sums
Explanation:
A combiner can aggregate repeated keys on each mapper before intermediate data crosses the network.
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28A MapReduce job uses four reducers. What determines which reducer receives a particular intermediate key?
Hadoop MapReduce paradigm
Medium
A.The TaskTracker applied to the reducer count
B.The NameNode applied to the source file path
C.The partitioner applied to the intermediate key
D.The InputFormat applied to the final output
Correct Answer: The partitioner applied to the intermediate key
Explanation:
The partitioner maps each intermediate key to one reducer, ensuring identical keys reach the same reducer.
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29In a word-count job, a mapper emits (cloud, 1) three times and (data, 1) twice. After shuffle and sort, what input is logically presented to reducers?
MapReduce Terminology
Medium
A.(cloud, 3) and (data, 2) in every case
B.(cloud, [1,1,1]) and (data, [1,1])
C.(1, [cloud,cloud,cloud,data,data])
D.([cloud,data], [1,1,1,1,1])
Correct Answer: (cloud, [1,1,1]) and (data, [1,1])
Explanation:
Shuffle and sort group all intermediate values associated with the same key before reduction.
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30Which MapReduce component defines how input data is divided into logical splits and converted into key-value records?
MapReduce Terminology
Medium
A.Combiner
B.Partitioner
C.InputFormat
D.OutputFormat
Correct Answer: InputFormat
Explanation:
InputFormat creates input splits and supplies a RecordReader that converts input data into mapper records.
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31A mapper task fails after producing intermediate output, but before reducers finish fetching it. What normally happens?
MapReduce Terminology
Medium
A.The input split is permanently removed from HDFS
B.The reducer calculates the missing mapper output
C.The NameNode creates the intermediate records
D.The mapper task is rerun to regenerate its output
Correct Answer: The mapper task is rerun to regenerate its output
Explanation:
Mapper output is temporary, so Hadoop reruns the failed map task to recreate unavailable intermediate data.
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32A user renames an HDFS file without changing its contents. Which node primarily updates the information for this operation?
Hadoop NameNode
Medium
A.NameNode
B.DataNode
C.TaskTracker
D.JobTracker
Correct Answer: NameNode
Explanation:
Renaming changes filesystem namespace metadata, which is managed by the NameNode.
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33The NameNode stops receiving heartbeats from a DataNode for an extended period. What action is most appropriate?
Hadoop NameNode
Medium
A.Delete all files that had blocks on the DataNode
B.Transfer filesystem metadata to the failed DataNode
C.Mark the DataNode unavailable and schedule missing replicas
D.Run reducer tasks to reconstruct the namespace
Correct Answer: Mark the DataNode unavailable and schedule missing replicas
Explanation:
The NameNode treats the DataNode as unavailable and initiates replication for under-replicated blocks.
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34During an HDFS read, a client discovers that the nearest replica is unavailable. What should the client normally do?
Hadoop DataNode
Medium
A.Request the block contents directly from the NameNode
B.Request the block from another listed DataNode
C.Wait until the nearest DataNode becomes available
D.Ask the JobTracker to recreate the complete file
Correct Answer: Request the block from another listed DataNode
Explanation:
The client can use another replica location provided by the NameNode when one DataNode is unavailable.
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35Which message allows a DataNode to inform the NameNode about the HDFS blocks it currently stores?
Hadoop DataNode
Medium
A.Input split
B.Reduce output
C.Job submission
D.Block report
Correct Answer: Block report
Explanation:
A block report lists the blocks stored by a DataNode and helps the NameNode maintain block-location metadata.
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36In Hadoop MapReduce version 1, a TaskTracker repeatedly fails tasks assigned to it. Which JobTracker response best maintains job progress?
Hadoop JobTracker
Medium
A.Combine every input split into one reducer task
B.Move all HDFS metadata to the failing TaskTracker
C.Reschedule failed tasks on other available TaskTrackers
D.Disable replication for the job's input blocks
Correct Answer: Reschedule failed tasks on other available TaskTrackers
Explanation:
The JobTracker monitors task execution and reschedules failed tasks on other workers when possible.
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37The JobTracker has two available TaskTrackers, but only one stores a local replica of the next map task's input block. Which placement is generally preferred?
