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Exam Code: CCD-410
Exam Name: Cloudera Certified Developer for Apache Hadoop (CCDH)
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NO.1 Table metadata in Hive is:
A. Stored as metadata on the NameNode.
B. Stored along with the data in HDFS.
C. Stored in the Metastore.
D. Stored in ZooKeeper.
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By default, hive use an embedded Derby database to store metadata information.
The metastore is the "glue" between Hive and HDFS. It tells Hive where your data files live in
HDFS, what type of data they contain, what tables they belong to, etc.
The Metastore is an application that runs on an RDBMS and uses an open source ORM layer
called DataNucleus, to convert object representations into a relational schema and vice versa.
They chose this approach as opposed to storing this information in hdfs as they need the
Metastore to be very low latency. The DataNucleus layer allows them to plugin many different
*By default, Hive stores metadata in an embedded Apache Derby database, and other
client/server databases like MySQL can optionally be used.
*features of Hive include:
Metadata storage in an RDBMS, significantly reducing the time to perform semantic checks during
Reference: Store Hive Metadata into RDBMS
NO.2 In a MapReduce job, the reducer receives all values associated with same key. Which statement
best describes the ordering of these values?
A. The values are in sorted order.
B. The values are arbitrarily ordered, and the ordering may vary from run to run of the same
C. The values are arbitrary ordered, but multiple runs of the same MapReduce job will always have
the same ordering.
D. Since the values come from mapper outputs, the reducers will receive contiguous sections of
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*Input to the Reducer is the sorted output of the mappers.
*The framework calls the application's Reduce function once for each unique key in the sorted
For the given sample input the first map emits:
< Hello, 1>
< World, 1>
< Bye, 1>
< World, 1>
The second map emits:
< Hello, 1>
< Hadoop, 1>
< Goodbye, 1>
< Hadoop, 1>
NO.3 You want to understand more about how users browse your public website, such as which
pages they visit prior to placing an order. You have a farm of 200 web servers hosting your website.
How will you gather this data for your analysis?
A. Ingest the server web logs into HDFS using Flume.
B. Write a MapReduce job, with the web servers for mappers, and the Hadoop cluster nodes for
C. Import all users' clicks from your OLTP databases into Hadoop, using Sqoop.
D. Channel these clickstreams inot Hadoop using Hadoop Streaming.
E. Sample the weblogs from the web servers, copying them into Hadoop using curl.
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NO.4 You've written a MapReduce job that will process 500 million input records and generated 500
million key-value pairs. The data is not uniformly distributed. Your MapReduce job will create a
significant amount of intermediate data that it needs to transfer between mappers and reduces
which is a potential bottleneck. A custom implementation of which interface is most likely to reduce
the amount of intermediate data transferred across the network?
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Combiners are used to increase the efficiency of a MapReduce program. They are used to aggregate
intermediate map output locally on individual mapper outputs. Combiners can help you reduce the
amount of data that needs to be transferred across to the reducers. You can use your reducer code
as a combiner if the operation performed is commutative and associative.
Reference: 24 Interview Questions & Answers for Hadoop MapReduce developers, What are
combiners? When should I use a combiner in my MapReduce Job?
NO.5 You write MapReduce job to process 100 files in HDFS. Your MapReduce algorithm uses
TextInputFormat: the mapper applies a regular expression over input values and emits key-values
pairs with the key consisting of the matching text, and the value containing the filename and byte
offset. Determine the difference between setting the number of reduces to one and settings the
number of reducers to zero.
A. There is no difference in output between the two settings.
B. With zero reducers, no reducer runs and the job throws an exception. With one reducer, instances
of matching patterns are stored in a single file on HDFS.
C. With zero reducers, all instances of matching patterns are gathered together in one file on HDFS.
With one reducer, instances of matching patterns are stored in multiple files on HDFS.
D. With zero reducers, instances of matching patterns are stored in multiple files on HDFS. With one
reducer, all instances of matching patterns are gathered together in one file on HDFS.
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* It is legal to set the number of reduce-tasks to zero if no reduction is desired.
In this case the outputs of the map-tasks go directly to the FileSystem, into the output path set by
setOutputPath(Path). The framework does not sort the map-outputs before writing them out to the
* Often, you may want to process input data using a map function only. To do this, simply set
mapreduce.job.reduces to zero. The MapReduce framework will not create any reducer tasks.
Rather, the outputs of the mapper tasks will be the final output of the job.
In this phase the reduce(WritableComparable, Iterator, OutputCollector, Reporter) method is
called for each <key, (list of values)> pair in the grouped inputs.
The output of the reduce task is typically written to the FileSystem via
Applications can use the Reporter to report progress, set application-level status messages and
update Counters, or just indicate that they are alive.
The output of the Reducer is not sorted.
NO.6 To process input key-value pairs, your mapper needs to lead a 512 MB data file in memory.
What is the best way to accomplish this?
A. Serialize the data file, insert in it the JobConf object, and read the data into memory in the
configure method of the mapper.
B. Place the data file in the DistributedCache and read the data into memory in the map method of
C. Place the data file in the DataCache and read the data into memory in the configure method of the
D. Place the data file in the DistributedCache and read the data into memory in the configure method
of the mapper.
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NO.7 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?
A. The amount of RAM installed on the TaskTracker node.
B. The amount of free disk space on the TaskTracker node.
C. The number and speed of CPU cores on the TaskTracker node.
D. The average system load on the TaskTracker node over the past fifteen (15) minutes.
E. The location of the InsputSplit to be processed in relation to the location of the node.
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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.
Reference: 24 Interview Questions & Answers for Hadoop MapReduce developers, How JobTracker
schedules a task?
NO.8 For each intermediate key, each reducer task can emit:
A. As 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).
B. As many final key-value pairs as desired, but they must have the same type as the intermediate
C. As 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.
D. One final key-value pair per value associated with the key; no restrictions on the type.
E. One final key-value pair per key; no restrictions on the type.
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Reference: Hadoop Map-Reduce Tutorial; Yahoo! Hadoop Tutorial, Module 4: MapReduce
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CCD-410 Study Guide
Begin Your Journey to Developer Certification
This exam focuses on engineering data solutions in MapReduce and understanding the Hadoop ecosystem (including Hive, Pig, Sqoop, Oozie, Crunch, and Flume). Candidates who successfully pass CCD–410 are awarded the Cloudera Certified Hadoop Developer (CCDH) credential.
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