Metadata Standards MCQ Quiz in తెలుగు - Objective Question with Answer for Metadata Standards - ముఫ్త్ [PDF] డౌన్‌లోడ్ కరెన్

The Correct answer is 15.

Key Points

• Dublin Core:
  • Dublin Core is a metadata standard used for describing electronic resources such as web pages, images, videos, and other online documents.
  • It provides a set of simple and standardized elements for invariably describing resources, making them more easily accessible and understandable to both humans and machines.
  • The Dublin Core Metadata Initiative (DCMI) is responsible for maintaining and promoting this standard.
  • Dublin Core is an initiative to create a Digital Library Card Catalogue for the web.
  • Dublin Core Metadata is a set of 15 core elements.
  • The 15 elements of Dublin Core Metadata are- 
    • Contributor
    • Coverage
    • Creator 
    • Date
    • Description
    • Format
    • Identifier
    • Language
    • Publisher
    • Relation
    • Rights
    • Source 
    • Subject 
    • Title 
    • Type

Additional Information

• The Qualified Dublin Core:
  • The Qualified Dublin Core, which was replaced by the DCMI Metadata Terms in 2008, aimed to enhance the original 15 Dublin Core Metadata Element Set (DCMES) terms through an ongoing process of expansion and refinement.
  • By incorporating Audience, Provenance, and Rights Holder, the Qualified Dublin Core provided more specific metadata details, supplementing the original Dublin Core terms with information about encoding schemes, enumerated lists of values, or other hints for processing.
  • These additional terms were formulated within working groups by the Dublin Core Metadata Initiative (DCMI) and evaluated by the DCMI Usage Board to ensure their alignment with sound practices for qualifying Dublin Core metadata elements.

The Correct answer is the METAe project group.

Key Points

• The Analyzed Layout and Text Object (ALTO) is an open XML Schema formulated by the METAe project, which received funding from the European Union.
• ALTO is frequently employed in conjunction with the Metadata Encoding and Transmission Standard (METS) for comprehensively detailing digitized objects and establishing connections across ALTO files, such as providing a description of reading sequences.
• Since 2010, the Library of Congress has been the host of this standard, and it is managed by the Editorial Board established at the same time.
• From the initial version of the ALTO standard in June 2004 (version 1.0), CCS Content Conversion Specialists GmbH, Hamburg, oversaw ALTO up to version 1.4.

Additional Information

• RLG:
  • The Research Libraries Group (RLG) was a library consortium headquartered in the United States, operational from 1974 until its amalgamation with the OCLC library consortium in 2006.
  • Under RLG, the SHARES program was instituted as an Interlibrary Lending and Document Supply Program, aimed at facilitating resource sharing among RLG members.
  • RLG was responsible for the development of several notable resources, including the Eureka interlibrary search engine, the RedLightGreen database containing bibliographic descriptions, and ArchiveGrid, a database comprising descriptive information about archival collections.

The Correct answer is the University of Waikato.

Key Points

• Weka is a versatile data mining tool featuring visualization tools, algorithms for data analysis and predictive modeling, and intuitive graphical user interfaces.
• Initially developed in 1993 by the University of Waikato in New Zealand, Weka started as a mix of Tcl/Tk, C, and makefiles, primarily aimed at analyzing data from agricultural domains.
• In 1997, Weka underwent a significant transformation, transitioning to a fully Java-based version (Weka 3) with newly implemented modeling algorithms.
• This modern iteration of Weka quickly gained popularity across various application areas, especially in educational and research settings.
• Weka offers several advantages, including free availability under the GNU General Public License, portability across different computing platforms due to its Java implementation, and a comprehensive suite of data preprocessing and modeling techniques.
• Its user-friendly interfaces further enhance its ease of use.
• Supporting standard data mining tasks such as preprocessing, clustering, classification, regression, visualization, and feature selection, Weka operates on data formatted in the Attribute-Relational File Format (.arff).
• While it primarily handles flat-file data, Weka can also access SQL databases through Java Database Connectivity.
• Although Weka does not support multi-relational data mining out of the box, separate software exists for converting linked database tables into a suitable format. Additionally, Weka lacks algorithms for sequence modeling, a gap in its functionality.
• Throughout its history, Weka has garnered recognition, including the SIGKDD Data Mining and Knowledge Discovery Service Award in 2005.
• In 2006, Pentaho Corporation acquired an exclusive license for Weka's use in business intelligence, integrating it into the Pentaho business intelligence suite.
• Following Pentaho's acquisition by Hitachi Vantara, Weka now forms the foundation for the PMI (Plugin for Machine Intelligence) open-source component.

