- Exam Code: CCD-333
- Exam Name: Cloudera Certified Developer for Apache Hadoop
- Updated: Oct 13, 2026
- Q & A: 60 Questions and Answers
Cloudera Certified Developer for Apache Hadoop : CCD-333
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Cloudera CCD-333 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Hadoop Architecture and HDFS | 25% | - HDFS API usage - Data access, replication, and consistency - HDFS components and design |
| MapReduce Design and Development | 35% | - Mappers, reducers, combiners, partitioners - Job configuration and optimization - MapReduce workflow and execution - Input/output formats and data serialization |
| Hadoop Ecosystem Tools | 25% | - Sqoop and Flume for data ingestion - Apache Hive and data warehousing - Apache Pig and data flow scripting - Oozie workflow scheduling |
| Best Practices and Optimization | 15% | - Debugging and error handling - Scalability and fault tolerance - Performance tuning |
Cloudera Certified Developer for Apache Hadoop Sample Questions:
Question #1
Workflows expressed in Oozie can contain:
- A. Iterative repetition of MapReduce jobs until a desired answer or state is reached.
- B. Sequences of MapReduce and Pig jobs. These are limited to linear sequences of actions with exception handlers but no forks.
- C. Sequences of MapReduce and Pig. These sequences can be combined with other actions including forks, decision points, and path joins.
- D. Sequences of MapReduce jobs only; no Pig or Hive tasks or jobs. These MapReduce sequences can be combined with forks and path joins.
Correct Answer: C 🗳️
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Question #2
In the standard word count MapReduce algorithm, why might using a combiner reduce the overall Job running time?
- A. Because combiners perform local aggregation of word counts, thereby reducing the number of key-value pairs that need to be snuff let across the network to the reducers.
- B. Because combiners perform local aggregation of word counts, thereby allowing the mappers to process input data faster.
- C. Because combiners perform local aggregation of word counts, thereby reducing the number of mappers that need to run.
- D. Because combiners perform local aggregation of word counts, and then transfer that data to reducers without writing the intermediate data to disk.
Correct Answer: B 🗳️
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Question #3
How does the NameNode detect that a DataNode has failed?
- A. When HDFS starts up, the NameNode tries to communicate with the DataNode and considers the DataNode as failed if it does not respond.
- B. The NameNode does not need to know that a DataNode has failed.
- C. When the NameNode fails to receive periodic heartbeats from the DataNode, it considers the DataNode as failed.
- D. The NameNode periodically pings the datanode. If the DataNode does not respond, the NameNode considers the DataNode as failed.
Correct Answer: C 🗳️
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Question #4
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. Six
- B. Two
- C. Four
- D. Five
- E. One
- F. Three
Correct Answer: A 🗳️
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Question #5
You need to import a portion of a relational database every day as files to HDFS, and generate Java classes to Interact with your imported data. Which of the following tools should you use to accomplish this?
- A. Hue
- B. Flume
- C. Hive
- D. Oozie
- E. fuse-dfs
- F. Sqoop
- G. Pig
Correct Answer: F 🗳️
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