Moving Data into Hadoop Cognitive Class Exam Quiz Answers

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Enroll Here: Moving Data into Hadoop Cognitive Class Exam Quiz Answers

Moving Data into Hadoop Cognitive Class Certification Answers

Module 1 – Lord Scenario Quiz Answers – Cognitive Class

Question 1: What is Data at rest?

• Data that is being transferred over
• Data that is already in a file in some directory
• Data that hasn’t been used in a while
• Data that needs to be copied over

Question 2: Data can be moved using BigSQL Load. True or false?

• True
• False

Question 3: Which of the following does not relate to Flume?

• Pipe
• Sink
• Interceptors
• Source

Module 2 – Using Sqoop Quiz Answers – Cognitive Class

Question 1: Sqoop is designed to

• export data from HDFS to streaming software
• read and understand data from a relational database at a high level
• prevent “bad” data in a relational database from going into Hadoop
• transfer data between relational database systems and Hadoop

Question 2: Which of the following is NOT an argument for Sqoop?

• –update-key
• –split-from
• –target-dir
• –connect

Question 3: By default, Sqoop assumes that it’s working with space-separated fields and that each record is terminated by a newline. True or false?

• True
• False

Module 3 – Flume Overview Quiz Answers – Cognitive Class

Question 1: Avro is a remote procedure call and serialization framework, developed within a separate Apache project. True or false?

• True
• False

Question 2: Data sent through Flume

• may have different batching but must be in a constant stream
• may have different batching or a different reliability setup
• must be in a particular format
• has to be in a constant stream

Question 3: A single Avro source can receive data from multiple Avro sinks. True or false?

• True
• False

Module 4 – Using Flume Quiz Answers – Cognitive Class

Question 1: Which of the following is NOT a supplied Interceptor?

• Regex extractor
• Regex sinker
• HostType
• Static

Question 2: Channels are:

• where the data is staged after having been read in by a source and not yet written out by a sink
• where the data is staged after having been read in by a sink and not yet written out by a source
• where the data is staged after having been written in by a source and not yet read out by a sink
• where the data is staged after having been written in by a sink and not yet written out by a source

Question 3: One property for sources is selector.type? True or false?

• True
• False

Moving Data into Hadoop Final Exam Answers – Cognitive Class

Question 1: The HDFS copyFromLocal command can be used to

• capture streaming data that you want to store in Hadoop
• ensure that log files which are actively being used to capture logging from a web server are moved into Hadoop
• move data from a relational database or data warehouse into Hadoop
• None of the above

Question 2: What is the primary purpose of Sqoop in the Hadoop architecture?

• To “catch” logging data as it is written to log files and move it into Hadoop
• To schedule scripts that can be run periodically to collect data into Hadoop
• To import data from a relational database or data warehouse into Hadoop
• To move static files from the local file system into HDFS
• To stream data into Hadoop

Question 3: A Sqoop JDBC connection string must include

• the name of the database you wish to connect to
• the hostname of the database server
• the port that the database server is listening on
• the name of the JDBC driver to use for the connection
• All of the above

Question 4: Sqoop can be used to either import data from relational tables into Hadoop or export data from Hadoop to relational tables. True or false?

• True
• False

Question 5: When importing data via Sqoop, the imported data can include

• a collection of data from multiple tables via a join operation, as specified by a SQL query
• specific rows and columns from a specific table
• all of the data from a specific table
• All of the Above

Question 6: When importing data via Sqoop, the incoming data can be stored as

• Serialized Objects
• JSON
• XML
• None of the Above

Question 7: Sqoop uses MapReduce jobs to import and export data, and you can configure the number of Mappers used. True or false?

• True
• False

Question 8: What is the primary purpose of Flume in the Hadoop architecture?

• To “catch” logging data as it is written to log files and move it into Hadoop
• To schedule scripts that can be run periodically to collect data into Hadoop
• To import data from a relational database or data warehouse into Hadoop
• To move static files from the local file system into HDFS
• To stream data into Hadoop

Question 9: When you create the configuration file for a Flume agent, you must configure

• an Interceptor
• a Sink
• a Channel
• a Source
• All of the above

Question 10: When using Flume, a Source and a Sink are “wired together” using an Interceptor. True or false?

• True
• False

Question 11: Flume agents can run on multiple servers in the enterprise, and they can communicate with each other over the network to move data. True or false?

• True
• False

Question 12: Possible Flume channels include

• The implementation of your own channel
• File Storage
• Database Storage
• In Memory
• All of the Above

Question 13: Flume provides a number of source types including

• Elastic Search
• HBase
• Hive
• HDFS
• None of the Above

Question 14: Flume agent configuration is specified using

• CSV
• a text file, similar to the Java.properties format
• JSON
• XML, similar to Sqoop configuration

Question 15: To pass data from a Flume agent on one node to another, you can configure an Avro sink on the first node and an Avro source on the second. True or false?

• True
• False

Introduction to Moving Data into Hadoop

Moving data into Hadoop involves several steps and considerations to ensure efficient and effective data storage and processing within the Hadoop ecosystem. Here’s an introduction to the process:

1. Understanding Hadoop: Hadoop is an open-source framework designed for distributed storage and processing of large datasets across clusters of commodity hardware. It consists of two main components: Hadoop Distributed File System (HDFS) for storage and MapReduce (or newer processing frameworks like Apache Spark) for processing.
2. Data Sources: Identify the sources from which you want to move data into Hadoop. These sources could include databases, log files, sensor data, social media feeds, etc.
3. Data Extraction: Extract data from the source systems using appropriate methods. This could involve using ETL (Extract, Transform, Load) tools, custom scripts, or built-in connectors provided by Hadoop ecosystem tools.
4. Data Transformation (if needed): Data may need to be transformed to fit the schema of the target system or to clean and prepare it for analysis. This step often involves data normalization, cleansing, and enrichment.
5. Data Loading into Hadoop: Once the data is extracted and transformed, it needs to be loaded into Hadoop. Hadoop provides several mechanisms for data ingestion:
   • HDFS Command Line Interface (CLI): You can use commands like hadoop fs -put to manually copy files into HDFS.
   • Hadoop File APIs: If you’re writing custom applications, you can use Hadoop’s Java APIs to interact with HDFS programmatically.
   • Sqoop: Sqoop is a tool designed specifically for transferring bulk data between Hadoop and structured data stores such as relational databases (e.g., MySQL, Oracle).
   • Flume: Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data into HDFS.
   • Kafka: Kafka can be used for real-time data ingestion into Hadoop, particularly useful for streaming data scenarios.
6. Data Replication and Backup: Consider data replication and backup strategies within the Hadoop cluster to ensure data reliability and fault tolerance. Hadoop’s default replication factor is 3, meaning each block of data is replicated across three different nodes for fault tolerance.
7. Data Access and Processing: Once the data is in Hadoop, you can leverage various processing frameworks such as MapReduce, Apache Spark, Apache Hive, Apache Pig, etc., to analyze and derive insights from the data.
8. Monitoring and Maintenance: Regularly monitor the data ingestion process, Hadoop cluster health, and data quality. Perform necessary maintenance tasks such as data compaction, node addition/removal, and software upgrades to ensure smooth operation.

By following these steps, you can efficiently move data into Hadoop and leverage its capabilities for storing, processing, and analyzing large datasets.