D495 Big Data Foundations

Explanation:

The high value characteristic of Big Data emphasizes the importance of extracting meaningful and actionable insights from large datasets to support strategic decision-making. When data is valuable, it provides organizations with critical information about trends, patterns, customer behaviors, and operational efficiencies. Leveraging high-value data allows companies to make informed, evidence-based decisions, optimize processes, and gain competitive advantages. The utility of Big Data lies not in its sheer volume, but in the actionable insights it can provide to improve decision-making outcomes.

Correct Answer:

High value allows organizations to derive insights that inform and enhance decision-making.

Why Other Options Are Wrong:

High value refers to the amount of data collected over time.

This option is incorrect because the amount of data collected relates to the volume characteristic, not the value. High value focuses on the usefulness and actionable potential of the data rather than its quantity.

High value indicates the ethical considerations of data ownership.

This option is incorrect because ethical considerations pertain to privacy and data governance, not the inherent value or usefulness of the data for decision-making purposes.

High value is solely about the speed of data processing.

This option is incorrect because the speed of processing is associated with the velocity characteristic, not value. Value is about the insights and actionable knowledge derived from the data, not how quickly it is processed.

Explanation:

Obtaining consent is a fundamental principle of ethical and legal data use. It ensures that individuals are informed about how their data will be collected, stored, and used, and that they agree to these practices. This process promotes transparency, accountability, and trust between data collectors and participants. Consent is critical across all types of personal data, not limited to financial or public data, and remains important even when data is anonymized, as re-identification risks may exist.

Correct Answer:

Obtaining consent ensures that individuals are aware of and agree to how their data will be used.

Why Other Options Are Wrong:

Obtaining consent only applies to financial data.

This is incorrect because consent is required for a wide range of personal data, not just financial information. Limiting it to financial data ignores privacy regulations and ethical practices that cover health, location, behavioral, and other types of data.

Obtaining consent is unnecessary if data is anonymized.

This is incorrect because anonymization reduces but does not completely eliminate privacy risks. Ethical guidelines and regulations often still require consent to ensure transparency and respect for individual rights.

Obtaining consent is only relevant for public data.

This is incorrect because public data does not automatically negate the need for consent when it is collected, processed, or linked to other datasets. Consent principles are relevant whenever personal data is used in ways that could impact individuals.

Explanation:

SQL databases are designed to handle structured data efficiently by storing it in organized tables with defined relationships. They allow for reliable storage, retrieval, and management of large datasets using standardized query languages. In the context of Big Data, SQL databases help manage structured data and support analytics by enabling fast querying, indexing, and reporting. While SQL databases are less suitable for unstructured or semi-structured data, they remain essential for transactional systems, business intelligence, and other structured data applications.

Correct Answer:

SQL databases are used to store, retrieve, and manage large volumes of structured data efficiently.

Why Other Options Are Wrong:

SQL databases are designed for processing unstructured data only.

This is incorrect because SQL databases are optimized for structured data, not unstructured data. Handling unstructured data typically requires NoSQL databases or other Big Data storage solutions.

SQL databases are primarily used for creating presentations.

This is incorrect because SQL databases are tools for data storage and querying, not for presentation creation. Presentations may use data extracted from SQL databases, but the database itself is not a presentation tool.

SQL databases are used to write code for web applications.

This is incorrect because SQL databases are not programming environments. While web applications may interact with SQL databases, the databases themselves are for data management, not for writing application code.

Explanation:

SQL databases are a technology commonly used to manage structured data in Big Data environments. They allow for querying, storing, and organizing large datasets efficiently. Although Big Data often involves semi-structured and unstructured data, SQL databases remain a foundational tool for managing structured components, supporting analytics, and integrating with more advanced Big Data platforms. Other specialized technologies, such as NoSQL databases, Hadoop, and cloud-based storage, complement SQL databases in handling Big Data’s volume, variety, and velocity.

Correct Answer:

SQL databases

Why Other Options Are Wrong:

Word

This option is incorrect because Word is a word-processing tool and is not designed for storing, managing, or analyzing large datasets.

Notepad

This option is incorrect because Notepad is a basic text editor with no capabilities for handling, querying, or managing Big Data effectively.

PowerPoint

This option is incorrect because PowerPoint is a presentation software and cannot manage, process, or analyze large datasets.

Explanation:

When a company struggles to extract insights from large datasets, adopting advanced data processing tools is essential. Tools such as distributed computing platforms, machine learning algorithms, and real-time analytics frameworks enable efficient handling of high-volume, high-velocity, and high-variety data. These tools facilitate data cleaning, integration, transformation, and analysis, allowing organizations to uncover patterns, trends, and actionable insights that would be impossible or inefficient with traditional or manual methods. Leveraging the right tools maximizes the value of Big Data and supports informed decision-making.

