Cloudera CCD-410 Cloudera Certified Developer for Apache Hadoop (CCDH) Practice Test 2 | Practice Test / Quiz / MCQs

1. Determine which best describes when the reduce method is first called in a MapReduce job?

Reducers start copying intermediate key-value pairs from each Mapper as soon as it has completed. The reduce method is called as soon as the intermediate key-value pairs start to arrive.Reduce methods and map methods all start at the beginning of a job, in order to provide optimal performance for map-only or reduce-only jobs.Reducers start copying intermediate key-value pairs from each Mapper as soon as it has completed. The programmer can configure in the job what percentage of the intermediate data should arrive before the reduce method begins.Reducers start copying intermediate key-value pairs from each Mapper as soon as it has completed. The reduce method is called only after all intermediate data has been copied and sorted.

2. A combiner reduces:

The number of values across different keys in the iterator supplied to a single reduce method call.The amount of intermediate data that must be transferred between the mapper and reducer.The number of input files a mapper must process.The number of output files a reducer must produce.

3. You want to run Hadoop jobs on your development workstation for testing before you submit them to your production cluster. Which mode of operation in Hadoop allows you to most closely simulate a production cluster while using a single machine?

Run all the nodes in your production cluster as virtual machines on your development workstation.Run simldooop, the Apache open-source software for simulating Hadoop clusters.Run the DataNode, TaskTracker, NameNode and JobTracker daemons on a single machine.Run the hadoop command with the jt local and the fs file:///options.

4. 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?

Write a MapReduce job, with the web servers for mappers, and the Hadoop cluster nodes for reduces.Channel these clickstreams inot Hadoop using Hadoop StreamingIngest the server web logs into HDFS using Flume.Import all users' clicks from your OLTP databases into Hadoop, using Sqoop.

5. You want to perform analysis on a large collection of images. You want to store this data in HDFS and process it with MapReduce but you also want to give your data analysts and data scientists the ability to process the data directly from HDFS with an interpreted high-level programming language like Python. Which format should you use to store this data in HDFS?

HTMLAvroJSONSequenceFiles

6. Which describes how a client reads a file from HDFS?

The client queries all DataNodes in parallel. The DataNode that contains the requested data responds directly to the client. The client reads the data directly off the DataNode.The client contacts the NameNode for the block location(s). The NameNode then queries the DataNodes for block locations. The DataNodes respond to the NameNode, and the NameNode redirects the client to the DataNode that holds the requested data block(s). The client queries the NameNode for the block location(s). The NameNode returns the block location(s) to the client. The client reads the data directory off the DataNode(s).The client contacts the NameNode for the block location(s). The NameNode contacts the DataNode that holds the requested data block. Data is transferred from the DataNode to the NameNode, and then from the NameNode to the client.

7. In a MapReduce job, you want each of your input files processed by a single map task. How do you configure a MapReduce job so that a single map task processes each input file regardless of how many blocks the input file occupies?

Write a custom MapRunner that iterates over all key-value pairs in the entire file.Write a custom FileInputFormat and override the method isSplitable to always return false.Set the number of mappers equal to the number of input files you want to process.Increase the parameter that controls minimum split size in the job configuration.

8. In a MapReduce job, the reducer receives all values associated with same key. Which statement best describes the ordering of these values?

Since the values come from mapper outputs, the reducers will receive contiguous sections of sorted values.The values are arbitrarily ordered, and the ordering may vary from run to run of the same MapReduce job.The values are in sorted order.The values are arbitrary ordered, but multiple runs of the same MapReduce job will always have the same ordering.

9. What types of algorithms are difficult to express in MapReduce v1 (MRv1)?

Large-scale graph algorithms that require one-step link traversal.Algorithms that require global, sharing states.Relational operations on large amounts of structured and semi-structured data.Algorithms that require applying the same mathematical function to large numbers of individual binary records.

10. You wrote a map function that throws a runtime exception when it encounters a control character in input data. The input supplied to your mapper contains twelve such characters totals, spread across five file splits. The first four file splits each have two control characters and the last split has four control characters. Indentify the number of failed task attempts you can expect when you run the job with mapred.max.map.attempts set to 4:

You will have twenty failed task attemptsYou will have forty-eight failed task attemptsYou will have seventeen failed task attemptsYou will have five failed task attempts

11. Your cluster's HDFS block size in 64MB. You have directory containing 100 plain text files, each of which is 100MB in size. The InputFormat for your job is TextInputFormat. Determine how many Mappers will run?

64200100640

12. You have user profile records in your OLPT database, that you want to join with web logs you have already ingested into the Hadoop file system. How will you obtain these user records?

