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Big Data Quiz 2 (10 MCQs)

This set of multiple-choice questions evaluates understanding of big data analysis, management, and governance. It covers data diversity, advanced analytical techniques, data velocity, and the challenges of big data. Skills tested include data collection, integration, and processing speed, as well as the ability to identify root causes and solve information problems using reputable resources.

Quiz Instructions

Select an option to see the correct answer instantly.

1. Big data adds complexity to data governance because governance needs vary widely by data source.

Correct Answer: Correct answer is: (A) True.

Exam Relevance: Certified Data Management Professional (CDMP), Certified Analytics Professional (CAP), Certified Data Governance Professional (CDGP)

Difficulty: Moderate

Concept notes: Big data introduces complexity to data governance due to the diverse sources and varying governance needs.

Common Mistakes: A common misunderstanding is that data governance needs are uniform across all data sources, which is not the case with big data.

Explanations: Big data encompasses a wide variety of data sources, each with unique characteristics and governance requirements. For example, data from social media platforms may require different governance policies compared to data from IoT devices or transactional databases. This diversity in data sources necessitates a more complex and nuanced approach to data governance, making it more challenging to manage and ensure compliance.

Option Analysis:

Option A: This option correctly identifies that big data adds complexity to data governance due to the varying governance needs by data source.
Option B: This option incorrectly suggests that big data does not add complexity to data governance, which is not accurate given the diverse nature of data sources.

2. Which step in the Big 6 Process requires the researcher to find reputable resources?

Correct Answer: Correct answer is: (D) Location and Access.

Exam Relevance: Library Science Exams, Information Literacy Assessments, Research Methodology Exams

Difficulty: Moderate

Concept notes: The Big 6 Process is a model for information problem-solving that includes six steps: Task Definition, Information Seeking Strategies, Location and Access, Use of Information, Synthesis, and Evaluation. The step "Location and Access" involves finding and accessing the most appropriate and reputable resources to gather information.

Common Mistakes: A common misunderstanding is that "Information Seeking Strategies" involves the actual location and access to resources, but it primarily focuses on determining the best methods to find information.

Explanations: In the Big 6 Process, the step "Location and Access" specifically requires the researcher to find and access reputable resources. This step involves identifying and locating the most appropriate sources of information, such as databases, books, and online resources, to ensure the information gathered is reliable and credible.

Option Analysis:

Option A: Task Definition involves understanding the problem and defining the information needed, not finding resources.
Option B: Information Seeking Strategies involves determining the best methods to find information, not the actual location and access to resources.
Option C: Use of Information involves organizing and using the information gathered, not finding resources.
Option D: Location and Access involves finding and accessing reputable resources, which is the correct step.

3. The use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes called as

Correct Answer: Correct answer is: (C) Big data analytics.

Exam Relevance: GRE, GMAT, Data Science Certifications, Big Data Certifications

Difficulty: Moderate

Concept notes: Big data analytics refers to the use of advanced analytical techniques to process and analyze large, diverse data sets that include structured, semi-structured, and unstructured data from various sources.

Common Mistakes: A common misunderstanding is that big data analytics only involves structured data, but it encompasses a wide range of data types and sources.

Explanations: Big data analytics involves the application of advanced analytical techniques to very large, diverse data sets that include structured, semi-structured, and unstructured data from different sources. This aligns with the definition provided in the question, making "Big data analytics" the correct answer.

Option Analysis:

Option A: "Big Data Source" is incorrect because it refers to the origin of the data rather than the analytical process.
Option B: "Big Structure" is incorrect as it does not relate to the analytical techniques used on diverse data sets.
Option C: "Big data analytics" is the correct term for the use of advanced techniques on large, diverse data sets.
Option D: "None of the above" is incorrect because "Big data analytics" is a valid and widely used term.

4. What are the key issues with 'Big Data' in relation to marketing research?

Correct Answer: Correct answer is: (B) Hard to capture, store, manage and analyse.

Exam Relevance: MBA, Marketing Research Exams, Data Science Exams

Difficulty: Moderate

Concept notes: Big Data refers to extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. The key issues with Big Data in marketing research include the challenges of capturing, storing, managing, and analyzing vast amounts of data.

