Big Data Fundamentals - Practice MCQs for CCAT

Correct Answer: B - Validation

The 5 V's of Big Data are Volume, Velocity, Variety, Veracity, and Value. Validation is not one of them.

Correct Answer: A - Too large for traditional database systems

Big Data refers to datasets that are too large, complex, or fast-moving for traditional database systems to handle.

Correct Answer: B - Velocity

Velocity refers to the speed at which data is generated, collected, and processed.

Correct Answer: D - Variety

Variety refers to the different types of data formats - structured (tables), semi-structured (JSON, XML), and unstructured (text, images).

Correct Answer: C - HDFS

HDFS (Hadoop Distributed File System) is designed for distributed storage across clusters of commodity hardware.

Correct Answer: C - Data Lake stores raw data in native format

Data Lake stores raw data in its native format (schema-on-read), while Data Warehouse requires structured data with predefined schema (schema-on-write).

Correct Answer: C - Extract, Transform, Load

ETL stands for Extract, Transform, Load - the process of extracting data from sources, transforming it, and loading into a destination system.

Correct Answer: B - Credit card fraud detection

Credit card fraud detection requires real-time processing to identify suspicious transactions as they occur.

Correct Answer: B - Trustworthiness of data

Veracity refers to the quality, accuracy, and trustworthiness of the data.

Correct Answer: D - Single-user desktop application

Single-user desktop applications don't require Big Data technologies. The others involve processing large datasets.

Correct Answer: D - Processing large volumes of data at scheduled intervals

Batch processing involves processing large volumes of accumulated data at scheduled intervals, not in real-time.

Correct Answer: D - Yahoo

Hadoop was originally developed at Yahoo based on Google's published papers on MapReduce and GFS.

Correct Answer: B - Two of three properties simultaneously

CAP theorem states that a distributed system can only guarantee 2 of 3: Consistency, Availability, and Partition tolerance.

Correct Answer: B - Apache Kafka Streams

Apache Kafka Streams is designed for real-time stream processing. MapReduce is for batch processing.

Correct Answer: D - Distributing data across multiple databases

Sharding horizontally partitions data across multiple database instances for scalability and performance.

Correct Answer: D - Social media posts

Social media posts are unstructured - they don't follow a predefined data model or schema.

Correct Answer: C - Data quality, security, and compliance

Data governance encompasses policies and processes for data quality, security, privacy, and regulatory compliance.

Correct Answer: D - Batch and stream processing

Lambda architecture combines batch processing for comprehensive analysis and stream processing for real-time views.

Correct Answer: B - Data freshness

Data freshness measures how current or up-to-date the data is - critical for time-sensitive applications.

Correct Answer: A - Adding more machines to the cluster

Horizontal scaling (scale-out) adds more machines to distribute the workload, unlike vertical scaling which upgrades existing machines.

Correct Answer: D - Veracity

The original 3Vs of Big Data defined by Doug Laney are Volume, Velocity, and Variety. Veracity (data quality/trustworthiness) was added later as a 4th V.

Correct Answer: C - The speed at which data is generated and processed

Velocity refers to the speed at which data is generated, collected, and processed. Real-time or near-real-time processing of streaming data is a key challenge addressed by this V.

Correct Answer: B - Relational database tables

Relational database tables contain structured data organized in predefined rows and columns with fixed schemas. Social media posts, videos, and emails are examples of unstructured or semi-structured data.

Correct Answer: A - A centralized repository that stores raw data in its native format

A Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale in its raw, native format. Unlike a data warehouse, it uses schema-on-read rather than schema-on-write.

Correct Answer: D - Extract, Transform, Load

ETL stands for Extract, Transform, Load. It is a process where data is extracted from source systems, transformed (cleaned, enriched, formatted) to fit operational needs, and loaded into a target database or data warehouse.

Correct Answer: C - Apache Kafka

Apache Kafka is a distributed streaming platform used for building real-time data pipelines and streaming applications. It can handle trillions of events per day with high throughput and low latency.

Correct Answer: D - Data Lakes use schema-on-read while Data Warehouses use schema-on-write

Data Lakes follow a schema-on-read approach where raw data is stored without a predefined schema and structure is applied when data is read. Data Warehouses use schema-on-write where data must conform to a predefined schema before being stored.

Correct Answer: C - The economic worth derived from data analysis

Value refers to the economic worth or business insights that can be derived from analyzing Big Data. Having large volumes of data is meaningless unless valuable information and actionable insights can be extracted from it.

Correct Answer: B - Apache Sqoop

Apache Sqoop is a tool designed for efficiently transferring bulk data between Apache Hadoop and structured data stores such as relational databases. It can import tables from RDBMS to HDFS and export data from HDFS to RDBMS.

