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Business Intelligence Foundation Professional Certification Exam Answers – Certiprof

Business Intelligence Foundation Professional Certification Exam Answers – Certiprof

  • Foreign Key.
  • Composite Primary Key.
  • Primary Key.
  • True.
  • False.
  • It is the location where the data being used comes from.
  • A BI software.
  • A Data Mining Platform.
  • Perform analysis for each source.
  • Data integration.
  • Request to unify data sources.
  • Export data.
  • Data relationship model.
  • Qlik Sense.
  • Power BI.
  • Primary key.
  • ETL.
  • Descriptive analysis.
  • Prescriptive Analysis.
  • Predictive analytics.
  • True.
  • False.
  • True.
  • False.
  • Historical Analysis.
  • Descriptive Analysis.
  • Referential analysis.
  • Descriptive Analytics.
  • Historical Analytics.
  • Predictive Analytics.
  • Prescriptive Analytics.
  • Advanced programmer.
  • BI Developer.
  • Data science.
  • BI Analysis.
  • Observe.
  • Understand.
  • Predict.
  • Decide.
  • Transformation Processes.
  • Extraction Processes.
  • Data Loading Processes.
  • Predict.
  • Collaborate.
  • Observe.
  • Understand.
  • Business Improve.
  • Business Impact.
  • Business Intelligence.
  • Business Insider.
  • Time increase.
  • Reduction of information security.
  • Identification of new business opportunities.
  • Expand the company’s value chain.
  • Databases.
  • Flat files.
  • Registration data.
  • All of the above.
  • True.
  • False.
  • Prescriptive Analytics.
  • Predictive Analytics.
  • Descriptive Analytics.
  • Historical Analytics.
  • Identify key stakeholders.
  • Failure to define the scope.
  • Have a roadmap.
  • A and B are correct.
  • A and C are correct.
  • None is correct.
  • All are correct.
  • As an Internet browser.
  • As Software to create reports and analyze data.
  • To Generate Graphics.
  • As a Spreadsheet.
  • Transformation model.
  • Data relationship model.
  • ETL Model.
  • Granularity model.
  • True.
  • False.
  • It is a single field within a table and is used to establish relationships between tables.
  • It’s to make my Tablet look pretty.
  • It is any field in the table.
  • It is a field used to protect my database.
  • External, Transform, Load.
  • Extract, Transform, Load.
  • Extract, Transaction, Loading.
  • Extract, Transform, Loop.
  • Database
  • Entity.
  • Foreign key
  • Primary key.
  • External.
  • Internal.
  • Foreign.
  • Primary.
  • Identify the tools to be used.
  • Identify key stakeholders.
  • To have external expert advisors.
  • Data extraction.
  • Data transformation.
  • Data deletion.
  • Data upload.
  • Observing, Compiling, Predicting, Collaborating, Deciding.
  • Observing, Understanding, Programming, Collaborating, Deciding.
  • Observing, Understanding, Predicting, Collaborating, Dialoguing.
  • Observing, Understanding, Predicting, Collaborating, Deciding.
  • Datawarehouse.
  • Accumulation.
  • ETL.
  • Rolling.
  • 1-to-1, 1-to-Many, many-to-one, many-to-many, many-to-many relationships.
  • Many-to-many relationships.
  • No relationships between tables are established.
  • 1 to 1 ratio.
  • Data Science.
  • Business intelligence.
  • Data Analytics.
  • Data Information.
  • Recurring.
  • Predictive.
  • Prescriptive.
  • Descriptive.
  • True.
  • False.
  • Analysis to establish relationships between tables.
  • It is a diagram where data are related by their shapes.
  • Both of the above.
  • None of the above.
  • Simple extraction.
  • Complete Extraction.
  • Incremental Extraction.
  • All are correct.
  • A and B are correct.
  • A and C are correct.
  • B and C are correct.
  • None of the above.
  • Inverted.
  • Star.
  • Objects.
  • Structured.
  • Predictive.
  • Descriptive.
  • Recurring.
  • Prescriptive.
  • Data Visualization.
  • Data quality management and monitoring.
  • Dynamic Report Generation.
  • All of the above.
  • True.
  • False.
  • Predictive Analytics.
  • Diagnostic Analysis.
  • Descriptive Analytics.
  • Prescriptive Analytics.
