When a Hadoop job fails due to a specific node repeatedly crashing, what diagnostic action should be prioritized?

  • Check Node Logs for Errors
  • Ignore the Node and Rerun the Job
  • Increase Job Redundancy
  • Reinstall Hadoop on the Node
If a Hadoop job fails due to a specific node repeatedly crashing, the diagnostic action that should be prioritized is checking the node logs for errors. This helps identify the root cause of the node's failure and allows for targeted troubleshooting and resolution.

What is the primary role of Apache Oozie in Hadoop data pipelines?

  • Data Analysis
  • Data Ingestion
  • Data Storage
  • Workflow Coordination
The primary role of Apache Oozie in Hadoop data pipelines is workflow coordination. Oozie allows users to define and manage workflows that coordinate the execution of Hadoop jobs, making it easier to schedule and manage complex data processing tasks in a coordinated manner.

Avro's ____ feature enables the seamless handling of complex data structures and types.

  • Compression
  • Encryption
  • Query Optimization
  • Schema Evolution
Avro's Schema Evolution feature allows the modification of data structures without requiring changes to the entire dataset. This flexibility is crucial for handling evolving data in Big Data environments.

In the context of Hadoop cluster security, ____ plays a crucial role in authentication and authorization processes.

  • Kerberos
  • LDAP
  • OAuth
  • SSL/TLS
Kerberos plays a crucial role in Hadoop cluster security, providing strong authentication and authorization mechanisms. It ensures that only authorized users and processes can access Hadoop resources, enhancing the overall security of the cluster.

Which metric is crucial for assessing the health of a DataNode in a Hadoop cluster?

  • CPU Temperature
  • Disk Usage
  • Heartbeat Status
  • Network Latency
The heartbeat status is crucial for assessing the health of a DataNode in a Hadoop cluster. DataNodes send periodic heartbeats to the NameNode to confirm their availability. If the NameNode stops receiving heartbeats from a DataNode, it may be an indication of a node failure or network issues.

____ is an essential step in data loading to optimize the storage and processing of large datasets in Hadoop.

  • Data Aggregation
  • Data Compression
  • Data Encryption
  • Data Indexing
Data Compression is an essential step in data loading to optimize the storage and processing of large datasets in Hadoop. Compression reduces the storage space required for data and speeds up data transfer, improving overall performance in Hadoop clusters.

The ____ method in the Reducer class is crucial for aggregating the Mapper's outputs into the final result.

  • Aggregate
  • Combine
  • Finalize
  • Reduce
The 'Reduce' method in the Reducer class is essential for aggregating the outputs generated by the Mapper tasks. It processes the intermediate key-value pairs, performs the required operations, and produces the final result of the MapReduce job.

____ is a column-oriented file format in Hadoop, optimized for querying large datasets.

  • Avro
  • ORC
  • Parquet
  • SequenceFile
Parquet is a column-oriented file format in Hadoop designed for optimal query performance on large datasets. It organizes data in a columnar fashion, allowing for efficient compression and improved read performance, making it suitable for analytical workloads.

In Hadoop Streaming, the ____ serves as a connector between the script and the Hadoop framework for processing data.

  • Combiner
  • InputFormat
  • Mapper
  • Reducer
In Hadoop Streaming, the InputFormat serves as a connector between the script and the Hadoop framework. It defines how the data is read and presented to the mapper for processing. The InputFormat specifies the input data's structure and how it should be split and processed.

In Hadoop, the process of adding more nodes to a cluster is known as _____.

  • Cluster Augmentation
  • Node Expansion
  • Replication
  • Scaling Out
In Hadoop, the process of adding more nodes to a cluster is known as Scaling Out. This involves increasing the number of nodes in the cluster to handle growing data volumes and enhance processing capabilities. Scaling out is a key strategy to accommodate the scalability requirements of big data applications.