Correct Answer: A - Semi-structured data

XML is classified as semi-structured data. It does not conform to a strict tabular schema like structured data, but it has tags and markers that provide some organizational structure, unlike purely unstructured data such as images or free text.

Correct Answer: C - Apache Tomcat

Apache Tomcat is a web server and servlet container, not a Big Data processing framework. Apache Spark, Flink, and Storm are all frameworks used for large-scale data processing.

Correct Answer: B - The trustworthiness and quality of data

Veracity refers to the trustworthiness, accuracy, and quality of data. In Big Data, data can be noisy, incomplete, or inconsistent, making it important to assess and ensure data quality before analysis.

Correct Answer: B - Collecting and processing data in large blocks at scheduled intervals

Batch processing involves collecting data over a period of time and processing it in large blocks (batches) at scheduled intervals. It is suitable for non-time-critical processing of large volumes of data, as opposed to real-time stream processing.

Correct Answer: A - Ingesting large volumes of log and event data into Hadoop

Apache Flume is a distributed, reliable service for efficiently collecting, aggregating, and moving large amounts of log and event data from many sources to a centralized data store like HDFS.

Correct Answer: B - The different formats, types, and sources of data

Variety refers to the different types, formats, and sources of data including structured (databases), semi-structured (JSON, XML), and unstructured (images, videos, text). Managing this diversity is a key Big Data challenge.

Correct Answer: A - Data is loaded into the target system first, then transformed

ELT (Extract, Load, Transform) loads raw data into the target system first and then transforms it within the target system. This approach leverages the processing power of modern data warehouses and data lakes, unlike traditional ETL.

Correct Answer: A - Distributed coordination and configuration management

Apache Zookeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. It is used by many Big Data tools like Kafka and HBase for coordination.

Correct Answer: A - A collection of video surveillance footage

Video surveillance footage is unstructured data as it does not follow any predefined data model or schema. Database tables, CSV files, and spreadsheets are structured data with defined formats and schemas.

Correct Answer: C - The process of transporting data from various sources to a storage medium

Data ingestion is the process of obtaining and importing data from various sources for immediate use or storage in a database or data warehouse. It can be done in real-time (streaming) or in batches.

Correct Answer: A - Apache Hive

Apache Hive provides a SQL-like query language called HiveQL (HQL) that allows users to query and manage large datasets stored in Hadoop's HDFS. It converts SQL queries into MapReduce or Tez jobs.

Correct Answer: C - Adding more machines to distribute the workload

Horizontal scaling (scale-out) involves adding more machines to a cluster to distribute the workload. This is the preferred approach in Big Data as it is more cost-effective and provides better fault tolerance compared to vertical scaling.

Correct Answer: B - Schema is applied when data is read from storage

Schema-on-read means the structure or schema is applied to data only when it is read or queried, not when it is stored. This approach is used in data lakes and allows flexibility in storing raw data of any format.

Correct Answer: A - Spark performs in-memory processing which is significantly faster

Apache Spark's primary advantage is in-memory computing. It keeps intermediate data in memory (RAM) rather than writing to disk after each stage like MapReduce, making it up to 100x faster for certain workloads.

Correct Answer: A - The process of cleaning, restructuring, and enriching raw data

Data wrangling or data munging is the process of cleaning, restructuring, and enriching raw data into a more usable format. It involves handling missing values, correcting errors, and transforming data for analysis.

Correct Answer: C - Apache Parquet

Apache Parquet is a columnar storage format optimized for Big Data processing. It stores data by columns rather than rows, enabling efficient compression, encoding, and fast analytical queries that only need specific columns.

Correct Answer: C - An isolated repository of data controlled by one department, inaccessible to others

A data silo is an isolated collection of data held by one group or department that is not easily or fully accessible by other groups in the same organization. Data silos hinder cross-functional analysis and collaboration.

Correct Answer: D - Apache Oozie

Apache Oozie is a workflow scheduler system for managing Apache Hadoop jobs. It allows users to create directed acyclic graphs (DAGs) of actions and schedule complex data pipelines with dependencies.

Correct Answer: A - To divide large datasets into smaller, manageable chunks for parallel processing

Data partitioning divides a large dataset into smaller, more manageable subsets (partitions) that can be processed in parallel across multiple nodes. This improves query performance and enables distributed processing.

Correct Answer: D - Apache Storm

Apache Storm is a free, open-source distributed real-time computation system that processes unbounded streams of data reliably. Unlike Hive and Pig which are batch processing tools, Storm processes data in real-time.

Correct Answer: A - Value

Value is considered the 5th V of Big Data and refers to the ability to turn data into meaningful and actionable business insights. Without extracting value, even large volumes of data serve no practical purpose.