HDFS commandSqoop importHive LOAD DATA commandPig LOAD command

14. Analyze each scenario below and indentify which best describes the behavior of the default partitioner?

The default partitioner assigns key-values pairs to reduces based on an internal random number generator.The default partitioner computes the hash of the key and divides that valule modulo the number of reducers. The result determines the reducer assigned to process the key-value pair..The default partitioner computes the hash of the key. Hash values between specific ranges are associated with different buckets, and each bucket is assigned to a specific reducer.The default partitioner implements a round-robin strategy, shuffling the key-value pairs to each reducer in turn. This ensures an event partition of the key space.

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

Into in-memory buffers that spill over to the local file system (outside HDFS) of the TaskTracker node running the ReducerInto in-memory buffers that spill over to the local file system of the TaskTracker node running the Mapper.Into in-memory buffers on the TaskTracker node running the Mapper that spill over and are written into HDFS.Intermediate data in streamed across the network from Mapper to the Reduce and is never written to disk.

16. Assuming default settings, which best describes the order of data provided to a reducer's reduce method:

Neither keys nor values are in any predictable order.Both the keys and values passed to a reducer always appear in sorted order.The keys given to a reducer are in sorted order but the values associated with each key are in no predictable orderThe keys given to a reducer aren't in a predictable order, but the values associated with those keys always are.

17. You have a directory named jobdata in HDFS that contains four files: _first.txt, second.txt, .third.txt and #data.txt. How many files will be processed by the FileInputFormat.setInputPaths () command when it's given a path object representing this directory?

None, the directory cannot be named jobdataTwo, file names with a leading period or underscore are ignoredFour, all files will be processedThree, the pound sign is an invalid character for HDFS file names

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

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

19. You need to perform statistical analysis in your MapReduce job and would like to call methods in the Apache Commons Math library, which is distributed as a 1.3 megabyte Java archive (JAR) file. Which is the best way to make this library available to your MapReducer job at runtime?

Package your code and the Apache Commands Math library into a zip file named JobJar.zipHave your system administrator copy the JAR to all nodes in the cluster and set its location in the HADOOP_CLASSPATH environment variable before you submit your job.Have your system administrator place the JAR file on a Web server accessible to all cluster nodes and then set the HTTP_JAR_URL environment variable to its location.When submitting the job on the command line, specify the libjars option followed by the JAR file path.

20. 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?

WritableComparablePartitionerOutputFormatCombiner

21. Your cluster's HDFS block size in 64MB. You have directory containing 100 plain text files, each of which is 100MB in size. The InputFormat for your job is TextInputFormat. Determine how many Mappers will run?

10064200640

22. 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?

Place the data file in the DistributedCache and read the data into memory in the configure method of the mapper.Place the data file in the DataCache and read the data into memory in the configure method of the mapper.Serialize the data file, insert in it the JobConf object, and read the data into memory in the configure method of the mapper.Place the data file in the DistributedCache and read the data into memory in the map method of the mapper.

23. MapReduce v2 (MRv2/YARN) splits which major functions of the JobTracker into separate daemons?

Resource management / Job scheduling/monitoringHeath states checks (heartbeats)Job coordination between the ResourceManager and NodeManager Launching tasks

24. For each input key-value pair, mappers can emit:

As many intermediate key-value pairs as designed, as long as all the keys have the same types and all the values have the same type.As many intermediate key-value pairs as designed. There are no restrictions on the types of those key- value pairs (i.e., they can be heterogeneous).As many intermediate key-value pairs as designed, but they cannot be of the same type as the input key-value pair.One intermediate key-value pair, of a different type.

25. 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.

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.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.There is no difference in output between the two settings.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.

25 Questions

❤ If you liked Fatskills, consider supporting us by checking out The Life Manuals You Never Got.

About | Explore | User Guide | Topics | Subjects | Doubt Solver | Career Aptitude Test | Answers | Free Tools | OSHA Basics Quiz | What Should We Know?
Privacy | Terms |

Without work one finishes nothing. - Ralph Waldo Emerson
© 2026 Fatskills.com

All trademarks, logos and brand names are the property of their respective owners. All company, product and service names used in this website are for identification purposes only. Use of these names, trademarks and brands does not imply endorsement.

25 Questions

❤ If you liked Fatskills, consider supporting us by checking out The Life Manuals You Never Got.

About | Explore | User Guide | Topics | Subjects | Doubt Solver | Career Aptitude Test | Answers | Free Tools | OSHA Basics Quiz | What Should We Know? Privacy | Terms |

Without work one finishes nothing. - Ralph Waldo Emerson© 2026 Fatskills.com

All trademarks, logos and brand names are the property of their respective owners. All company, product and service names used in this website are for identification purposes only. Use of these names, trademarks and brands does not imply endorsement.

About | Explore | User Guide | Topics | Subjects | Doubt Solver | Career Aptitude Test | Answers | Free Tools | OSHA Basics Quiz | What Should We Know?
Privacy | Terms |

Without work one finishes nothing. - Ralph Waldo Emerson
© 2026 Fatskills.com