Data warehousing options often differ by one word, such as volatile versus non-volatile, or by one layer, such as operational detail versus warehouse summary. You may know the chapter and still lose the mark by reading too quickly.
A data warehouse supports analysis by integrating historical data from multiple operational sources. Its defining properties are subject orientation, integration, time variance and non-volatility. Cuboids and summaries organise aggregate views, while data scrubbing improves source quality. Business intelligence, CRM processing and SSIS apply these foundations. Choose an option before opening each explanation, then check the reasoning against the worked product example. UGC NET CS Exam Preparation places the topic in the wider syllabus. Questions 2 and 4 have no individual solved page, so use the Data Warehousing practice hub for them. Each other question links directly to its solved page.
Data warehousing facts to lock before solving
Keep the operational system and the warehouse separate:
Point | OLTP system | Data warehouse |
|---|---|---|
Purpose | Day-to-day transactions | Analysis and decision support |
Data | Current detailed rows | Integrated historical and summary data |
Workload | Short read/write operations | Long read-heavy queries |
Updates | Continuous updates | Controlled ETL or ELT loads |
Orientation | Process-oriented | Subject-oriented |
A warehouse is subject-oriented around areas such as sales, integrated across sources, time-variant across dated states and non-volatile for analytical use. Non-volatile does not mean data is never loaded. It means ordinary analytical users do not continually overwrite transactional rows.
Consider this ETL example:
Source | Product identifier | Quantity | Date | Product key |
|---|---|---|---|---|
A |
| 12 |
| 501 |
B |
| 9 |
| 501 |
C |
| 14 |
| 501 |
ETL maps all three identifiers to the same product key, 501, showing integration. The three time-stamped fact rows show time variance and controlled loading. Their product roll-up is 12 + 9 + 14 = 35.
Data Warehousing MCQs 1-3: definition and defining characteristics
Question 1
What is data warehouse?
A. A database that stores only current transactional data
B. A centralized repository of integrated data collected from multiple sources
C. A temporary cache used for Quick access to web pages
D. A cloud storage system for raw data
Correct answer: B. A centralized repository of integrated data collected from multiple sources.
A warehouse centralises and reconciles data from several sources for analysis, as P-17, P017 and 17 become product_key=501. A is an operational store, C a cache and D generic raw-data storage.
Solved page: What is data warehouse?
Question 2
The important aspect of the data warehouse environment is that data found within the Data Warehouse is___________.
A. Subject-oriented.
B. Time-variant.
C. Integrated.
D. All of the above.
Correct answer: D. All of the above.
Check A, B and C separately. Products show subject orientation, dated rows show time variance, and mapping three identifiers to key 501 shows integration. All three are valid, so D is correct.
Question 3
Which of the following is NOT a valid characteristic of a data warehouse?
A. Volatile
B. Time-variant
C. Subject-oriented
D. Integrated
Correct answer: A. Volatile.
NOT flips the test. Time-variant, subject-oriented and integrated are valid. The fourth characteristic is non-volatile. Scheduled loads may refresh data, but routine analytical use does not continually overwrite transactional rows.
Solved page: Which characteristic is not valid?
Data Warehousing MCQs 4-6: read access, summary data and cuboids
Question 4
The data Warehouse is__________.
A. Read only.
B. Write only.
C. Read write only.
D. None
Correct answer: A. Read only.
Analysts and BI tools primarily read a warehouse, so A matches the exam's intended distinction. Controlled ETL, ELT or change-data-capture jobs still write data, but this managed loading is not ordinary transactional read/write activity.
Question 5
Data warehouse contains _____ data that is never found in operational environment.
A. Summary
B. Encoded
C. Encrypted
D. Scripted
Correct answer: A. Summary.
Operational systems preserve the quantity events 12, 9 and 14. A warehouse may also store their roll-up, 12 + 9 + 14 = 35, for analysis. That is summary data. The other options do not name a data category.
Solved page: Data warehouse summary data question
Question 6
In data warehousing , a cuboid is used to represent what?
A. Summarized data from the data cube
B. Raw transactional data without aggregation
C. A single dimension table
D. Temporary staging data
E. None of the above
Correct answer: A. Summarized data from the data cube.
A cuboid is one aggregation level over selected dimensions. Here, (product, date) retains the dated quantities 12, 9 and 14, while (product) rolls them up to 35. It is neither a dimension table nor staging data.