Hadoop JobTracker
Medium
A.Assign the map task to the TaskTracker with the local replica
B.Assign the map task directly to the NameNode process
C.Assign the map task to the TaskTracker without the local replica
D.Delay the map task until every node stores the block
Correct Answer: Assign the map task to the TaskTracker with the local replica
Explanation:
A data-local assignment reduces network traffic because the mapper reads its input from local storage.
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38In MapReduce version 1, how does a TaskTracker normally obtain new work from the JobTracker?
Hadoop TaskTracker
Medium
A.It reads pending task definitions from an HDFS data block
B.It sends a heartbeat that reports status and available slots
C.It waits for a DataNode to push a complete job archive
D.It requests namespace changes from the secondary NameNode
Correct Answer: It sends a heartbeat that reports status and available slots
Explanation:
TaskTracker heartbeats report health and capacity, enabling the JobTracker to assign tasks.
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39A word-count job has written results to /output/wc in HDFS. Which command displays all reducer output part files on the terminal?
Word count on command line
Medium
A.hadoop fs -rm /output/wc/part-*
B.hadoop fs -mkdir /output/wc/part-*
C.hadoop fs -put /output/wc/part-*
D.hadoop fs -cat /output/wc/part-*
Correct Answer: hadoop fs -cat /output/wc/part-*
Explanation:
hadoop fs -cat prints HDFS file contents, and part-* selects the reducer output files.
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40A word-count command fails because its intended HDFS output directory already exists. Which action usually resolves the issue before rerunning the job?
Word count on command line
Medium
A.Upload the old output with hadoop fs -put /output/wc
B.Copy the old output with hadoop fs -get /output/wc
C.Remove the old output with hadoop fs -rm -r /output/wc
D.Display the old output with hadoop fs -cat /output/wc
Correct Answer: Remove the old output with hadoop fs -rm -r /output/wc
Explanation:
MapReduce normally requires the output path not to exist, so the old directory must be removed or a new path used.
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41In a classic Hadoop 1.x cluster, a worker remains reachable and continues sending TaskTracker heartbeats, but its DataNode process has stopped. Which system behavior is most accurate?
Hadoop Architecture
Hard
A.The worker becomes unavailable to both subsystems only after the JobTracker enters safe mode.
B.The TaskTracker remains active, and the NameNode reconstructs the stopped DataNode process remotely.
C.The JobTracker may still schedule tasks there, while HDFS eventually marks its block replicas unavailable.
D.The NameNode immediately terminates the TaskTracker because both daemons share one heartbeat.
Correct Answer: The JobTracker may still schedule tasks there, while HDFS eventually marks its block replicas unavailable.
Explanation:
DataNode and TaskTracker are independent daemons with separate masters and heartbeats. A live TaskTracker can still receive tasks even after the NameNode detects that its DataNode has failed.
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42A 300 MiB HDFS file uses a 128 MiB block size and replication factor 3. Ignoring checksums and metadata, which statement is correct?
Hadoop Storage: HDFS
Hard
A.It creates 9 logical blocks and consumes 1152 MiB across all replicas.
B.It creates 3 logical blocks and consumes 1152 MiB across all replicas.
C.It creates 3 logical blocks and consumes 900 MiB across all replicas.
D.It creates 9 logical blocks and consumes 900 MiB across all replicas.
Correct Answer: It creates 3 logical blocks and consumes 900 MiB across all replicas.
Explanation:
The file has logical blocks. HDFS does not pad the final block to 128 MiB, so replicated payload storage is MiB.
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43An HDFS file has replication factor 3, and all DataNodes are healthy. The NameNode reports that all three replicas of one block are on the same rack. What is the most important consequence?
Hadoop Storage: HDFS
Hard
A.The block tolerates two DataNode failures but not loss of that rack.
B.The block tolerates loss of that rack because three replicas exist.
C.The block is automatically converted into three independent namespace entries.
D.The block becomes unreadable until the SecondaryNameNode checkpoints it.
Correct Answer: The block tolerates two DataNode failures but not loss of that rack.
Explanation:
Replication protects against individual node failures only when replicas survive elsewhere. Co-locating every replica on one rack leaves the block vulnerable to a rack-level outage.