Common Mistakes: A common misunderstanding is that Big Data is simply a large amount of data, without recognizing the complexity involved in handling it.

Explanations: Big Data presents significant challenges in terms of capturing, storing, managing, and analyzing large volumes of data. These challenges arise due to the sheer scale and complexity of the data, which can come from various sources and in different formats. Effective management and analysis of Big Data require advanced technologies and methodologies to ensure that the data can be processed and interpreted accurately.

Option Analysis:

Option A: This option is incorrect because 'Big Data' is not easily confused with 'Small Data'. The term 'Big Data' specifically refers to the large volume, velocity, and variety of data that cannot be handled by traditional data processing applications.
Option B: This option is correct because capturing, storing, managing, and analyzing Big Data is indeed challenging due to the volume, velocity, and variety of the data.
Option C: This option is incorrect because while Big Data can be broad and generic, the primary issue is the technical challenges in handling it, not its broadness or generic nature.
Option D: This option is incorrect because there are indeed key issues with Big Data, as described in the correct answer.

5. What characterizes data management problems associated with big data storage?

Correct Answer: Correct answer is: (B) Generating and collecting data from multiple sources.

Exam Relevance: GRE, GMAT, Data Science Certification Exams, Big Data Analytics Exams

Difficulty: Moderate

Concept notes: Data management problems associated with big data storage often involve the challenges of generating and collecting data from multiple sources. This is a core issue because big data typically comes from diverse and numerous sources, making it difficult to manage and integrate.

Common Mistakes: A common misunderstanding is that data management problems are primarily about maintaining data integrity or ensuring accessibility, which are important but not the primary challenge in the context of big data storage.

Explanations: Big data storage involves handling vast amounts of data from various sources, which can be highly complex. The challenge lies in efficiently collecting and integrating data from multiple sources, ensuring that the data is consistent and usable. This process is critical for effective data management in big data environments.

Option Analysis:

Option A: Maintaining the integrity of stored data is important but not the primary challenge associated with big data storage.
Option B: Generating and collecting data from multiple sources is a key challenge in big data storage, making it the correct answer.
Option C: Ensuring data accessibility is a concern but not the primary issue related to big data storage.
Option D: Making data available to authorized users is a security concern but not the primary challenge in big data storage.

6. After noticing a 15% drop in user engagement on a mobile app, a data team performs a drill-down analysis to find that the drop occurred specifically after a recent software update. This is an example of:

Correct Answer: Correct answer is: (B) Diagnostic Analytics.

Exam Relevance: Certified Data Analyst, Data Science Certification, Big Data Analytics Exam

Difficulty: Moderate

Concept notes: Diagnostic analytics involves analyzing data to understand why something happened. It focuses on identifying the root cause of a problem or trend.

Common Mistakes: A common mistake is confusing diagnostic analytics with descriptive analytics, which only describes what happened without explaining why.

Explanations: The scenario describes a situation where a data team performs a drill-down analysis to identify the cause of a 15% drop in user engagement after a software update. This is a clear example of diagnostic analytics, as the team is investigating the underlying reasons for the drop in engagement.

Option Analysis:

Option A: Descriptive Analytics is incorrect because it only describes what happened, not why it happened.
Option B: Diagnostic Analytics is correct because it involves identifying the root cause of the drop in user engagement.
Option C: Predictive Analytics is incorrect because it focuses on forecasting future trends, not understanding past events.
Option D: Prescriptive Analytics is incorrect because it involves recommending actions based on data, not diagnosing the cause of a problem.

Mnemonic: D for Diagnose, D for Diagnostic

7. Which attribute of big data involves an exponential data growth rate?

Correct Answer: Correct answer is: (D) Velocity.

Exam Relevance: GRE, GMAT, Data Science Certification Exams, Big Data Analytics Exams

Difficulty: Easy

Concept notes: Velocity in big data refers to the speed at which data is generated and processed.

Common Mistakes: A common misunderstanding is that volume refers to the speed of data generation, but volume actually refers to the amount of data.

