In Hadoop YARN's Capacity Scheduler, the yarn.scheduler.capacity..maximum-capacity parameter is used to define a hard limit on the percentage of cluster resources a specific queue can utilize. This prevents a single queue from monopolizing the entire cluster, even when resources are idle. For a queue named QueueA under the root queue, the correct parameter is yarn.scheduler.capacity.root.QueueA.maximum-capacity. This ensures that QueueA's resource consumption does not exceed the configured percentage of its parent's (root's) capacity, providing elasticity while maintaining control over resource allocation.

A. yarn.scheduler.capacity.root.QueueA.user-limit-factor controls the maximum resources a single user can acquire within the queue, not the total limit for the queue itself.

B. yarn.scheduler.capacity.root.QueueA.minimum-user-limit-percent sets a guaranteed minimum resource percentage for each user, ensuring fairness among users, not the queue's maximum cap.

C. yarn.scheduler.capacity.root.QueueA.state is an administrative parameter used to set the queue's operational state to either RUNNING or STOPPED, not to configure resource limits.

1. Apache Hadoop 3.3.6 Documentation, YARN Resource Management and Scheduler, Capacity Scheduler.

Reference: In the section "Queue Properties," the parameter yarn.scheduler.capacity..maximum-capacity is described as: "The maximum capacity of the queue... It’s a hard limit that prevents a single queue from using a disproportionate amount of cluster resources." This directly supports the correct answer D. The same section also defines user-limit-factor, minimum-user-limit-percent, and state, confirming their different purposes.

Location: Apache Hadoop Official Documentation -> YARN -> Capacity Scheduler -> Configuration -> Queue Properties.

2. Huawei FusionInsight HD 6.5.1 Product Documentation, Administrator Guide, YARN Configuration.

Reference: The guide details YARN service configuration parameters. Under the Capacity Scheduler settings, yarn.scheduler.capacity.root..maximum-capacity is specified as the parameter to set the "Maximum queue capacity (%)." This aligns with the official Apache Hadoop documentation and confirms its use in the Huawei ecosystem.

Location: Huawei Support -> FusionInsight HD -> Product Documentation -> Administrator Guide -> Appendix -> YARN Configuration Parameters. (Specific document ID: 31188851, Section: "Capacity Scheduler").

In a recommended high-availability (HA) deployment for a large FusionInsight HD cluster, node roles are strictly separated to ensure stability and performance.

1. DataNode Data Partition (E): The directory /srv/BigData/hadoop/data5 is used for storing HDFS data blocks by the DataNode service. In a standard architecture, DataNode services run exclusively on data nodes, not on control (management) nodes. Therefore, this partition will not exist on a control node.

2. OMS Database Partition (B): The directory /srv/BigData/dbdataom stores the database for the Operations and Maintenance System (OMS). OMS is deployed in an active/standby HA mode on two primary control nodes. However, a robust HA cluster requires a quorum of at least three control nodes for services like ZooKeeper and JournalNode. The third control node acts as a quorum peer but does not host the OMS service. Consequently, the /srv/BigData/dbdataom partition would not exist on this third control node.

A. /: This is the root filesystem partition, which is a fundamental requirement for the operating system on every node in the cluster, including control nodes.

C. /srv/BigData: This is the default base directory for the FusionInsight installation, component packages, and runtime data. It exists on all nodes where cluster components are installed.

D. /srv/BigData/journalnode: The JournalNode service is a critical component for HDFS NameNode HA and is deployed across the control node quorum (typically three nodes) to store edit logs.

1. Huawei FusionInsight HD 6.5.1 Installation Guide, Section: "Planning the Cluster" -> "HA Mode". This section details the deployment topology, specifying that OMS is deployed in active/standby mode on two management nodes, while JournalNode and ZooKeeper are deployed on three management nodes. This confirms that at least one control node will not host the OMS service.

2. Huawei FusionInsight HD 6.5.1 Installation Guide, Section: "Planning Disks" -> "Disk Partitions and Mount Points (Management Nodes)" and "Disk Partitions and Mount Points (Data Nodes)". These tables explicitly show that /srv/BigData/dbdataom is a partition for management nodes (hosting OMS) and /srv/BigData/hadoop/data partitions are for data nodes, confirming the role-based separation of these storage paths.

In a Huawei FusionInsight Hadoop cluster, management nodes host the core services required for cluster operation, management, and coordination. The directory /srv/BigData is the standard base installation path and is typically a dedicated partition. Management nodes run critical components such as the Operations Manager (OMS), ZooKeeper, and HDFS JournalNode (for NameNode High Availability). Therefore, the data directories for these services—/srv/BigData/dbdataom (OMS database), /srv/BigData/zookeeper (ZooKeeper data), and /srv/BigData/journalnode (JournalNode data)—are expected to exist on the management nodes.

