Hortonworks HDPCD Exam Practice Test Instant Access

Question 1

How are keys and values presented and passed to the reducers during a standard sort and shuffle phase of MapReduce?

AKeys are presented to reducer in sorted order; values for a given key are not sorted.

BKeys are presented to reducer in sorted order; values for a given key are sorted in ascending order.

CKeys are presented to a reducer in random order; values for a given key are not sorted.

DKeys are presented to a reducer in random order; values for a given key are sorted in ascending order.

Answer : A

Reducer has 3 primary phases:

1. Shuffle

The Reducer copies the sorted output from each Mapper using HTTP across the network.

2. Sort

The framework merge sorts Reducer inputs by keys (since different Mappers may have output the same key).

The shuffle and sort phases occur simultaneously i.e. while outputs are being fetched they are merged.

SecondarySort

To achieve a secondary sort on the values returned by the value iterator, the application should extend the key with the secondary key and define a grouping comparator. The keys will be sorted using the entire key, but will be grouped using the grouping comparator to decide which keys and values are sent in the same call to reduce.

3. Reduce

In this phase the reduce(Object, Iterable, Context) method is called for each <key, (collection of values)> in the sorted inputs.

The output of the reduce task is typically written to a RecordWriter via TaskInputOutputContext.write(Object, Object).

The output of the Reducer is not re-sorted.

Question 2

Given the following Hive command:

Which one of the following statements is true?

AThe files in the mydata folder are copied to a subfolder of /apps/hlve/warehouse

BThe files in the mydata folder are moved to a subfolder of /apps/hive/wa re house

CThe files in the mydata folder are copied into Hive's underlying relational database

DThe files in the mydata folder do not move from their current location In HDFS

Answer : D

Question 3

For each intermediate key, each reducer task can emit:

AAs many final key-value pairs as desired. There are no restrictions on the types of those key-value pairs (i.e., they can be heterogeneous).

BAs many final key-value pairs as desired, but they must have the same type as the intermediate key-value pairs.

CAs many final key-value pairs as desired, as long as all the keys have the same type and all the values have the same type.

DOne final key-value pair per value associated with the key; no restrictions on the type.

EOne final key-value pair per key; no restrictions on the type.

Answer : C

Question 4

Which HDFS command uploads a local file X into an existing HDFS directory Y?

Ahadoop scp X Y

Bhadoop fs -localPut X Y

Chadoop fs-put X Y

Dhadoop fs -get X Y

Answer : C

Question 5

On a cluster running MapReduce v1 (MRv1), a TaskTracker heartbeats into the JobTracker on your cluster, and alerts the JobTracker it has an open map task slot.

What determines how the JobTracker assigns each map task to a TaskTracker?

AThe amount of RAM installed on the TaskTracker node.

BThe amount of free disk space on the TaskTracker node.

CThe number and speed of CPU cores on the TaskTracker node.

DThe average system load on the TaskTracker node over the past fifteen (15) minutes.

EThe location of the InsputSplit to be processed in relation to the location of the node.

Answer : E

The TaskTrackers send out heartbeat messages to the JobTracker, usually every few minutes, to reassure the JobTracker that it is still alive. These message also inform the JobTracker of the number of available slots, so the JobTracker can stay up to date with where in the cluster work can be delegated. When the JobTracker tries to find somewhere to schedule a task within the MapReduce operations, it first looks for an empty slot on the same server that hosts the DataNode containing the data, and if not, it looks for an empty slot on a machine in the same rack.

Question 6

You have just executed a MapReduce job. Where is intermediate data written to after being emitted from the Mapper's map method?

AIntermediate data in streamed across the network from Mapper to the Reduce and is never written to disk.

BInto in-memory buffers on the TaskTracker node running the Mapper that spill over and are written into HDFS.

CInto in-memory buffers that spill over to the local file system of the TaskTracker node running the Mapper.

DInto in-memory buffers that spill over to the local file system (outside HDFS) of the TaskTracker node running the Reducer

EInto in-memory buffers on the TaskTracker node running the Reducer that spill over and are written into HDFS.

Answer : C

The mapper output (intermediate data) is stored on the Local file system (NOT HDFS) of each individual mapper nodes. This is typically a temporary directory location which can be setup in config by the hadoop administrator. The intermediate data is cleaned up after the Hadoop Job completes.

Question 7

Identify the MapReduce v2 (MRv2 / YARN) daemon responsible for launching application containers and monitoring application resource usage?

AResourceManager

BNodeManager

CApplicationMaster

DApplicationMasterService

ETaskTracker

FJobTracker

Answer : B

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