Correct Answer:

Adopting advanced data processing tools to enhance analysis capabilities.

Why Other Options Are Wrong:

Reducing the amount of data collected to simplify analysis.

This option is incorrect because reducing data limits the insights available. Big Data’s value lies in its volume and variety, and discarding data may prevent organizations from uncovering meaningful patterns.

Relying solely on manual data analysis methods.

This option is incorrect because manual analysis cannot handle the scale and complexity of Big Data efficiently. It is too slow and prone to errors for large or unstructured datasets.

Increasing the number of data sources without improving tools.

This option is incorrect because adding more data sources without enhancing processing capabilities exacerbates the challenge. Without advanced tools, the additional data can overwhelm existing systems and reduce the effectiveness of analysis.

Explanation:

In this scenario, the researcher is collecting two types of data. Ratings on a scale of 1 to 10 represent quantitative data because they are numerical and measurable, allowing for statistical analysis such as calculating averages or trends. Open-ended feedback represents qualitative data because it captures descriptive, non-numerical information, such as opinions, experiences, and suggestions. Using both quantitative and qualitative data provides a comprehensive understanding of customer satisfaction by combining measurable metrics with detailed personal insights.

Correct Answer:

Quantitative and qualitative data

Why Other Options Are Wrong:

Categorical and ordinal data

This option is incorrect because while ordinal data (ratings) and categorical data (categories or labels) are types of data, the scale of 1 to 10 is considered quantitative, and open-ended feedback is qualitative, not categorical.

Only quantitative data

This option is incorrect because it ignores the open-ended feedback, which is qualitative in nature. Only considering the numeric ratings would exclude important descriptive insights.

Only qualitative data

This option is incorrect because the numeric ratings are quantitative, providing measurable information that can be statistically analyzed. Excluding this would overlook a key aspect of the study.

Explanation:

Moore's Law observes that the number of transistors on a microchip—and thus the processing power and capacity of integrated circuits—doubles approximately every 18 months. This exponential growth has historically driven advancements in computing performance, efficiency, and cost-effectiveness, enabling more complex computations and applications over time. The law specifically refers to doubling, not general increases, decreases, or tripling, which makes the statement precise and measurable.

Correct Answer:

doubles

Why Other Options Are Wrong:

decreases

This is incorrect because Moore’s Law predicts growth, not reduction, in integrated circuit capacity. The statement that capacity decreases contradicts decades of observed technological trends in semiconductor development.

increases

This is incorrect because while increases happen, Moore's Law specifically quantifies the rate of growth as doubling approximately every 18 months. Simply stating “increases” is too vague and does not capture the exponential pattern described by Moore’s Law.

triples

This is incorrect because Moore’s Law does not describe a tripling of capacity. The recognized standard is doubling every 18 months, and suggesting tripling would overstate the expected growth rate.

Explanation:

MapReduce jobs can be configured at execution time using both command-line arguments and job configuration files. Command-line arguments allow users to quickly specify parameters when submitting a job, while configuration files provide a structured way to define multiple parameters, including input/output paths, memory allocation, and task settings. Utilizing both methods provides flexibility and ensures that jobs run with the desired configurations, making them adaptable to different datasets and cluster environments.

Correct Answer:

Both A and B

Why Other Options Are Wrong:

Command-line arguments

This option is partially correct but incomplete because configuration files can also be used to set parameters. Only using command-line arguments limits flexibility and reproducibility.

Job configuration files

This option is partially correct but incomplete because command-line arguments also allow parameter configuration. Excluding them ignores an important execution-time configuration method.

Default settings only

This option is incorrect because relying solely on default settings prevents customization for specific job requirements and may result in suboptimal performance.

Explanation:

To improve data reliability, an organization should implement robust data governance and quality assurance practices. Data governance establishes policies, standards, and responsibilities for managing data, ensuring consistency, accuracy, and accountability. Quality assurance practices involve validating, cleaning, and verifying data to prevent errors and maintain reliability. Together, these measures enhance the integrity of data processing and support more accurate and trustworthy analyses, which are essential for effective decision-making in Big Data initiatives.

Correct Answer:

Implement data governance and quality assurance practices.

Why Other Options Are Wrong:

Focus solely on data visualization tools.

This is incorrect because visualization tools help interpret data but do not address the root causes of unreliable data. Without improving data quality and governance, visualizations may present misleading or incorrect insights.

Reduce the amount of data collected.

This is incorrect because reducing data collection does not inherently improve reliability. The issue lies in the accuracy and consistency of the data, not the quantity collected.

Increase data storage capacity only.

This is incorrect because storage capacity addresses volume but does not resolve reliability or quality issues. Simply having more storage does not ensure accurate or trustworthy data processing.