Big Data Analytics with Hive, Pig & MapReduce Exam Questions and Answers

Big Data Analytics with Hive, Pig & MapReduce certification exam assessment practice question and answer (Q&A) dump including multiple choice questions (MCQ) and objective type questions, with detail explanation and reference available free, helpful to pass the Big Data Analytics with Hive, Pig & MapReduce exam and earn Big Data Analytics with Hive, Pig & MapReduce certificate.

Question 1

What is the primary role of Apache Hive in the Hadoop ecosystem?

A. Providing a data warehouse for querying and analysis
B. Managing the file system and replication in Hadoop
C. Real-time data streaming and event processing
D. Scheduling and monitoring Hadoop jobs

Answer

A. Providing a data warehouse for querying and analysis

Explanation

Apache Hive’s primary role in the Hadoop ecosystem is to act as a data warehouse layer that enables users to query and analyze large datasets stored in HDFS using SQL-like syntax. It abstracts the complexity of MapReduce by translating queries into distributed processing jobs, making Hadoop accessible to analysts and data professionals without deep programming expertise.

Question 2

Which feature of Hive simplifies query execution for analysts?

A. Native support for machine learning algorithms
B. Hive Query Language (HQL)
C. Using custom file system commands
D. Automatic indexing of unstructured data

Answer

B. Hive Query Language (HQL)

Explanation

Hive simplifies query execution for analysts through Hive Query Language (HQL), which closely resembles SQL. This familiarity allows analysts to write complex analytical queries without needing to understand low-level distributed processing concepts, significantly reducing the learning curve when working with big data.

Question 3

Hive was originally developed at which organization?

A. Facebook
B. Microsoft
C. Google
D. Amazon

Answer

A. Facebook

Explanation

Apache Hive was originally developed at Facebook to handle large volumes of structured data stored in Hadoop. The goal was to enable data analysts to run ad-hoc queries using a SQL-like language instead of writing complex MapReduce programs.

Question 4

Which command is used to create a new database in Hive?

A. USE DATABASE
B. CREATE DATABASE
C. CREATE TABLE
D. SHOW DATABASES

Answer

B. CREATE DATABASE

Explanation

The CREATE DATABASE command is used to create a new database in Hive. A database in Hive serves as a logical container for organizing tables and other objects, helping manage metadata more effectively in large environments.

Question 5

In Hive, which command allows users to switch from one database to another?

A. CREATE DATABASE
B. ALTER DATABASE
C. DROP DATABASE
D. USE

Answer

D. USE

Explanation

The USE command allows users to switch from one database to another in Hive. Once executed, all subsequent operations such as creating or querying tables apply to the selected database context.

Question 6

Why are databases in Hive useful?

A. They logically organize tables into separate namespaces
B. They automatically index all records
C. They replicate data across clusters
D. They store actual data files

Answer

A. They logically organize tables into separate namespaces

Explanation

Databases in Hive are useful because they logically group tables into separate namespaces. This improves organization, avoids naming conflicts, and supports better management of large data warehouse environments.

Question 7

What distinguishes an external table from a managed table in Hive?

A. Both store data files inside Hive warehouse
B. External tables cannot be queried
C. External tables automatically partition data
D. External tables reference data stored outside Hive’s warehouse directory

Answer

D. External tables reference data stored outside Hive’s warehouse directory

Explanation

An external table in Hive differs from a managed table because it references data stored outside the Hive warehouse directory. Dropping an external table removes only the metadata, while the underlying data remains intact.

Question 8

What is the main advantage of partitioning in Hive tables?

A. It encrypts sensitive data
B. It ensures high availability of Hive tables
C. It improves query performance by filtering data subsets
D. It automatically creates indexes on all columns

Answer

C. It improves query performance by filtering data subsets

Explanation

Partitioning improves query performance by dividing a table into smaller, manageable subsets based on column values. Queries that filter on partition columns scan only the relevant partitions rather than the entire dataset.

Question 9

What is the purpose of bucketing in Hive?