  • ETL.
  • Predictive analytics.
  • Prescriptive Analysis.
  • Descriptive analysis.
  • Prescriptive Analysis.
  • Predictive analytics.
  • Descriptive analysis.
  • ETL.
  • True.
  • False.
  • Data integration.
  • Promote business sales.
  • Facilitate and improve decision making.
  • Facilitate data collection.
  • Saas.
  • Extract, Re-engineer, Programming.
  • Extract, Transform, Load.
  • Extract, Program, Load.
  • Organization, Budget and Administration.
  • Decisions, Analysis and Proposals.
  • Decisions, Big Data Consumption and Proposals.
  • Time savings, data clarity and customizable reporting.
  • Business intelligence.
  • Business intelligence.
  • Production intelligence.
  • Management intelligence.
  • Integration of different data sources and with other platforms.
  • Variety of languages.
  • Free.
  • Multiplatform.
  • ETL.
  • Datawarehouse.
  • Data mining.
  • Analysis.
  • Compatibility problems.
  • Unstructured data correctly.
  • Data security.
  • All of the above.
  • Foreign key.
  • Primary key.
  • Secondary key.
  • None of the above.
  • Data Sources.
  • ETL.
  • Data Repository.
  • Access Interface.
  • All of the above.
  • Prescriptive.
  • Recurring.
  • Predictive.
  • Descriptive.
  • Descriptive Analytics.
  • Predictive Analytics.
  • Diagnostic Analysis.
  • Prescriptive Analytics.
  • True.
  • False.
  • SAAS.
  • ETL.
  • Transformation.
  • Load.
  • Data Transformation.
  • Data Extraction.
  • Data Modeling.
  • Data Loading.
  • ETL.
  • Primary key.
  • Data relationship model.
  • Power BI.
  • Data loading.
  • Data extraction.
  • Data transformation.
  • All of the above.
  • Data analysis.
  • Data science.
  • BI Analysis.
  • None of the above.
  • Data loading.
  • Data extraction.
  • Data transformation.
  • All of the above.
  • Primary key.
  • Secondary key.
  • Data relationship model.
  • Data extraction.
  • Organized collection of information.
  • Confirmation of data upload.
  • Existence of business data.
  • Level of detail at which business information is stored.
  • To save costs in the company.
  • To adapt to the market.
  • To achieve the necessary agility without sacrificing data security.
  • To forecast data.
  • Extract information and analyze it.
  • Storing data in a data warehouse.
  • Load the information in a new destination.
  • Convert data from one format to a more user-friendly format.
  • Objects.
  • Structured.
  • Star.
  • Inverted.
  • True.
  • False.
  • Incremental.
  • Complete.
  • Progressive.
  • Closed.
  • Concrete information about facts, elements, etc., that allows them to be studied, analyzed or known.
  • The registration of an event.
  • One variable.
  • A numeric value, generated by a transaction.
  • Knowledge Panning Indicator.
  • Key Planning Indicator.
  • Key Performance Indicator.
  • None of the above.
  • Common key.
  • Structured key.
  • Primary key.
  • Foreign key.
  • Collaborate.
  • Observe.
  • Decide.
  • Predict.
  • Specialized programming and high cost.
  • Larger data repository size.
  • Intuitive, easy to use, higher processing data loading.
  • It is the process for moving data from multiple sources.
  • A Data connection protocol.
  • A tool for importing and transforming data.
  • None of the above.
  • Extract data.
  • Transform data.
  • Create data.
  • Load data.
  • All of the above.
  • It is an organized collection of structured information or data, usually stored electronically in a computer system.
  • Repository where all the transactional information of a company is transferred.
  • Data Warehouse.
  • All of the above.
  • File System.
  • Entity – Relationship Diagram.
  • Database.
  • Extraction, Transformation and Cleaning.
  • Extraction, Transformation and Load.
  • Extraction, Transformation and Transportation.

Important Keys to Remember:

  • Observe: What is happening?
  • Understand: Why it happens?
  • Predict: What would happen?
  • Collaborate: What should the team do?
  • Decide: Which way to go?

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