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Cloudera CCD-333 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Hadoop Fundamentals | - HDFS architecture and data storage concepts - MapReduce programming model |
| Data Analysis and Optimization | - Performance tuning in MapReduce jobs - Query optimization in Hive |
| Data Processing with Hadoop Ecosystem | - Hive query language and data warehousing - Pig Latin scripting basics |
| Data Ingestion and Workflow | - Flume data ingestion concepts - Sqoop data import/export |
| Hadoop Cluster Operations | - Resource management with YARN - Cluster configuration and management basics |
Cloudera Certified Developer for Apache Hadoop Sample Questions:
You use the hadoop fs -put command to write a 300 MB file using an HDFS block size of 64 MB. Just after this command has finished writing 200 MB of this file, what would another user see when trying to access this file?
- A. They would see Hadoop throw an concurrentFileAccessException when they try to access this file.
- B. They would see no content until the whole file is written and closed.
- C. They would see the current state of the file, up to the last bit written by the command.
- D. They would see the content of the file through the last completed block.
Correct Answer: B 🗳️
Explanation: Only visible for ValidExam members. You can sign-up / login (it's free).
Which of the following describes how a client reads a file from HDFS?
- A. 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.
- B. 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 then reads the data directly off the DataNode.
- C. 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 directly off the DataNode(s).
- D. The client contacts the NameNode for the block location(s). The NameNode contacts theDataNode that holds the requested data block. Data is transferred from the DataNode to the NameNode, and then from the NameNode to the client.
Correct Answer: B 🗳️
Explanation: Only visible for ValidExam members. You can sign-up / login (it's free).
Given a Mapper, Reducer, and Driver class packaged into a jar, which is the correct way of submitting the job to the cluster?
- A. hadoop jar MyJar.jar MyDriverClass inputdir outputdir
- B. jar MyJar.jar MyDriverClass inputdir outputdir
- C. hadoop jar class MyJar.jar MyDriverClass inputdir outputdir
- D. jar MyJar.jar
Correct Answer: A 🗳️
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Which of the following statements most accurately describes the relationship between MapReduce and Pig?
- A. Pig provides additional capabilities that allow certain types of data manipulation not possible with MapReduce.
- B. Pig programs rely on MapReduce but are extensible, allowing developers to do special-purpose processing not provided by MapReduce.
- C. Pig provides no additional capabilities to MapReduce. Pig programs are executed as MapReduce jobs via the Pig interpreter.
- D. Pig provides the additional capability of allowing you to control the flow of multiple MapReduce jobs.
Correct Answer: D 🗳️
Explanation: Only visible for ValidExam members. You can sign-up / login (it's free).
You write a MapReduce job to process 100 files in HDFS. Your MapReduce algorithm uses TextInputFormat and the IdentityReducer: the mapper applies a regular expression over input values and emits key-value 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 reducers to zero.
- A. 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.
- B. 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.
- C. There is no difference in output between the two settings.
- D. With zero reducers, all instances of matching patterns are gathered together in one file on HDFS. With one reducer, instances of matching patterns stored in multiple files on HDFS.
Correct Answer: B 🗳️
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