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44A MapReduce job reads 10,000 files of 1 KiB each from HDFS. The total payload is small, yet startup and metadata overhead are severe. Which explanation best identifies the structural cause?
Hadoop Storage: HDFS
Hard
A.HDFS encrypts each small file with a distinct key stored in the JobTracker namespace.
B.Every 1 KiB file physically occupies a complete replicated block on each DataNode.
C.Every file forces the NameNode to transmit its full contents before mapping can begin.
D.Each file and block adds NameNode metadata and may produce a separate input split.
Correct Answer: Each file and block adds NameNode metadata and may produce a separate input split.
Explanation:
The small-files problem is dominated by per-file and per-block namespace metadata plus excessive map-task setup. HDFS does not necessarily allocate a full block's physical capacity to each file.
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45Which statement correctly distinguishes a SecondaryNameNode checkpoint from NameNode failover in classic Hadoop 1.x?
Hadoop NameNode
Hard
A.A checkpoint merges fsimage with edits but does not automatically assume the active NameNode role.
B.A checkpoint accepts client writes while the primary NameNode serves only read operations.
C.A checkpoint continuously mirrors every DataNode block and immediately becomes the active NameNode.
D.A checkpoint stores application data blocks and reconstructs namespace metadata only after a failure.
Correct Answer: A checkpoint merges fsimage with edits but does not automatically assume the active NameNode role.
Explanation:
The SecondaryNameNode periodically creates checkpoints by merging namespace image and edit logs. It is not a hot standby and does not provide automatic failover.
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46After a NameNode restart, HDFS enters safe mode because the configured threshold of reported blocks has not yet been reached. Which action is expected during this period?
Hadoop NameNode
Hard
A.The JobTracker assumes block management duties and creates replicas for every missing report.
B.DataNodes stop sending block reports so that the namespace image remains unchanged.
C.Clients can generally read reported blocks, but namespace mutations and replication scheduling are restricted.
D.Clients can create files, but existing files remain unreadable until every replica reports.
Correct Answer: Clients can generally read reported blocks, but namespace mutations and replication scheduling are restricted.
Explanation:
Safe mode is primarily a read-only startup state. The NameNode waits for sufficient block reports before enabling normal namespace changes and replication management.
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47A client detects a checksum mismatch while reading one replica of an HDFS block, while another replica is healthy. What is the expected recovery path?
Hadoop DataNode
Hard
A.The DataNode asks the SecondaryNameNode to reconstruct the block from the namespace edit log.
B.The NameNode returns the corrupt bytes because checksum verification occurs only during block creation.
C.The client repairs the bytes locally and writes the corrected range directly into the corrupt replica.
D.The client reads another replica and reports the corrupt replica so it can be invalidated and replaced.
Correct Answer: The client reads another replica and reports the corrupt replica so it can be invalidated and replaced.
Explanation:
HDFS clients verify block checksums and can retry another replica. Corruption is reported so the NameNode can arrange removal and re-replication from a valid copy.
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48An administrator wants to retire a healthy DataNode without reducing the durable replication of its blocks. Which procedure has the intended semantics?
Hadoop DataNode
Hard
A.Stop it immediately and manually remove its block files from the local data directories.
B.Add it to the exclude list, refresh nodes, and wait for decommissioning to complete.
C.Delete its storage ID from fsimage and restart every remaining DataNode in the cluster.
D.Reduce every file's replication factor, stop the node, and restore the factors afterward.
Correct Answer: Add it to the exclude list, refresh nodes, and wait for decommissioning to complete.
Explanation:
Decommissioning lets the NameNode replicate affected blocks elsewhere before declaring the DataNode safely retired. Abrupt shutdown can temporarily create under-replicated blocks.
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49A large text file has HDFS block boundaries that fall in the middle of several lines. With a standard line-oriented TextInputFormat, why are records normally neither lost nor duplicated?
Hadoop MapReduce paradigm
Hard
A.Each mapper processes only complete physical blocks and sends partial lines directly to the reducer.
B.The JobTracker merges duplicate mapper records by comparing their byte offsets after the map phase.
C.Each split's RecordReader adjusts record boundaries, typically skipping or extending partial lines as needed.
D.The NameNode rewrites every file so that newline characters always coincide with HDFS block boundaries.
