MC	Which statement is NOT CORRECT?	One way to deal with rapidly changing dimensions is by splitting the information into stable and rapidly changing information.  The latter can then be put into a separate mini-dimension table with a new surrogate key.  The connection can then be made by using the fact table or by introducing a new table connecting both.	incorrect	Junk dimensions can be defined to efficiently accommodate low cardinality attribute types such as flags or indicators.	incorrect	An outrigger table can be defined to store a set of attribute types of a dimension table which are uncorrelated, high in cardinality and updated simultaneously.	correct	For slowly changing dimensions, surrogate keys can be handy to store the historical information by duplicating a record and adding, e.g., Start_Date, End_Date and Current_Flag attribute types.	incorrect	
MC	Which statement is CORRECT?	In the context of an ETL process, an incremental extraction strategy only extracts data from specific business units.	incorrect	During the ETL process, a staging area can be used to decrease the burden on the operational systems and the data warehouse.  This area can then be used to execute (complex) queries.	incorrect	During the loading step of the ETL process, the data warehouse is populated by filling the fact and dimension tables. Dimension rows should be inserted/updated before the fact rows are inserted/updated.	correct	The ETL process is only executed right before the data warehouse is made operational.	incorrect	
MC	Which statement is NOT CORRECT?	A fact constellation schema has more than one fact table.	incorrect	A snowflake schema normalizes the dimension tables such that report generation may go faster because there are less joins needed.	correct	Star schemas typically lead to duplicate storage of information.	incorrect	In a star schema, the dimension tables often contain denormalized data.	incorrect	
MC	Which statement is CORRECT?	To speed up report generation and avoid time consuming joins in a star schema, the dimension tables need to be normalized.	incorrect	A data lake assumes that data schema definitions are determined when the data are loaded (schema-on-write).	incorrect	Surrogate keys are usually larger than business keys to speed up query execution time.	incorrect	During the loading step of the ETL process, the data warehouse is populated by filling the fact and dimension tables. Dimension rows should be inserted/updated before the fact rows are inserted/updated.	correct	
MC	Which statement is NOT CORRECT?	HOLAP tries to combine the best of both ROLAP and MOLAP.	incorrect	Pivot tables are used to represent multidimensional data in a two-dimensional tabular format.	incorrect	MOLAP scales better to more dimensions than ROLAP.	correct	OLAP allows you to interactively analyze the data, summarize it, and visualize it in various ways	incorrect	
MC	Which statement is NOT CORRECT?	Junk dimensions can be defined to efficiently accommodate low cardinality attribute types such as flags or indicators.	incorrect	A charecteristic of data warehouses is that they are time-variant.	incorrect	Roll-up (or drill-up) refers to aggregating the current set of fact values within or across one or more dimensions.	incorrect	The information system needed to make operational decisions is often referred to as a decision support systems (DSS).	correct	
MC	Which statement is CORRECT?	The key difference between the CUBE and ROLLUP OLAP operators is that the former generates a result set showing the aggregates for a hierarchy of values of the specified attribute types, whereas the latter generates a result set showing the aggregates for all combinations of values of the selected attribute types.	incorrect	In OLAP, Windowing allows calculating cumulative totals or running averages based on a specified window of values	correct	Contrary to the RANK() OLAP function, the DENSE_RANK() OLAP function leaves gaps between the ranks.  	incorrect	The GROUPING SETS OLAP operator generates a result set equivalent to that generated by an INTERSECT of multiple simple GROUP BY clauses.	incorrect	
