Data Contextualization — Learn free

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Questions 1–10 of 10

1. Data Contextualisation has emerged as a critical factor in dealing with which kind of datasets?
Question 1A. Small, static, well-structured reference tablesB. Vast, rapidly changing and diverse unstructured datasetsC. Encrypted datasets held by regulatorsD. Archived datasets no longer in useE. Datasets stored only on tape
2. Which everyday situation is used to explain the idea of context in Data Contextualisation?
Question 2A. Reading a map before a journeyB. Filing a tax returnC. Backing up a laptopD. Choosing a database vendorE. Opening a meeting with management by acknowledging them and stating its purpose
3. What does unstructured data typically contain, according to the case for Data Contextualisation?
Question 3A. A substantial amount of information that is irrelevant to decision-makingB. Only data that has already been validatedC. Perfectly consistent metadataD. No information of any valueE. Only numeric measurements
4. How does filtering irrelevant data through Data Contextualisation help?
Question 4A. It increases the volume of data available for analysisB. It makes all data structuredC. It reduces the overall data volume, simplifying processing, cleaning, transformation and maintenanceD. It removes the need for data governanceE. It guarantees complete data accuracy
5. How does Data Contextualisation enhance decision-making?
Question 5A. By removing all external data from considerationB. By replacing human decisions with automated rulesC. By limiting analysis to a single data sourceD. By combining external data with internal data to give a more comprehensive foundationE. By encrypting decision records
6. What reduction in data processing, cleaning, transformation and maintenance effort is suggested from a robust Data Contextualisation process?
Question 6A. About 5 to 10 percent, as confirmed by industry benchmarksB. Approximately 30 to 40 percent, based on personal experience rather than formal statisticsC. Exactly 50 percent, as set by regulationD. Around 80 to 90 percent, as measured in formal studiesE. No reduction; the effort increases
7. In the student exam scores example of Data Contextualisation, what contextual data is added to the scores?
Question 7A. Teachers' salaries and school budgetsB. Students' home addresses and agesC. Exam paper difficulty ratingsD. Textbook prices and library opening hoursE. Study hours, attendance records and participation in class discussions
8. In the student exam scores example, what serves as the raw data?
Question 8A. The exam scoresB. The attendance recordsC. The study hoursD. The class participation notesE. The correlation results
9. Which of these is NOT listed as a reason why Data Contextualisation is essential?
Question 9A. Filtering irrelevant dataB. Enhancing decision-makingC. Encrypting sensitive dataD. Providing clarityE. Adding points of interest
10. In Data Contextualisation, on what does the relevance of a data element depend?
Question 10A. The size of the datasetB. The age of the dataC. The format in which the data is storedD. The specific decision being madeE. The number of users who access it