Correct Answer: Each split's RecordReader adjusts record boundaries, typically skipping or extending partial lines as needed.
Explanation:
Input splits describe logical work, while the RecordReader defines record boundaries. A line reader can read beyond a split boundary to finish a record and avoid re-emitting the preceding partial record.
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50A job uses four reducers and the default conceptual partition rule . Keys A and B have different hash codes but compare equal under the reducer's grouping comparator. What must be true for correctness?
MapReduce Terminology
Hard
A.The sort comparator must force A and B into different spill files.
B.The partitioner must send A and B to the same reducer.
C.The combiner must convert A and B into an identical serialized key.
D.The RecordReader must emit A and B from the same input split.
Correct Answer: The partitioner must send A and B to the same reducer.
Explanation:
Grouping occurs only within a reducer. Keys considered part of one reduce group must therefore be assigned to the same partition; otherwise the logical group is split across reducers.
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51A mapper emits (department, (salary, 1)). A proposed combiner replaces all salaries for each department with (averageSalary, 1), and the reducer averages its received salary values. Why can this produce an incorrect global average?
Hadoop MapReduce paradigm
Hard
A.An average alone discards the partial count needed to weight groups of unequal size.
B.Combiners execute exactly once, so the reducer receives only one value from each mapper.
C.Reducers cannot receive floating-point values generated by a mapper-side combiner.
D.The shuffle sorts departments by salary and therefore changes the arithmetic order.
Correct Answer: An average alone discards the partial count needed to weight groups of unequal size.
Explanation:
An unweighted average of partial averages is invalid when partial groups differ in size. A safe design combines (sum, count) pairs, which are associative and composable.
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52A reducer writes directly to an external payment service, and speculative execution is enabled. Two attempts of the same reduce task both reach the service before one attempt is discarded. Which design property is required to prevent duplicate payments?
Hadoop MapReduce paradigm
Hard
A.The combiner must aggregate payments because combiners are guaranteed to execute exactly once.
B.The reducer must use more input splits so each speculative attempt handles fewer payment records.
C.The external operation must be idempotent or deduplicated using a stable transaction identifier.
D.The HDFS replication factor must equal the number of simultaneous speculative task attempts.
Correct Answer: The external operation must be idempotent or deduplicated using a stable transaction identifier.
Explanation:
Speculation can run multiple attempts for one logical task. Hadoop commits managed output carefully, but arbitrary external side effects require idempotency or explicit deduplication.
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53A secondary-sort job uses composite keys (customer, timestamp) and must deliver each customer's records to one reducer in ascending timestamp order. Which configuration is correct?
MapReduce Terminology
Hard
A.Partition by the full composite key, sort by customer, and group by timestamp.
B.Partition randomly, sort by timestamp only, and group by customer at the JobTracker.
C.Partition by customer, sort by the full composite key, and group by customer.
D.Partition by timestamp, sort by customer only, and group by the full composite key.
Correct Answer: Partition by customer, sort by the full composite key, and group by customer.
Explanation:
Customer-based partitioning co-locates each customer's keys. Full-key sorting orders timestamps, while customer-only grouping invokes the reducer once per customer.
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54A job has 12 map tasks and 5 reduce tasks. Every mapper emits data, but the partitioner assigns no keys to one reducer. How many final reducer part files are normally created after successful completion?
MapReduce Terminology
Hard
A.Four part files, because reducers without keys do not commit task output.
B.Sixty part files, because every mapper creates one committed file per reducer.
C.Twelve part files, because each mapper creates one final shuffled output file.
D.Five part files, including an empty file for the reducer receiving no keys.
Correct Answer: Five part files, including an empty file for the reducer receiving no keys.
Explanation:
Each successful reducer task normally commits one final part file. A reducer with no assigned records still has a task output, which may be empty.
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55In a default classic Hadoop 1.x deployment without configured recovery or external high availability, what is the principal effect of JobTracker failure?
Hadoop JobTracker
Hard
A.Only reduce tasks fail; map tasks continue and commit the entire job without a coordinator.
B.The NameNode assumes scheduling duties because it already tracks block and worker locations.
C.TaskTrackers elect a replacement JobTracker and preserve all in-memory job state automatically.
D.Running jobs lose central coordination and generally must be resubmitted after the JobTracker returns.
