Cloudera Certified Developer for Apache Hadoop: CCD-333 Exam Premium File as PDF & Test Engine
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CCD-333 exam is an important Cloudera Certification which can test your professional skills. Candidates want to pass the exam successfully to prove their competence. Fast2test Cloudera technical experts have collected and certified 60 questions and answers of CCDH - Cloudera Certified Developer for Apache Hadoop which are designed to cover the knowledge points of the Planning and Designing Cloudera Superdome Server Solutions and enhance candidates' abilities. With Fast2test CCD-333 preparation tests you can pass the CCDH - Cloudera Certified Developer for Apache Hadoop easily, get the Cloudera certification and go further on Cloudera career path.
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Our Cloudera Certified Developer for Apache Hadoop Preparation Material provides you everything you will need to take a Cloudera CCDH CCD-333 examination. Details are researched and produced by Cloudera Certification Experts who are constantly using industry experience to produce precise, and logical.
Cloudera CCD-333 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Workflow and Scheduling | - Oozie
|
| Topic 2: Data Formats and Storage | - Serialization formats
|
| Topic 3: Data Processing Tools | - Hive
|
| Topic 4: Data Processing with MapReduce | - MapReduce programming model
|
| Topic 5: Data Ingestion and Integration | - Flume
|
| Topic 6: Hadoop Ecosystem and Architecture | - Hadoop Distributed File System (HDFS)
|
Cloudera Certified Developer for Apache Hadoop Sample Questions:
You have the following key value pairs as output from your Map task:
(The, 1)
(Fox, 1)
(Runs, 1)
(Faster, 1)
(Than, 1)
(The, 1)
(Dog, 1)
How many keys will be passed to the reducer?
- A. One
- B. Three
- C. Two
- D. Six
- E. Four
- F. Five
Correct Answer: D 🗳️
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You've written a MapReduce job that will process 500 million input records and generate 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 reducers which is a potential bottleneck. A custom implementation of which of the following interfaces is most likely to reduce the amount of intermediate data transferred across the network?
- A. Partitioner
- B. Writable
- C. WritableComparable
- D. Combiner
- E. OutputFormat
- F. InputFormat
Correct Answer: D 🗳️
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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. IdentityMapper
- B. Default Partitioner
- C. Combiner
- D. IdentityReducer
- E. Speculative Execution
Correct Answer: E 🗳️
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You need to create a GUI application to help your company's sales people add and edit customer information. Would HDFS be appropriate for this customer information file?
- A. No, because HDFS is optimized for write-once, streaming access for relatively large files.
- B. No, because HDFS can only be accessed by MapReduce applications.
- C. Yes, because HDFS is optimized for fast retrieval of relatively small amounts of data.
- D. Yes, because HDFS is optimized for random access writes.
Correct Answer: A 🗳️
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Which two of the following are valid statements? (Choose two)
- A. HDFS is a distributed file system that runs on top of native OS filesystems and is well suited to storage of very large data sets.
- B. HDFS is a distributed file system that replaces ext3 or ext4 on Linux nodes in a Hadoop cluster.
- C. HDFS is optimized for storing a large number of files smaller than the HDFS block size.
- D. HDFS has the Characteristic of supporting a "write once, read many" data access model.
Correct Answer: A,D 🗳️
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