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NEW QUESTION NO: 5

The hdfs_streamscript is used by the Oracle SQL Connector for HDFS to perform a specific task to access data.
What is the purpose of this script?
A. It is the preprocessor script for the Impala table.
B. It is the preprocessor script for the HDFS external table.
C. It is the streaming script that creates a database directory.
D. It is the preprocessor script for the Oracle partitioned table.
E. It defines the jar file that points to the directory where Hive is installed.
Answer: B
Explanation/Reference:
The hdfs_stream script is the preprocessor for the Oracle Database external table created by Oracle SQL Connector for HDFS.
References: https://docs.oracle.com/cd/E37231_01/doc.20/e36961/start.htm#BDCUG107

NEW QUESTION NO: 6
Your customer's security team needs to understand how the Oracle Loader for Hadoop Connector writes data to the Oracle database.
Which service performs the actual writing?
A. OLH agent
B. reduce tasks
C. write tasks
D. map tasks
E. NameNode
Answer: B
Explanation/Reference:
Oracle Loader for Hadoop has online and offline load options. In the online load option, the data is both preprocessed and loaded into the database as part of the Oracle Loader for Hadoop job. Each reduce task makes a connection to Oracle Database, loading into the database in parallel. The database has to be available during the execution of Oracle Loader for Hadoop.
References: http://www.oracle.com/technetwork/bdc/hadoop-loader/connectors-hdfs-wp-1674035.pdf

NEW QUESTION NO: 7
You recently set up a customer's Big Data Appliance. At the time, all users wanted access to all the Hadoop data. Now, the customer wants more control over the data that is stored in Hadoop.
How should you accommodate this request?
A. Configure Audit Vault and Database Firewall protection policies for the Hadoop data.
B. Update the MySQL metadata for Hadoop to define access control lists.
C. Configure an /etc/sudoers file to restrict the Hadoop data.
D. Configure Apache Sentry policies to protect the Hadoop data.
Answer: D
Explanation/Reference:
Apache Sentry is a new project that delivers fine grained access control; both Cloudera and Oracle are the project's founding members. Sentry satisfies the following three authorization requirements:
Secure Authorization: the ability to control access to data and/or privileges on data for authenticated users.
Fine-Grained Authorization: the ability to give users access to a subset of the data (e.g. column) in a database
Role-Based Authorization: the ability to create/apply template-based privileges based on functional roles.
Incorrect Answers:
C: The file /etc/sudoers contains a list of users or user groups with permission to execute a subset of commands while having the privileges of the root user or another specified user. The program may be configured to require a password.
References: https://blogs.oracle.com/datawarehousing/new-big-data-appliance-security-features

NEW QUESTION NO: 8
How is Oracle Loader for Hadoop (OLH) better than Apache Sqoop?
A. OLH performs a great deal of preprocessing of the data on Hadoop before loading it into the database.
B. OLH performs a great deal of preprocessing of the data on the Oracle database before loading it into NoSQL.
C. OLH does not use MapReduce to process any of the data, thereby increasing performance.
D. OLH performs a great deal of preprocessing of the data on the Oracle database before loading it into Hadoop.
E. OLH is fully supported on the Big Data Appliance. Apache Sqoop is not supported on the Big Data Appliance.
Answer: A
Explanation/Reference:
Oracle Loader for Hadoop provides an efficient and high-performance loader for fast movement of data from a Hadoop cluster into a table in an Oracle database. Oracle Loader for Hadoop prepartitions the data if necessary and transforms it into a database-ready format. It optionally sorts records by primary key or user-defined columns before loading the data or creating output files.
Note: Apache Sqoop(TM) is a tool designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases.
Incorrect Answers:
A, D: Oracle Loader for Hadoop provides an efficient and high-performance loader for fast movement of data from a Hadoop cluster into a table in an Oracle database.
C: Oracle Loader for Hadoop is a MapReduce application that is invoked as a command-line utility. It accepts the generic command-line options that are supported by the org.apache.hadoop.util.Tool interface.
E: The Oracle Linux operating system and Cloudera's Distribution including Apache Hadoop (CDH) underlie all other software components installed on Oracle Big Data Appliance. CDH includes Apache projects for MapReduce and HDFS, such as Hive, Pig, Oozie, ZooKeeper, HBase, Sqoop, and Spark.
References:
https://docs.oracle.com/cd/E37231_01/doc.20/e36961/start.htm#BDCUG326
https://docs.oracle.com/cd/E55905_01/doc.40/e55814/concepts.htm#BIGUG117

NEW QUESTION NO: 9
Which statement is true about the NameNode in Hadoop?
A. A query in Hadoop requires a MapReduce job to be run so the NameNode gets the location of the data from the JobTracker.
B. If the NameNode goes down and a secondary NameNode has not been defined, the cluster is still accessible.
C. When loading data, the NameNode tells the client or program where to write the data.
D. All data passes through the NameNode; so if it is not sized properly, it could be a potential bottleneck.
Answer: B
Explanation/Reference:
Note that, in an HA cluster, the Standby NameNode also performs checkpoints of the namespace state, and thus it is not necessary to run a Secondary NameNode, CheckpointNode, or BackupNode in an HA cluster. In fact, to do so would be an error.
In a typical HA cluster, two separate machines are configured as NameNodes. At any point in time, exactly one of the NameNodes is in an Active state, and the other is in a Standby state.
Note that, in an HA cluster, the Standby NameNode also performs checkpoints of the namespace state, and thus it is not necessary to run a Secondary NameNode, CheckpointNode, or BackupNode in an HA cluster.
References: https://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/ HDFSHighAvailabilityWithNFS.html