The Correct answer is A, C, and D.

Key Points

• Data Mining is the practice of extracting meaningful patterns from large datasets to inform business decisions.
• Data mining is performed by data scientists and analysts using tools from machine learning, AI, and statistics to sort, clean, and model data drawn from sources like data lakes, warehouses, or IoT feeds.

Core Process:

• Data Gathering: Identify and collect relevant raw data from structured databases or unstructured sources.
• Data Preparation: Clean, profile, and preprocess to correct errors and ensure consistency.
• Mining & Modeling: Choose algorithms (e.g., clustering, classification) and train models on sample data before applying them at scale.
• Analysis & Communication: Interpret model outputs, build predictive or descriptive analytics, and present findings—often via visualizations—to guide strategic decisions.

​

The Correct answer is ​All of these.

Key Points

• Data mining is the process of searching and analyzing a large batch of raw data in order to identify patterns and extract useful information. 

Data Mining Techniques:

• Association Rules (Market Basket Analysis): Finds items that frequently co-occur—e.g., which products customers often buy together—to inform promotions and inventory.
• Classification: Assigns data to predefined categories (e.g., tagging email as “spam” or “not spam”) for organized analysis.
• Clustering: Groups similar records without pre-set labels (e.g., segmenting customers into “hair care” vs. “dental health” buyers).
• Decision Trees: Uses a series of branching questions to classify or predict outcomes (e.g., diagnosing equipment issues based on symptoms).
• K-Nearest Neighbors (KNN): Classifies a data point by comparing it to its closest neighbors in feature space (e.g., predicting a patient’s risk by matching symptoms to past cases).
• Neural Networks: Models complex patterns through interconnected layers of weighted “neurons” (e.g., handwriting or image recognition).
• Predictive Analysis: Builds statistical or machine-learning models on historical data to forecast future values (e.g., sales projections).

The Correct answer is Data Lake.

Key Points

• A data lake is a centralized, scalable repository that holds vast amounts of structured, semi-structured, and unstructured data in its native format.
• It ingests data from any source—on-premises, cloud, or edge—at any speed and without size limits.
• Data lakes support real-time or batch processing and let users analyze information using SQL, Python, R, or third-party analytics tools.

Key Capabilities:

• Power Data Science & ML
  • Transform raw data into analytics-ready tables with low latency.
  • Retain raw inputs indefinitely for future experiments.
• Centralize & Catalogue
  • Break down data silos—batch streams, databases, logs—in one searchable repository.
• Integrate Any Source
  • Ingest batch or streaming data, binary files, geospatial feeds, etc., keeping all sources up to date.
• Democratize Access
  • Support diverse users and tools—from SQL analysts to Python data scientists—with unified access.

Additional Information

• Big data:
  • ​Big data describes massive, rapidly growing information collections characterized by three key attributes—volume (scale), velocity (ingest speed), and variety (different data types).
  • It serves as the raw material for data mining.
• Data Mining:
  • The process of exploring large datasets to discover patterns, correlations, and anomalies, often with statistical or machine-learning techniques.
• Predictive Analytics:
  • Using historical and real-time data, statistical algorithms, and machine-learning models to predict future outcomes.

The Correct answer is (i), (iii), (iv) are correct.