E. /srv/BigData/hadoopdata5: This directory naming convention is used for HDFS DataNode storage. DataNodes are worker nodes that store the actual data blocks and are not deployed on management nodes.

1. Huawei FusionInsight HD 6.5.1 Installation Guide, Section: "Planning Disks and Directories". This official guide details the disk and directory planning for different node types. It specifies that management nodes host services like OMS, ZooKeeper, and JournalNode, requiring partitions for their respective data directories (/srv/BigData/dbdataom, /srv/BigData/zookeeper, /srv/BigData/journalnode). It explicitly assigns DataNode data directories (/srv/BigData/hadoopdata[N]) to data nodes.

2. Huawei FusionInsight HD Product Documentation, Section: "System Architecture" > "Logical Architecture". The documentation describes the roles of different nodes. Management nodes are defined as hosting management and control components (like OMS, DBService, ZooKeeper, NameNode, JournalNode), while data nodes host data storage and computing components (DataNode, NodeManager). This architectural separation confirms that DataNode directories would not be on management nodes.

HBase is a distributed, column-oriented NoSQL database built on top of HDFS. It is specifically designed for high-throughput, real-time random read and write access to massive, sparse datasets. While it can store binary data, it is not optimized for storing large, unstructured files (e.g., videos, large images, ISOs) directly in its cells. Storing large objects (megabytes or more) can severely degrade performance, particularly during region splits and data compactions. The standard architectural pattern is to store large files in HDFS and place a pointer or reference to the file's location in an HBase cell.

B: HBase is fundamentally designed for massive data scenarios, capable of scaling horizontally to manage petabytes of data across thousands of nodes.

C: HBase provides low-latency random reads and writes, making it an excellent choice for applications requiring high throughput and real-time data processing.

D: The dynamic, column-family-based schema of HBase is ideal for storing semi-structured and sparse data, where rows may have different columns.

1. Huawei HCIA-Big Data V2.0 Training Material (H13-711), Chapter 4: "Introduction to the Big Data Technology", Section: "HBase Principles". The material describes HBase as a database for real-time random access to massive structured and semi-structured data, distinguishing its role from HDFS, which is designed for storing large files.

2. George, L. (2015). HBase: The Definitive Guide, 2nd Edition. O'Reilly Media. Chapter 1, "Introduction", Section: "Where HBase Fits". The text explicitly states, "HBase is not a replacement for HDFS... If you have large binary files, for example, 100 MB images, you should store them in HDFS and put a pointer to the file in an HBase cell." (p. 12).

3. Huawei FusionInsight HD 6.5.1 Product Documentation, "Developer Guide > HBase Development Guide > Introduction to HBase > Application Scenarios". The documentation outlines that typical HBase scenarios include high-throughput writes, real-time queries on massive data, and storage of structured/semi-structured data. It does not list large file storage as a primary use case.

Hadoop Distributed File System (HDFS) is architected for large-scale, data-intensive applications. Its design prioritizes high-throughput, streaming access to very large datasets (gigabytes to terabytes) over low-latency access. A core feature is high fault tolerance, achieved by replicating data blocks across multiple nodes in a cluster, ensuring data availability even if some nodes fail. This makes HDFS ideal for batch processing workloads, such as those executed by MapReduce, where the system reads through massive datasets sequentially.

A. The NameNode in HDFS stores all file system metadata in memory. A large number of small files would create excessive metadata, overwhelming the NameNode and creating a performance bottleneck.

C. HDFS is optimized for high throughput (moving large amounts of data over time), not low latency (quick response to a single read request), making it unsuitable for interactive or real-time applications.

1. Huawei HCIA-Big Data V2.0 Course Material: In the module describing HDFS architecture and principles, it is explicitly stated that HDFS is suitable for scenarios requiring high throughput and fault tolerance for large files, using a streaming data access model. It also lists "not suitable for low-latency data access" and "inefficient for storing a large number of small files" as key limitations. (Reference: HCIA-Big Data V2.0 Training Material, Chapter 2: HDFS Distributed File System).

2. Official Vendor Documentation (Apache Hadoop): The official Apache Hadoop documentation for HDFS architecture outlines its core assumptions and goals. It states, "HDFS is built around the idea that the most efficient data processing pattern is a write-once/read-many-times pattern... A HDFS application needs high throughput data access... It is not a low-latency data access filesystem." (Reference: Apache Hadoop Documentation, HDFS Architecture Guide, Section: "Assumptions and Goals").

