Cloudera Certified Developer for Apache Hadoop: CCD-333 Exam
"Cloudera Certified Developer for Apache Hadoop", also known as CCD-333 exam, is a Cloudera Certification. With the complete collection of questions and answers, PrepAwayPDF has assembled to take you through 60 Q&As to your CCD-333 Exam preparation. In the CCD-333 exam resources, you will cover every field and category in CCDH Certification helping to ready you for your successful Cloudera Certification.
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Cloudera CCD-333 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Processing with Hadoop Ecosystem | - Hive query language and data warehousing - Pig Latin scripting basics |
| Data Analysis and Optimization | - Query optimization in Hive - Performance tuning in MapReduce jobs |
| Data Ingestion and Workflow | - Sqoop data import/export - Flume data ingestion concepts |
| Hadoop Cluster Operations | - Resource management with YARN - Cluster configuration and management basics |
| Hadoop Fundamentals | - MapReduce programming model - HDFS architecture and data storage concepts |
Cloudera Certified Developer for Apache Hadoop Sample Questions:
1. Given a Mapper, Reducer, and Driver class packaged into a jar, which is the correct way of submitting the job to the cluster?
A) jar MyJar.jar MyDriverClass inputdir outputdir
B) jar MyJar.jar
C) hadoop jar MyJar.jar MyDriverClass inputdir outputdir
D) hadoop jar class MyJar.jar MyDriverClass inputdir outputdir
2. When is the reduce method first called in a MapReduce job?
A) 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.
B) 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.
C) 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.
D) 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.
3. Your cluster has 10 DataNodes, each with a single 1 TB hard drive. You utilize all your disk capacity for HDFS, reserving none for MapReduce. You implement default replication settings. What is the storage capacity of your Hadoop cluster (assuming no compression)?
A) about 3 TB
B) about 10 TB
C) about 5 TB
D) about 11 TB
4. The Hadoop framework provides a mechanism for coping with machine issues such as faulty configuration or impending hardware failure. MapReduce detects that one or a number of machines are performing poorly and starts more copies of a map or reduce task. All the tasks run simultaneously and the task that finish first are used. This is called:
A) Default Partitioner
B) IdentityMapper
C) IdentityReducer
D) Speculative Execution
E) Combiner
5. In a MapReduce job, you want each of you 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?
A) Write a custom FileInputFormat and override the method isSplittable to always return false.
B) Set the number of mappers equal to the number of input files you want to process.
C) Write a custom MapRunner that iterates over all key-value pairs in the entire file.
D) Increase the parameter that controls minimum split size in the job configuration.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: D | Question # 5 Answer: A |
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