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.
For a data analytics project requiring integration with AI frameworks, how does Spark support this requirement?
- Spark GraphX
- Spark MLlib
- Spark SQL
- Spark Streaming
Spark supports integration with AI frameworks through Spark MLlib. MLlib provides a scalable machine learning library that integrates seamlessly with Spark, enabling data analytics projects to incorporate machine learning capabilities.
For a Hadoop cluster facing performance issues with specific types of jobs, what targeted tuning technique would be effective?
- Input Split Size Adjustment
- Map Output Compression
- Speculative Execution
- Task Tracker Heap Size
When addressing performance issues with specific types of jobs, utilizing speculative execution can be effective. Speculative execution involves launching backup tasks for slower tasks, ensuring that the job completes faster by using additional resources if needed. This is particularly useful for handling straggler tasks.
In Hive, ____ is a mechanism that enables more efficient data retrieval by skipping over irrelevant data.
- Data Skewing
- Indexing
- Predicate Pushdown
- Query Optimization
In Hive, Predicate Pushdown is a mechanism that enables more efficient data retrieval by pushing filtering conditions closer to the data source. It helps to skip over irrelevant data early in the query execution process, improving performance.
When planning the capacity of a Hadoop cluster, what metric is critical for balancing the load across DataNodes?
- CPU Usage
- Memory Usage
- Network Bandwidth
- Storage Capacity
When planning the capacity of a Hadoop cluster, network bandwidth is a critical metric for balancing the load across DataNodes. It ensures efficient data transfer and prevents bottlenecks in the network, optimizing the overall performance of the cluster.
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.