3. Academic Publication (Inspiration for HDFS): The design of HDFS was heavily influenced by Google's GFS. The original paper states, "The system is built from inexpensive commodity components that often fail... It must provide high aggregate throughput to many clients." This highlights the goals of fault tolerance and high throughput. (Reference: Ghemawat, S., Gobioff, H., & Leung, S. T. (2003). The Google file system. ACM SIGOPS operating systems review, 37(5), 29-43. Section 2.1, "Assumptions". DOI: https://doi.org/10.1145/1165389.945450).

For a production-ready and highly available FusionInsight cluster, specific node counts are recommended. Management nodes are deployed in a two-node active/standby configuration to prevent a single point of failure (SPOF). A minimum of three control nodes is required to establish a ZooKeeper quorum, which is essential for the high availability (HA) of other critical services like HDFS NameNode and YARN ResourceManager. Finally, a minimum of three data nodes is necessary to accommodate the default HDFS data block replication factor of three, ensuring data reliability and fault tolerance across different physical nodes.

A. A single management node is a SPOF, and two control/data nodes are insufficient for standard ZooKeeper quorum and HDFS replication.

B. A single management node is not recommended for a highly available cluster as it creates a single point of failure.

C. A single data node cannot satisfy the default HDFS replication factor of three, leading to under-replicated data blocks.

1. Huawei FusionInsight HD 6.5.1 Product Documentation, "Cluster Planning > Node Planning". Document ID: 3150-01. This document explicitly states: "Management nodes are deployed in active/standby mode. Therefore, two management nodes need to be planned... To ensure high availability, it is recommended that you deploy at least three control nodes... To ensure data reliability, HDFS stores three replicas of data by default. Therefore, at least three data nodes need to be deployed."

2. Huawei FusionInsight HD Administrator Guide, "System Architecture > High Availability (HA) Design", Section 2.3. This section details the necessity of two management nodes for the OMS active/standby mechanism and the requirement of an odd number of nodes (minimum three) for the ZooKeeper quorum, which underpins the cluster's overall HA strategy.

3. Huawei FusionInsight HD Technical White Paper, "HDFS Architecture and Principles", Section 3.2 "Data Replication". This paper explains that the default replication factor (dfs.replication) is set to 3 to ensure data durability against node failures, making a minimum of three DataNodes a prerequisite for a standard deployment.

The statement is correct. Kerberos is a network authentication protocol designed to provide strong, secure authentication for clients and services in a distributed network environment. In the context of a big data cluster, such as one managed by Huawei's FusionInsight, Kerberos is the fundamental mechanism used to authenticate users and services (e.g., HDFS, YARN, HBase) to each other. It prevents unauthorized access and ensures that communication within the cluster is secure by using a system of tickets issued by a trusted third-party, the Key Distribution Center (KDC). The "R" in the question is likely a typographical error and does not change the validity of the core statement about Kerberos's function.

B. wrong: This option is incorrect because the statement accurately describes the primary function of the Kerberos protocol within a distributed computing cluster, which is to provide security authentication.

1. Huawei FusionInsight HD 6.5.1 Administrator Guide, Chapter 10: Security Management, Section: Security Mode. The document states, "In security mode, Kerberos authentication is enabled for all components... Kerberos is a network authentication protocol. It is designed to provide strong authentication for client/server applications by using secret-key cryptography." This confirms Kerberos's role in authenticating cluster services.

2. Huawei FusionInsight HD 6.5.1 Solution Description, Chapter 3: Architecture, Section: Security Architecture. This document outlines the security design, specifying, "FusionInsight HD uses Kerberos for unified identity authentication of users." This highlights Kerberos as the central authentication component.

3. Apache Hadoop 3.3.4 Documentation, Section: "Hadoop in Secure Mode". The official documentation for Hadoop, the foundation of many big data platforms, explains, "When Hadoop is configured to be secure, user and service authentication is performed by Kerberos." This establishes the standard industry use of Kerberos for authenticating services in a cluster.

FusionInsight Manager's scheduled backup functionality is critical for disaster recovery. It is designed to back up all essential configuration and metadata required to restore the cluster's operational state. The backup includes:

1. Manager's own data: This encompasses the O&M server (OMs) configuration files and the DBService database, which stores cluster configurations, monitoring data, and alarms.

2. Security component data: This includes the LDAP database containing user and group information, which is vital for authentication and authorization.

3. Core component metadata: This includes the HDFS NameNode metadata (fsimage and edits logs), which is essential for recovering the HDFS file system.

Therefore, all listed components are part of the regular backup scope. Note: "OMs (orthophosphoric acid case)" contains a typographical error; "OMs" correctly refers to the Operation and Management server.