Solved page: Cuboid use in data warehousing
Data Warehousing MCQs 7-9: BI use and data scrubbing
Question 7
Consider the following two statements : (A) Business intelligence and Data warehousing is used for forecasting and Data mining. (B) Business intelligence and Data warehousing is used for analysis of large volumes of sales data. Which one of the following options is correct ?
A. (A) is true, (B) is false.
B. Both (A) and (B) are true.
C. (A) is false, (B) is true.
D. Both (A) and (B) are false.
Correct answer: B. Both (A) and (B) are true.
Judge the statements independently. Historical data supports forecasting and mining, so A is true. Warehouses also analyse large sales datasets, so B is true. The 35-unit roll-up is a small example. Option B preserves both truths.
Solved page: Business intelligence and data warehousing statements
Question 8
Consider the following two statements : (A) Data scrubling is a process to upgrade the quality of data, before it is moved into Data warehouse. (B) Data scrubling is a process of rejecting data from data warehouse to create indexes. Which one of the following options is correct ?
A. (A) is true, (B) is false.
B. (A) is false, (B) is true.
C. Both (A) and (B) are false.
D. Both (A) and (B) are true.
Correct answer: A. (A) is true, (B) is false.
The standard term is data scrubbing. Here it improves source quality before loading, so A is true. Indexing is a separate performance task and does not require rejecting warehouse data, so B is false.
Solved page: Data scrubling statements
Question 9
Data scrubbing is
A. A process to reject data from the data warehouse and to create the necessary indexes.
B. A process to load the data in the data warehouse and to create the necessary indexes.
C. A process to upgrade the quality of data after it is moved into a data warehouse
D. A process to upgrade the quality of data before it is moved into a data warehouse
Correct answer: D. A process to upgrade the quality of data before it is moved into a data warehouse.
Mapping P-17, P017 and 17 to key 501 before loading is a quality and consistency transformation, so D matches. A and B mix cleansing with indexing; C reverses the timing assumed here. Modern ELT may clean after landing.
Solved page: Data scrubbing definition
Data Warehousing MCQs 10-11: processing requirements and SSIS practice
Question 10
Sorting and retrieving data from a data warehouse comes under ______ requirements of a CRM tool.
A. Marketing
B. Financial
C. Processing
D. Personnel
Correct answer: C. Processing.
Sorting and retrieving are operations on data, so they are processing requirements. Marketing, financial and personnel are functional areas, not the mechanics of organising and fetching warehouse data.
Solved page: CRM tool processing requirements
Question 11
Which of the following is the best practice related to SSIS?
A. Plan for capacity by understanding resource utilization
B. SSIS is an in-memory pipeline, so ensure that all transformations occur in memory
C. Baseline source system extract speed
D. All of the mentioned
Correct answer: D. All of the mentioned.
Capacity planning measures CPU, memory, I/O and network demand. Keeping transformations in memory avoids disk spooling, while baselining source extraction identifies the upstream throughput ceiling. A, B and C therefore hold independently, so D is the combined answer.
Solved page: SSIS best-practice question
Traps in data warehousing MCQs
Cue | What it should trigger |
|---|---|
Centralised and integrated repository | Q1: distinguish a warehouse from a cache or operational store |
All of the above | Q2: verify all three statements independently |
| Q3: reverse the four-characteristic check |
Read-only | Q4: separate analyst access from controlled loading |
Detail, summary and cuboid | Q5 and Q6: locate the aggregation level |
Cleansing and indexing | Q8 and Q9: keep quality work separate from access tuning |
Your 30-second routine is simple: circle NOT; evaluate statement A and B independently; expand every all of the above; name the warehouse layer; and test the answer against 12, 9, 14 and the summary 35.
Use DBMS Transaction MCQs for the OLTP side. Then contrast data scrubbing with normalization: scrubbing cleans source values, while normalization restructures relations to reduce redundancy and update anomalies.
The next data warehousing practice step
Redo Questions 2, 3, 6, 8 and 11 without looking. Score each one before rereading the explanation. The concepts tested are combined options, a negative stem, cuboid terminology, two-statement logic and an all-of-the-mentioned tool question.
For the wider Computer Science route with databases and PYQ practice, continue with NTA-UGC-NET Paper 2. For timed Paper 2 practice, use the UGC NET Computer Science and Applications Test Series.
The short version: a warehouse integrates historical subject data for analysis. ETL or ELT prepares and loads it. Summaries and cuboids make analysis faster. Analytical access is read-heavy, while managed loads still change warehouse contents.