Explanations: Velocity in big data is specifically concerned with the rate at which data is generated and processed. This attribute involves the exponential growth rate of data, making it the correct answer.

Option Analysis:

Option A: Volume refers to the amount of data, not the speed of data generation.
Option B: Variety refers to the different types of data, not the speed of data generation.
Option C: Value refers to the usefulness or benefit derived from the data, not the speed of data generation.
Option D: Velocity refers to the speed at which data is generated and processed, which aligns with the exponential data growth rate.

8. Technology growth is one of the biggest factors for the evolution of Big data. Is it true or false?

Correct Answer: Correct answer is: (A) True.

Exam Relevance: GRE, GMAT, Data Science Exams, Big Data Certification Exams

Difficulty: Easy

Concept notes: Technology growth has significantly contributed to the evolution of big data by enabling the collection, storage, and analysis of vast amounts of data.

Common Mistakes: A common misunderstanding is that big data is solely about the volume of data, but it also involves the velocity, variety, and veracity of data, all of which are influenced by technological advancements.

Explanations: Technology growth has enabled the development of advanced data storage solutions, such as cloud computing, and powerful data processing tools, such as Hadoop and Spark. These advancements have made it possible to handle and analyze large volumes of data efficiently, which is a key aspect of big data. Therefore, the statement is true.

Option Analysis:

Option A: Correct. Technology growth has indeed been a significant factor in the evolution of big data.
Option B: Incorrect. This option contradicts the significant role of technology in the evolution of big data.

9. In Big Data Analytics, velocity refers to

Correct Answer: Correct answer is: (A) Frequency of incoming data.

Exam Relevance: Data Science Certification, Big Data Analytics Exams, Data Engineering Certification

Difficulty: Easy

Concept notes: In Big Data Analytics, velocity refers to the speed at which data is generated and processed. It is often associated with the frequency of incoming data.

Common Mistakes: A common misunderstanding is that velocity refers to the speed of data access or the bandwidth of incoming data, rather than the frequency of data generation.

Explanations: Velocity in Big Data Analytics specifically refers to the rate at which data is generated and processed. This is often measured by the frequency of incoming data, which is crucial for real-time analysis and decision-making. The other options, such as bandwidth and data access speed, are related to data transmission and retrieval, not the rate of data generation.

Option Analysis:

Option A: Correct. Velocity in Big Data Analytics refers to the frequency of incoming data.
Option B: Incorrect. Bandwidth refers to the capacity of a network to transmit data, not the frequency of data generation.
Option C: Incorrect. The amount of data stored in a database is related to volume, not velocity.
Option D: Incorrect. Speed of data access is related to how quickly data can be retrieved, not the rate at which data is generated.

10. Data mining is a process used by companies to turn raw data into useful information

Correct Answer: Correct answer is: (A) True.

Exam Relevance: GRE, GMAT, Data Science Certifications

Difficulty: Easy

Concept notes: Data mining is a process that involves extracting useful information from large datasets.

Common Mistakes: A common misunderstanding is that data mining only involves collecting raw data, but it actually involves transforming raw data into meaningful insights.

Explanations: Data mining is a process used by companies to extract useful information from large datasets. This process involves various techniques such as pattern recognition, machine learning, and statistical analysis to turn raw data into actionable insights. Therefore, the statement is true.

Option Analysis:

Option A: This option is correct because data mining does involve turning raw data into useful information.
Option B: This option is incorrect because data mining is indeed a process that transforms raw data into useful information.

Frequently Asked Questions

What is Big Data?

Big Data refers to extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

What are some challenges associated with Big Data?

Challenges include managing the volume, velocity, and variety of data, ensuring data quality, and addressing privacy and security concerns.

How does Big Data impact marketing research?

Big Data allows for more detailed and accurate marketing research, enabling companies to better understand consumer behavior and preferences, and tailor their strategies accordingly.

What is data velocity in the context of Big Data?

Data velocity refers to the speed at which data is generated and processed, which is a critical aspect of Big Data as it requires real-time or near-real-time analysis.

What are some advanced analytical techniques used in Big Data?

Advanced analytical techniques include data mining, machine learning, and predictive analytics, which help extract meaningful insights from large and complex data sets.

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