This section is not applicable as all provided options are correct. Each listed component is a critical part of the data backed up by FusionInsight Manager for cluster recovery.

1. Huawei FusionInsight HD Administrator Guide. In the "Backup and Restore" chapter, under the section "Backup," the documentation specifies the content that is backed up. It explicitly lists Manager data (which includes DBService and OMs configurations), metadata of basic services like HDFS (NameNode), and security component data like LDAP. This confirms that all four options are part of the standard backup procedure. (Reference: FusionInsight HD V100R002C80SPC200 Administrator Guide, Chapter 10: Backup and Restore).

2. Huawei FusionInsight HD Product Documentation. The section on System Reliability and O&M describes the backup and restoration mechanism. It details that to ensure business continuity, the Manager provides a one-click backup of key data, including the Manager's own database, component metadata (HDFS, Yarn, etc.), and security information (LDAP, Kerberos). This comprehensive approach ensures a reliable recovery point for the cluster. (Reference: FusionInsight HD Product Documentation, Section: Reliability > Backup and Restoration).

In Apache Spark, the Directed Acyclic Graph (DAG) scheduler is responsible for dividing a job into a set of stages. The basis for this division is the type of dependency between Resilient Distributed Datasets (RDDs). When a transformation requires a data shuffle across the cluster, it is known as a wide dependency. The DAG scheduler creates a new stage at each wide dependency boundary. Transformations with narrow dependencies, where data can be processed on the same partition without shuffling, are grouped (pipelined) together into a single stage. This approach optimizes execution by minimizing costly network data transfer.

A. task: A task is the smallest unit of work executed on a single partition within a stage, not the criterion for creating a stage.

B. taskSet: A TaskSet is a collection of tasks that constitute a single stage. It is the result of stage division, not the cause.

C. action: An action (e.g., count, collect) triggers the execution of a job, which is then broken down into one or more stages.

1. Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., & Stoica, I. (2012). Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing. Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation (NSDI'12). USENIX Association, 2.

Reference Details: Section 3.2, "Job Scheduling," explains: "Our scheduler is similar to Dryad’s... However, it pipelines functions with narrow dependencies into a single stage... For example, a map followed by a filter can be performed in a single pass over the data... It creates a new stage at each wide dependency." This directly states that wide dependencies (shuffles) are the basis for stage creation.

2. Huawei HCIP-Big Data Developer V2.0 Training Material.

Reference Details: In the "Spark Core Principle" module, the section on the "DAGScheduler" details the process of stage division. It explicitly states that the DAGScheduler scans the RDD graph backwards from the final RDD, and whenever it encounters a shuffle dependency (wide dependency), it creates a new stage for the RDDs involved in that shuffle.

3. Karau, H., Konwinski, A., Wendell, P., & Zaharia, M. (2015). Learning Spark: Lightning-Fast Big Data Analysis. O'Reilly Media, Inc.

Reference Details: Chapter 4, "Spark Programming Model," in the section "How Spark Executes Applications," describes the role of the DAG scheduler. It clarifies that the scheduler groups RDDs into stages based on whether a shuffle is required, with shuffle operations marking the boundary between stages.

The dual-plane isolation networking scheme in Huawei FusionInsight HD is a fundamental architecture designed to separate different types of network traffic, typically management traffic and service traffic, onto physically distinct networks. This separation is crucial for security, performance, and reliability. To implement this physical isolation, each node in the cluster must be equipped with at least two physical Network Interface Cards (NICs). One NIC is dedicated to the management plane, and the other is dedicated to the service plane. A single NIC cannot provide the required physical separation, thus making the statement false.

A. True: This is incorrect. A single physical NIC represents a single point of failure and cannot achieve the physical network isolation that is the primary purpose of a dual-plane networking scheme in an enterprise-grade system like FusionInsight HD.

1. Huawei FusionInsight HD 6.5.1 Product Documentation - Network Planning: In the section describing network plane design, it is explicitly stated that for physical network isolation schemes (such as dual-plane networking), nodes must be configured with multiple NICs. The documentation details that in a typical dual-plane scenario, one NIC connects to the management plane switch, and another NIC connects to the service plane switch. This requirement for at least two physical NICs is a prerequisite for the architecture.

2. Huawei FusionInsight HD Administrator Guide: The guide's section on network configuration and management outlines the different network planes (management, service, floating IP). It implicitly supports the need for multiple physical interfaces to map these logical planes to physically isolated hardware for achieving the recommended high-availability and security postures. The configuration examples for dual-plane networking consistently show mappings to separate physical interfaces (e.g., eth0 for management, eth1 for service).