A. To replicate data across multiple nodes
B. To compress large data files
C. To provide encryption and security of Hive data
D. To evenly distribute data into fixed number of files based on column values

Answer

D. To evenly distribute data into fixed number of files based on column values

Explanation

Bucketing is used to distribute data evenly into a fixed number of files based on the hash of a column. This improves query efficiency, especially for joins and sampling operations, by ensuring predictable data distribution.

Question 10

Which component of Hive stores metadata about tables and databases?

A. HDFS
B. NameNode
C. Hive Metastore
D. MapReduce

Answer

C. Hive Metastore

Explanation

The Hive Metastore stores metadata about databases, tables, columns, partitions, and their locations in HDFS. It is a critical component that allows Hive to understand the structure and schema of the data being queried.

Question 11

What is one advantage of Hive Query Language (HQL)?

A. It requires advanced Java programming knowledge
B. It automatically creates machine learning models
C. It allows SQL-like queries on large datasets
D. It replicates data between clusters

Answer

C. It allows SQL-like queries on large datasets

Explanation

One major advantage of HQL is that it enables SQL-like querying over massive datasets stored in Hadoop. This makes big data analysis accessible to users with traditional SQL skills.

Question 12

Which type of data is best suited for Hive?

A. Low-latency real-time transactions
B. Small structured datasets
C. Large-scale analytical batch data
D. Streaming log events in milliseconds

Answer

C. Large-scale analytical batch data

Explanation

Hive is best suited for large-scale analytical batch processing rather than real-time or transactional workloads. It is optimized for high-throughput queries over large datasets, not low-latency operations.

Question 13

In Hive, which clause is used to filter rows in query results?

A. ORDER BY
B. LIMIT
C. GROUP BY
D. WHERE

Answer

D. WHERE

Explanation

The WHERE clause in Hive is used to filter rows in query results based on specified conditions. This reduces the amount of data processed and returned, improving efficiency and clarity of results.

Question 14

What happens if you drop a managed table in Hive?

A. The data files are archived in HDFS automatically
B. Both metadata and actual data files are deleted
C. Only metadata is removed
D. Only partitions are dropped

Answer

B. Both metadata and actual data files are deleted

Explanation

When a managed table is dropped in Hive, both its metadata from the metastore and the actual data files from HDFS are permanently removed. This behavior differs from external tables, where data remains untouched.

Question 15

Which command shows all available databases in Hive?

A. DESCRIBE DATABASE
B. USE DATABASE
C. CREATE DATABASE
D. SHOW DATABASES

Answer

D. SHOW DATABASES

Explanation

The SHOW DATABASES command displays all available databases in Hive. It is commonly used to explore existing database namespaces in a Hive environment.

Question 16

Which statement about Hive partitions is true?

A. They automatically replicate data
B. They divide a table into smaller parts for faster queries
C. They encrypt the table for security
D. They remove the need for indexes

Answer

B. They divide a table into smaller parts for faster queries

Explanation

Hive partitions divide a table into smaller logical parts based on column values. This structure allows queries to scan only relevant partitions, significantly improving performance.

Question 17

Why might bucketing be used instead of partitioning?

A. When data volumes are very small
B. When evenly distributing data into fixed-size files is desired
C. To enforce foreign key constraints
D. To automatically compress data

Answer

B. When evenly distributing data into fixed-size files is desired

Explanation

Bucketing is preferred over partitioning when even data distribution into a fixed number of files is required. It is especially beneficial for optimizing joins and improving performance consistency.

Question 18

Which Hive clause defines how data in a table should be parsed?

A. ROW FORMAT
B. PARTITIONED BY
C. CLUSTERED BY
D. STORED AS

Answer

A. ROW FORMAT

Explanation

The ROW FORMAT clause defines how data is parsed when it is read into a Hive table. It specifies delimiters or serialization mechanisms used to interpret raw data correctly.

Question 19

Which file format is columnar and often used in Hive for optimization?

A. TEXTFILE
B. JSON
C. ORC
D. SEQUENCEFILE

Answer

C. ORC

Explanation

ORC (Optimized Row Columnar) is a columnar file format widely used in Hive for performance optimization. It supports compression, predicate pushdown, and efficient storage, making it ideal for analytical workloads.