Accessing Hadoop Data Using Hive Cognitive Class Exam Quiz Answers

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Enroll Here: Accessing Hadoop Data Using Hive Cognitive Class Exam Quiz Answers

Accessing Hadoop Data Using Hive Cognitive Class Certification Answers

Module 1 – Introduction to Hive Quiz Answers – Cognitive Class

Question 1: Which company first developed Hive?

• Starbucks
• Facebook
• HP
• Yahoo

Question 2: Hive is a Data Warehouse system built on top of Hadoop. True or false?

• True
• False

Question 3: Which of the following is NOT a valid Hive Metastore config?

• Server Metastore
• Local Metastore
• Remote Metastore
• Embedded Metastore

Module 2 – Hive DDL Quiz Answers – Cognitive Class

Question 1: Which of the following commands will list the databases in the Hive system?

• DISPLAY ALL DB;
• SHOW ME THE DATABASES;
• DISPLAY DB;
• SHOW DATABASES;

Question 2: MAPS are a Hive complex data type. True or false?

• True
• False

Question 3: An index can be created on a Hive table. True or false?

• True
• False

Module 3 – Hive DML Quiz Answers – Cognitive Class

Question 1: LOAD DATA LOCAL means that the data should be loaded from HDFS. True or false?

• True
• False

Question 2: Which of the following commands is used to generate a Hive query plan?

• QUERYPLAN
• SHOWME
• HOW
• EXPLAIN

Question 3: Data can be exported out of Hive. True or false?

• True
• False

Module 4 – Hive Operators and Function Quiz Answers – Cognitive Class

Question 1: Which of the following is NOT a built-in Hive function?

• triplemultiple
• floor
• upper
• round

Question 2: Users can create their own custom user defined functions. True or false?

• True
• False

Question 3: Which of the following is NOT a valid Hive relational operator?

• A ATE B
• A IS NOT NULL
• A LIKE B
• A IS NULL

Accessing Hadoop Data Using Hive Final Exam Answers – Cognitive Class

Question 1: What is the primary purpose of Hive in the Hadoop architecture?

• To provide logging support for Hadoop jobs
• To support the execution of workflows consisting of a collection of actions
• To support SQL-like queries of data stored in Hadoop in place of writing MapReduce applications
• To move data into HDFS

Question 2: Hive is SQL-92 compliant and supports row-level inserts, updates, and deletes. True or false?

• True
• False

Question 3: In a production setting, you should configure the Hive metastore as

• Remote
• Local
• Embedded
• None of the above

Question 4: The Hive Command Line Interface (CLI) allows you to

• retrieve query explain plans
• view and manipulate table metadata
• perform queries, DML, and DDL
• All of the above

Question 5: When using the Hive CLI, which option allows you to execute HiveQL that’s saved in a text file?

• hive -d
• hive -S
• hive -e
• hive -f

Question 6: Which statement is true of “Managed” tables in Hive?

• Dropping a table deletes the table’s metadata, NOT the actual data
• You can easily share your data with other Hadoop tools
• Table data is stored in a directory outside of Hive
• None of the Above

Question 7: Hive Data Types include

• Maps
• Arrays
• Structs
• A subset of RDBMS primitive types
• All of the Above

Question 8: The PARTITION BY clause in Hive can be used to improve performance by storing all the data associated with a specified column’s value in the same folder. True or false?

• True
• False

Question 9: The LOAD DATA LOCAL command in Hive is used to move a datafile in HDFS into a Hive table structure. True or false?

• True
• False

Question 10: The INSERT OVERWRITE LOCAL DIRECTORY command in Hive is used to

• copy data into an externally managed table
• load data into a Hive Table
• append rows to an existing Hive Table
• export data from Hive to the local file system

Question 11: Hive supports which type of join?

• Left Semi-Join
• Inner Join
• Full Outer Join
• Equi-join
• All of the Above

Question 12: With Hive, you can write your own user defined functions in Java and invoke them using HiveQL. True or false?

• True
• False

Question 13: Which of the following is a valid Hive operator for complex data types?

• S.x where S is a struct and x is the name of the field you wish to retrieve
• M[k] where M is a map and k is a key value
• A[n] where A is an array and n is an int
• All of the above

Introduction to Accessing Hadoop Data Using Hive

Accessing Hadoop data using Hive is a powerful way to query and analyze large datasets stored in Hadoop Distributed File System (HDFS) using a SQL-like interface. Here’s a basic introduction to getting started with accessing Hadoop data using Hive:

1. Understanding Hive: Hive is a data warehousing infrastructure built on top of Hadoop. It provides a mechanism to project structure onto the data and query the data using a SQL-like language called HiveQL. This makes it easier for users who are familiar with SQL to interact with Hadoop data.
2. Installation and Setup: To start using Hive, you need to have Hadoop installed and configured on your system. Once Hadoop is set up, you can install Hive by downloading it from the Apache Hive website and following the installation instructions provided.
3. Creating Tables: Hive organizes data into tables, which can be created using either HiveQL or by importing existing data from Hadoop. You can define the schema of the table (columns and their data types) during table creation.
4. Loading Data: After creating a table, you can load data into it from Hadoop. This can be done using various methods, such as using HiveQL LOAD DATA command, copying data from Hadoop file system, or by importing data from external sources.
5. Querying Data: Once data is loaded into Hive tables, you can query it using HiveQL queries. HiveQL is similar to SQL, so users familiar with SQL can easily write queries to analyze the data. Queries can include filtering, aggregation, joins, and other SQL operations.
6. Optimizing Queries: Hive optimizes queries to run efficiently on Hadoop by converting them into MapReduce or Tez jobs. However, you can further optimize queries by partitioning tables, using appropriate data formats, and configuring Hive settings based on your use case.
7. Integration with Hadoop Ecosystem: Hive integrates with other components of the Hadoop ecosystem, such as HDFS, HBase, and Spark. This allows you to seamlessly work with data stored in different formats and systems within the Hadoop ecosystem.
8. Security and Access Control: Hive provides security features such as authentication, authorization, and encryption to ensure that data is accessed only by authorized users and applications. You can configure security settings based on your organization’s requirements.
9. Monitoring and Management: Hive comes with tools for monitoring query performance, resource usage, and job execution. You can use these tools to optimize queries, troubleshoot issues, and manage Hive resources effectively.
10. Advanced Features: Hive supports advanced features such as user-defined functions (UDFs), custom serialization formats, and integration with external tools and libraries. These features extend the capabilities of Hive and enable you to handle complex data processing tasks.

By following these steps and exploring the features of Hive, you can effectively access and analyze Hadoop data using a familiar SQL-like interface.