NEW QUESTION NO: 10
How should you encrypt the Hadoop data that sits on disk?
A. Enable Transparent Data Encryption by using the Mammoth utility.
B. Enable HDFS Transparent Encryption by using bdaclion a Kerberos-secured cluster.
C. Enable HDFS Transparent Encryption on a non-Kerberos secured cluster.
D. Enable Audit Vault and Database Firewall for Hadoop by using the Mammoth utility.
Answer: B
Explanation/Reference:
HDFS Transparent Encryption protects Hadoop data that's at rest on disk. When the encryption is enabled for a cluster, data write and read operations on encrypted zones (HDFS directories) on the disk are automatically encrypted and decrypted. This process is "transparent" because it's invisible to the application working with the data.
The cluster where you want to use HDFS Transparent Encryption must have Kerberos enabled.
Incorrect Answers:
D: The cluster where you want to use HDFS Transparent Encryption must have Kerberos enabled.
References: https://docs.oracle.com/en/cloud/paas/big-data-cloud/csbdi/using-hdfs-transparent- encryption.html#GUID-16649C5A-2C88-4E75-809A-BBF8DE250EA3

NEW QUESTION NO: 11
What does the following line do in Apache Pig?
products = LOAD '/user/oracle/products' AS (prod_id, item);
A. The productstable is loaded by using data pump with prod_idand item.
B. The LOADtable is populated with prod_idand item.
C. The contents of /user/oracle/productsare loaded as tuples and aliased to products.
D. The contents of /user/oracle/productsare dumped to the screen.
Answer: C
Explanation/Reference:
The LOAD function loads data from the file system.
Syntax: LOAD 'data' [USING function] [AS schema];
Terms: 'data'
The name of the file or directory, in single quote
References: https://pig.apache.org/docs/r0.11.1/basic.html#load

NEW QUESTION NO: 12
Your customer wants you to set up ODI by using the IKM SQL to Hive module and overwrite existing Hive tables with the customer's new clinical data.
What parameter must be set to true?
A. SQOOP_ODI_OVERWRITE
B. OVERWRITE_HIVE_TABLE
C. CREATE_HIVE_TABLE
D. OVERWRITE_HDFS_TABLE
Answer: C

NEW QUESTION NO: 13
Your customer is worried that the redundancy of HDFS will not meet its needs. The customer needs to store certain files with higher levels of redundancy than other files.
Which architectural feature of HDFS should you choose?
A. Apache Impala, which can be used to set the duplex level for each file
B. Automatic Storage Management, which can be used to multiplex the data
C. Apache Scala on top of MapReduce, which allows you to store files at various redundancies
D. the replication factor, which can be set at the file level
Answer: D
Explanation/Reference:
You can change the replication factor on a per-file basis using the Hadoop FS shell.
To set replication of an individual file to 4:
./bin/hadoop dfs -setrep -w 4 /path/to/file
Incorrect Answers:
A: Cloudera Impala is Cloudera's open source massively parallel processing (MPP) SQL query engine for data stored in a computer cluster running Apache Hadoop.
C: Apache Scala is a high-level programming language which is a combination of object-oriented programming and functional programming. It is highly scalable which is why it is called Scala.
References: https://sites.google.com/site/hadoopandhive/home/how-to-change-replication-factor-of- existing-files-in-hdfs

NEW QUESTION NO: 14
How should you control the Sqoop parallel imports if the data does not have a primary key?
A. by specifying no primary key with the --no-primaryargument
B. by specifying the number of maps by using the -moption
C. by indicating the split size by using the --direct-split-sizeoption
D. by choosing a different column that contains unique data with the --split-byargument
Answer: D
Explanation/Reference:
If the actual values for the primary key are not uniformly distributed across its range, then this can result in unbalanced tasks. You should explicitly choose a different column with the --split-by argument. For example, --split-by employee_id.
Note: When performing parallel imports, Sqoop needs a criterion by which it can split the workload. Sqoop uses a splitting column to split the workload. By default, Sqoop will identify the primary key column (if present) in a table and use it as the splitting column. The low and high values for the splitting column are retrieved from the database, and the map tasks operate on evenly-sized components of the total range.
References: https://sqoop.apache.org/docs/1.4.2/SqoopUserGuide.html#_importing_data_into_hbase

NEW QUESTION NO: 15
Your customer receives data in JSON format.
Which option should you use to load this data into Hive tables?
A. Python
B. Sqoop
C. a custom Java program
D. Flume
E. SerDe
Answer: E
Explanation/Reference:
SerDe is short for Serializer/Deserializer. Hive uses the SerDe interface for IO. The interface handles both serialization and deserialization and also interpreting the results of serialization as individual fields for processing.
A SerDe allows Hive to read in data from a table, and write it back out to HDFS in any custom format.
Anyone can write their own SerDe for their own data formats.
The JsonSerDe for JSON files is available in Hive 0.12 and later.

References: https://cwiki.apache.org/confluence/display/Hive/SerDe

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