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Mapr (HP) HBase Developer Certification Questions and Answers (Dumps and Practice Questions)



Question : MapR-DB is faster than HBase ?
: MapR-DB is faster than HBase ?
1. True
2. False

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Explanation: MapR-DB is built on the core MapR platform services layer that set records on both the TeraSort and the MinuteSort benchmarks. Recently,

MapR-DB ran over 30,000 batch put operations per second on one node, and showed as much as an elevenfold speed advantage over HBase

With its in-memory feature, MapR-DB can store a database in memory for additional performance gains




Question : Which all are the key-features of MapR-DB
A. Multi-master, real-time table replication
B. Row/document-level ACID transactions
C. Kerberos/LDAP integration, and easy-to-configure native authentication
D. HA/DR, instant recovery, and point-in-time recovery
: Which all are the key-features of MapR-DB
1. A,B,C
2. B,C,D
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4. A,D
5. A,B,C,D

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Explanation: Zero Downtime High-availability (HA). The MapR architecture eliminates single points of failure, avoiding data and job loss even upon multiple node failures
in the cluster. HA for MapR-DB leverages
the same data replication system used for Hadoop files.
Instant recovery. Upon node failure, a replica instantly takes over for the failed node without the failover lag seen in other distributions.
Disaster recovery (DR). Multi-master, real-time table replication enables distributed applications on global data while reducing the risk for data loss in DR scenarios.
Point-in-time recovery. Consistent snapshots instantly mark database tables at a specific time to recover from accidental deletions, overwrites, or corruption. Only MapR enables
exact recovery, even for files and database tables that are open at the time of the snapshot.
Integrated database operations. Database operations are efficiently run in the underlying, core MapR platform services layer, so no extra servers and administration are required.
Automatic optimizations. MapR-DB handles region splits (i.e., sharding) automatically and eliminates compaction (defragmentation) delays. And unlike other in-Hadoop databases,
MapR-DB is self-optimizing and does not require application-level database administration code.
Built-in HA/DR. The HA/DR capabilities of the MapR Converged Data Platform also include MapR-DB data. The MapR Control System (MCS) handles cluster administration as well as
database-specific administration such as creating and modifying tables. A command line interface (CLI) and REST API are also available for administration.
Access controls. Access Control Expressions (ACEs) control permissions at various levels including column and sub-document by a combination of user, group, and role.
Kerberos and LDAP integration. MapR-DB can authenticate users with Kerberos and/or LDAP. Native authentication. MapR also offers a standards-based authentication system as a simpler
alternative to Kerberos that leverages Linux Pluggable Authentication Modules (PAM) to provide the widest registry support.
Comprehensive auditing. MapR-DB auditing logs help to analyze user behavior as well as to meet regulatory compliance requirements. MapR-DB uses the JSON format to log accesses at
various levels including the column and sub-document levels. MapR also audits at the administrative, authentication, and file levels.
MapR-DB is built on the core MapR platform services layer that set records on both the TeraSort and the MinuteSort benchmarks. Recently, MapR-DB ran over 30,000 batch put operations
per second on one node, and showed as much as an elevenfold speed advantage over HBase. With its in-memory feature, MapR-DB can store a database in memory for additional performance
gains.
Auto-tuning and data structure innovations ensure consistent low latency, even at the 95th and 99th percentile latency measurements. MapR (in red on the graph) consistently responds
quickly, while the other distribution (in blue) shows many high spikes of low latency due to suboptimal disk cleanup.
Extreme Scalability : Users can scale out their MapR clusters linearly and incrementally, and manage data sets with millions of columns across trillions of rows.






Question :

From within an HBase application, you would like to create a new table named weblogs. You have started with
the following Java code:
HBaseAdmin admin = new HBaseAdmin (conf);
HTableDescriptor t = new HTableDescriptor("weblogs");
Which of the following method(s) would you use next?

:
1. admin.createTable(t);admin.enable.Table(t);
2. admin.createTable(t);
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4. HTable.createTable(t);




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Creating a table in HBase
public void createTable (String tablename, String familyname) throws IOException { Configuration conf =
HBaseConfiguration.create();
HBaseAdmin admin = new HBaseAdmin(conf);
HTableDescriptor tabledescriptor = new HTableDescriptor(Bytes.toBytes(tablename)); tabledescriptor.
addFamily(new HColumnDescriptor (familyname)); admin.createTable(tabledescriptor);
}


Related Questions

Question : MapR-FS stores data in abstract entities called _______ that allow for _____________.

: MapR-FS stores data in abstract entities called _______ that allow for _____________.
1. containers, random read access

2. containers, random write access

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4. box, random read access


Question : Each tablet of a table, along with its corresponding write-ahead log (WAL) files, b-trees, and other associated structures, is stored in _______ container.

 : Each tablet of a table, along with its corresponding write-ahead log (WAL) files, b-trees, and other associated structures, is stored in _______ container.
1. one

2. Two

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4. more than one


Question : Whta is/are advantages of storing tables in conatiners.

A. Scalability of MapR clusters
B. High availability
C. Faster data read process
D. Faster data write process
 : Whta is/are advantages of storing tables in conatiners.
1. A,B
2. B,C
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4. A,D
5. B,D


Question : MapR provides volumes as a way to organize data and manage cluster performance, Containers are stored in volumes in MapR-FS. Volumes are used to enforce

A. disk usage limits
B. set replication levels
C. define snapshots and mirrors
D. establish ownership and accountability
 : MapR provides volumes as a way to organize data and manage cluster performance, Containers are stored in volumes in MapR-FS. Volumes are used to enforce
1. A,B,C
2. B,C,D
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4. A,B,C,D


Question : In MapR-DB, You can also isolate work environments for different database users or applications and place MapR-DB tables on specific hardware for better
performance or load isolation

 : In MapR-DB, You can also isolate work environments for different database users or applications and place MapR-DB tables on specific hardware for better
1. True
2. False


Question :

While schema design which of the following is valid point to keeping StoreFile indices small..



 :
1. Keep ColumnFamily names as small as possible
2. Avoid long verbose attribute names
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4. All 1,2 and 3 are correct
5. Only 1 and 3 are correct