Key Points

• Creator, Publisher, and Rights are the intellectual property-related elements in Dublin Core Metadata.
• The Dublin Core -
  • "The Dublin Core", also known as the Dublin Core Metadata Element Set, is a set of fifteen "core" elements (properties) for describing resources.
  • The 15 Dublin Core elements are -

The Correct answer is 2 and 4.

Key Points

• In Dublin Core Metadata, elements related to intellectual property are those that address ownership, authorship, and usage rights.

Dublin Core:

• Dublin Core is a metadata standard used for describing electronic resources such as web pages, images, videos, and other online documents.
• It provides a set of simple and standardized elements for invariably describing resources, making them more easily accessible and understandable to both humans and machines.
• The Dublin Core Metadata Initiative (DCMI) is responsible for maintaining and promoting this standard.
• Dublin Core is an initiative to create a Digital Library Card Catalogue for the web.
• Dublin Core Metadata is a set of 15 core elements.
• The 15 elements of Dublin Core Metadata are- 
  • Contributor
  • Coverage
  • Creator 
  • Date
  • Description
  • Format
  • Identifier
  • Language
  • Publisher
  • Relation
  • Rights
  • Source 
  • Subject 
  • Title 
  • Type

Additional Information

• The Qualified Dublin Core:
  • The Qualified Dublin Core, which was replaced by the DCMI Metadata Terms in 2008, aimed to enhance the original 15 Dublin Core Metadata Element Set (DCMES) terms through an ongoing process of expansion and refinement.
  • By incorporating Audience, Provenance, and Rights Holder, the Qualified Dublin Core provided more specific metadata details, supplementing the original Dublin Core terms with information about encoding schemes, enumerated lists of values, or other hints for processing.
  • These additional terms were formulated within working groups by the Dublin Core Metadata Initiative (DCMI) and evaluated by the DCMI Usage Board to ensure their alignment with sound practices for qualifying Dublin Core metadata elements.

Key Points

• Descriptive metadata:
  • ​Descriptive metadata refers to bibliographic details that identify specific data by providing information about its content, context, and attributes.
  • It encompasses details such as the document's title, creator's name, data type, creation date, volume count, keywords, and more.
  • Additionally, it may incorporate a brief summary outlining the content of the resource.

Additional Information

• Structural metadata:
  • ​Structural metadata functions similarly to a book's table of contents, offering details about the physical organization, arrangement, and interconnections among data files and resources within systems.
  • In the case of a video, structural metadata might entail information regarding the sequence of various segments within the video and the specific locations of advertisements.
• Process metadata:
  • ​Process metadata provides insights into the outcomes of diverse operations and workflows within a data warehouse.
  • It encompasses information about the various actions, tools, and steps involved in generating, modifying, and managing statistical data.
  • This form of metadata is instrumental in the analysis of data, facilitating an understanding of its quality and reproducibility.
• Legal metadata furnishes details regarding licensing, copyright, ownership, usage rights, and other legal considerations associated with digital resources.
• Usage metadata:
  • Usage metadata captures data set usage information, encompassing metrics such as the number of views a digital asset receives, user access details, interaction duration, and more.
  • Businesses leverage this metadata to evaluate asset popularity and analyze customer behavior, aiding in the enhancement of products and services.
• Quality metadata:
  • ​Quality metadata is information about the quality level of data.
  • It measures data quality, accuracy, currency, reliability, and completeness of the data.
  • It details on dataset statuses, freshness, tests run, and test success.

Key Points

• The Dublin Core metadata element set is a standard for cross-domain information resource description.
• DC.Format is used to describe the file format, physical medium, or dimensions of the resource.
• In the context of images, the MIME type is the appropriate syntax to use for encoding formats.
• The correct MIME type for a PNG image is image/png.
• Using the correct MIME type ensures that the resource is properly identified and can be rendered correctly by web browsers and other software.

 Additional Information

• application/png
  • The term application is generally used for types of software rather than image formats.
• png/image
  • This format is incorrect because MIME types always follow the pattern type/subtype, and image/png is the standard for PNG images.
• png/application
  • Similar to the first option, this is incorrect because it misplaces the terms and does not follow the MIME type convention.