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CCD-410 Online Practice Questions and Answers

Questions 4

Can you use MapReduce to perform a relational join on two large tables sharing a key? Assume that the two tables are formatted as comma-separated files in HDFS.

A. Yes.

B. Yes, but only if one of the tables fits into memory

C. Yes, so long as both tables fit into memory.

D. No, MapReduce cannot perform relational operations.

E. No, but it can be done with either Pig or Hive.

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Questions 5

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 reduces.

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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Questions 6

MapReduce v2 (MRv2/YARN) is designed to address which two issues?

A. Single point of failure in the NameNode.

B. Resource pressure on the JobTracker.

C. HDFS latency.

D. Ability to run frameworks other than MapReduce, such as MPI.

E. Reduce complexity of the MapReduce APIs.

F. Standardize on a single MapReduce API.

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Questions 7

A client application creates an HDFS file named foo.txt with a replication factor of 3. Identify which best describes the file access rules in HDFS if the file has a single block that is stored on data nodes A, B and C?

A. The file will be marked as corrupted if data node B fails during the creation of the file.

B. Each data node locks the local file to prohibit concurrent readers and writers of the file.

C. Each data node stores a copy of the file in the local file system with the same name as the HDFS file.

D. The file can be accessed if at least one of the data nodes storing the file is available.

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Questions 8

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 the mapper.

C. Place the data file in the DataCache and read the data into memory in the configure method of the mapper.

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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Questions 9

You need to create a job that does frequency analysis on input data. You will do this by writing a Mapper that uses TextInputFormat and splits each value (a line of text from an input file) into individual characters. For each one of these characters, you will emit the character as a key and an InputWritable as the value. As this will produce proportionally more intermediate data than input data, which two resources should you expect to be bottlenecks?

A. Processor and network I/O

B. Disk I/O and network I/O

C. Processor and RAM

D. Processor and disk I/O

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Questions 10

A combiner reduces:

A. The number of values across different keys in the iterator supplied to a single reduce method call.

B. The amount of intermediate data that must be transferred between the mapper and reducer.

C. The number of input files a mapper must process.

D. The number of output files a reducer must produce.

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Questions 11

In a MapReduce job with 500 map tasks, how many map task attempts will there be?

A. It depends on the number of reduces in the job.

B. Between 500 and 1000.

C. At most 500.

D. At least 500.

E. Exactly 500.

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Questions 12

When can a reduce class also serve as a combiner without affecting the output of a MapReduce program?

A. When the types of the reduce operation's input key and input value match the types of the reducer's output key and output value and when the reduce operation is both communicative and associative.

B. When the signature of the reduce method matches the signature of the combine method.

C. Always. Code can be reused in Java since it is a polymorphic object-oriented programming language.

D. Always. The point of a combiner is to serve as a mini-reducer directly after the map phase to increase performance.

E. Never. Combiners and reducers must be implemented separately because they serve different purposes.

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Questions 13

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?

A. SequenceFiles

B. Avro

C. JSON

D. HTML

E. XML

F. CSV

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Exam Code: CCD-410
Exam Name: Cloudera Certified Developer for Apache Hadoop (CCDH)
Last Update: Nov 17, 2024
Questions: 60
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