MC	Which statement is NOT CORRECT?	At the operational level, day to day business decisions are made, typically in real-time or with a short time frame.	incorrect	At the tactical level, decisions are made by middle management with a medium-term (e.g. a month, a quarter, a year) focus.	incorrect	A data warehouse provides a centralized, consolidated data platform by integrating data from different sources and in different formats.  As such, it provides a separate and dedicated environment for operational decision making.	correct	At the strategic level, decisions are made by senior management with long term implications (e.g., 1, 2, 5 years or more)	incorrect	
MC	Given the following SALESTABLE<br><table><tbody><tr><th>PRODUCT</th><th>QUARTER</th><th>REGION</th><th>SALES</th></tr><tr><td>A</td><td>Q1</td><td>Europe</td><td>10</td></tr><tr><td>A</td><td>Q1</td><td>America</td><td>20</td></tr><tr><td>A</td><td>Q2</td><td>Europe</td><td>20</td></tr><tr><td>A</td><td>Q2</td><td>America</td><td>50</td></tr><tr><td>A</td><td>Q3</td><td>America</td><td>20</td></tr><tr><td>A</td><td>Q4</td><td>Europe</td><td>10</td></tr><tr><td>A</td><td>Q4</td><td>America</td><td>30</td></tr><tr><td>B</td><td>Q1;</td><td>Europe</td><td>40</td></tr><tr><td>B</td><td>Q1</td><td>America</td><td>60</td></tr><tr><td>B</td><td>Q2</td><td>Europe</td><td>20</td></tr><tr><td>B</td><td>Q2</td><td>America</td><td>10</td></tr>  <tr><td>B</td><td>Q3</td><td>America</td><td>20</td></tr>  <tr><td>B</td><td>Q4</td><td>Europe</td><td>10</td></tr>  <tr><td>B</td><td>Q4</td><td>America</td><td>40</td></tr></tbody></table><br><br>What is the result of the following query?<br><br><b>SELECT</b> QUARTER, REGION, <b>SUM</b>(SALES)<br><b>FROM</b> SALESTABLE<br><b>GROUP BY GROUPING SETS</b> ((QUARTER), (REGION))	An error.	incorrect	<table><tbody><tr><th>QUARTER</th><th>REGION</th><th>SALES</th></tr><tr><td>Q1</td><td>NULL</td><td>130</td></tr><tr><td>Q2</td><td>NULL</td><td>100</td></tr><tr><td>Q3</td><td>NULL</td><td>40</td></tr><tr><td>Q4</td><td>NULL</td><td>90</td></tr><tr><td>NULL</td><td>Europe</td><td>110</td></tr><tr><td>NULL</td><td>America</td><td>250</td></tr></tbody></table>	correct	<table><tbody><tr><th>QUARTER</th><th>REGION</th><th>SALES</th></tr><tr><td>Q1</td><td>Europe</td><td>50</td></tr><tr><td>Q1</td><td>America</td><td>80</td></tr><tr><td>Q2</td><td>Europe</td><td>40</td></tr><tr><td>Q2</td><td>America</td><td>60</td></tr><tr><td>Q3</td><td>Europe</td><td>NULL</td></tr><tr><td>Q3</td><td>America</td><td>40</td></tr><tr><td>Q4</td><td>Europe</td><td>20</td></tr><tr><td>Q4</td><td>America</td><td>80</td></tr><tr><td>Q1</td><td>NULL</td><td>130</td></tr><tr><td>Q2</td><td>NULL</td><td>100</td></tr><tr><td>Q3</td><td>NULL</td><td>40</td></tr><tr><td>Q4</td><td>NULL</td><td>90</td></tr><tr><td>NULL</td><td>Europe</td><td>110</td></tr><tr><td>NULL</td><td>America</td><td>250</td></tr><tr><td>NULL</td><td>NULL</td><td>360</td></tr></tbody></table>	incorrect	<table><tbody><tr><th>QUARTER</th><th>REGION</th><th>SALES</th></tr><tr><td>Q1</td><td>Europe</td><td>50</td></tr><tr><td>Q1</td><td>America</td><td>80</td></tr><tr><td>Q2</td><td>Europe</td><td>40</td></tr><tr><td>Q2</td><td>America</td><td>60</td></tr><tr><td>Q3</td><td>Europe</td><td>NULL</td></tr><tr><td>Q3</td><td>America</td><td>40</td></tr><tr><td>Q4</td><td>Europe</td><td>20</td></tr><tr><td>Q4</td><td>America</td><td>80</td></tr><tr><td>Q1</td><td>NULL</td><td>130</td></tr><tr><td>Q2</td><td>NULL</td><td>100</td></tr><tr><td>Q3</td><td>NULL</td><td>40</td></tr><tr><td>Q4</td><td>NULL</td><td>90</td></tr><tr><td>NULL</td><td>NULL</td><td>360</td></tr></tbody></table>	incorrect	
MC	A data warehouse is:	Subject-oriented, integrated, time-variant and volatile.	incorrect	Subject-oriented, integrated, time-variant and non-volatile.	correct	Time-oriented, integrated, time-variant and volatile.	incorrect	Subject-oriented, integrated, time-invariant and non-volatile.	incorrect