Correct Answer: Running jobs lose central coordination and generally must be resubmitted after the JobTracker returns.
Explanation:
The JobTracker is the central MRv1 scheduler and job coordinator. Without recovery or HA mechanisms, its failure disrupts active jobs even though HDFS may remain operational.
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56For a map task whose input block has replicas on nodes N1, N2, and N3, no map slot is immediately free on those nodes, but a slot is free on another node in the same rack. Which scheduling choice best reflects delay scheduling for locality?
Hadoop JobTracker
Hard
A.Assign the task to all three replica nodes even though their map slots are occupied.
B.Wait briefly for node locality, then allow rack-local execution if the delay threshold expires.
C.Move the input block permanently to the free node before assigning the map task.
D.Run immediately on any cluster node because HDFS replication makes data locality irrelevant.
Correct Answer: Wait briefly for node locality, then allow rack-local execution if the delay threshold expires.
Explanation:
Delay scheduling briefly postpones assignment to improve node locality without allowing a job to wait indefinitely. Rack-local execution is a common fallback when node-local capacity does not become available.
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57A classic MRv1 TaskTracker is configured with 8 map slots and 2 reduce slots. All map slots are busy, both reduce slots are idle, and another map task is waiting. Under static slot configuration, what normally happens?
Hadoop TaskTracker
Hard
A.The map task immediately uses a reduce slot because all slots are dynamically interchangeable.
B.The TaskTracker moves one active mapper to a reduce slot and launches the waiting mapper.
C.The DataNode creates an additional map slot when it detects a locally replicated input block.
D.The map task waits because an idle reduce slot is not automatically converted into a map slot.
Correct Answer: The map task waits because an idle reduce slot is not automatically converted into a map slot.
Explanation:
Classic MRv1 commonly uses separately configured map and reduce slot capacities. Idle capacity of one slot type is not automatically available to the other type.
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58A TaskTracker stops heartbeating after launching several attempts. The attempts may still be running at the operating-system level. What does the JobTracker normally do after the worker timeout?
Hadoop TaskTracker
Hard
A.It asks the NameNode to convert the attempts into HDFS block-replication operations.
B.It marks the worker lost and reschedules incomplete attempts on other available TaskTrackers.
C.It waits indefinitely because only a task JVM, not a TaskTracker, can report task failure.
D.It treats the attempts as successful because their JVM processes may still be running.
Correct Answer: It marks the worker lost and reschedules incomplete attempts on other available TaskTrackers.
Explanation:
TaskTracker heartbeats report worker and task status. When the worker times out, the JobTracker cannot trust its attempts and schedules replacement attempts elsewhere.
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59The command hadoop jar hadoop-mapreduce-examples.jar wordcount /books /wc-out fails immediately with a message that /wc-out already exists. Which remedy preserves the existing output while allowing a new run?
Word count on command line
Hard
A.Append -overwrite because the example WordCount class always supports that flag.
B.Delete only _SUCCESS because its presence is the sole cause of the path-exists check.
C.Use the existing directory because each reducer automatically chooses a unique subdirectory.
D.Choose a different non-existing HDFS output path for the new job.
Correct Answer: Choose a different non-existing HDFS output path for the new job.
Explanation:
MapReduce output formats normally require the output directory not to exist. A new path preserves the prior results and avoids the pre-execution validation failure.
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60A WordCount job completes with three reducers. Running hadoop fs -cat /wc-out/part-r-00000 shows only some words, even though the input contained many more. What is the correct way to inspect the complete reducer output from the command line?
Word count on command line
Hard
A.Run hadoop fs -cat /wc-out/part-m-* because mapper files are the final word counts.
B.Run hadoop fs -cat /wc-out/_SUCCESS because it contains merged reducer records.
C.Run hadoop fs -cat /wc-out/part-r-* to concatenate all reducer part files.
D.Run hadoop fs -getmerge /wc-out/part-r-00000 result.txt because one part references all others.
Correct Answer: Run hadoop fs -cat /wc-out/part-r-* to concatenate all reducer part files.
Explanation:
Each reducer writes a separate part-r-* file containing its key partitions. _SUCCESS is only a completion marker, so all reducer files must be read to inspect the full result.
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