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NEW QUESTION: 1
How many really suffer as a result of labor market problems ? This is one of the most critical yet contentious social policy questions. In many ways, our social statistics exaggerate the degree of har-ship.
Unemployment does not have the same dire consequences today as it did in the 1930's when most of the unemployed were primary bread-winners, when income and earnings were usually much closer to the margin of subsistence, and when there were no countervailing social programs for those failing in the labor market. Increasing affluence, the rise of families with more than one wage earner, the growing predominance of secondary earners among the unemployed, and improved social welfare protection have unquestionably mitigated the consequences of joblessness. Earnings and income data also overstate the dimensions of hard-ship. Among the millions with hourly earnings at or below the minimum wage level, the overwhelming majority are from multiple-earner, relatively affluent families. Most of those counted by the poverty statistics are elderly or handicapped or have family responsibilities which keep them out of the labor force, so the poverty statistics are by no means an accurate indicator of labor market pathologies.
Yet there are also many ways our social statistics underestimate the degree of labour-market-related hardship. The unemployment counts exclude the millions of fully employed workers whose wages are so low that their families remain in poverty. Low wages and repeated or prolonged unemployment frequently interact to undermine the capacity for self-support. Since the number experiencing job-lessness at some time during the year is several times the number unemployed in any month, those who suffer s a result of forced idleness can equal or exceed average annual unemployment, even though only a minority of the jobless in any month really suffer. For every person counted in the month unemployment tallies, there is another working part-time because of the inability to find full-time work, or else outside the labor force but wanting a job. Finally, income transfers in our country have always focused on the elderly, disabled, and dependent, neglecting the needs of the working poor, so that the dramatic expansion of cash and in kind transfers does not necessarily mean that those failing in the labor market are adequately protected.
As a result of such contradictory evidence, it is uncertain whether those suffering seriously as a result of labor market problems number in the hundreds of thousands or the tens of millions, and hence, whether high levels of joblessness can be tolerated or must be countered by job creation and economic stimulus.
There is only one area of agreement in this debate-that the existing poverty, employment, and earnings statistics are inadequate for one of their primary applications, measuring the consequences of labor market problems.
The conclusion stated about the number of people who suffer as a result of forced idleness depends primarily on the point that
A. those in forced idleness include, besides the unemployed, both underemployed part-time workers and those not actively seeking work
B. in times of high unemployment, there are some people who do not remain unemployed for long
C. at different times during the year, different people are unemployed
D. the capacity for self-support depends on receiving moderate-to-high wages
E. many of those who are affected by unemployment are dependents of unemployed workers.
Answer: C
Explanation:
Explanation/Reference:
Explanation:

NEW QUESTION: 2
Match the types of visualizations to the appropriate descriptions.
To answer, drag the appropriate visualization type from the column on the left to its description on the right.
Each visualization type may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.

Answer:
Explanation:

Explanation

Box 1: Tree map
Treemaps are charts of colored rectangles, with size representing value. They can be hierarchical, with rectangles nested within the main rectangles.

Box 2: Key influencer
A key influencer chart displays the major contributors to a selected result or value.
Box 3: Scatter
Scatter and Bubble charts display relationships between 2 (scatter) or 3 (bubble) quantitative measures -- whether or not, in which order, etc.

NEW QUESTION: 3
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in the series.
Start of repeated scenario
You have a Microsoft SQL Server data warehouse instance that supports several client applications.
The data warehouse includes the following tables: Dimension.SalesTerritory, Dimension.Customer, Dimension.Date, Fact.Ticket and Fact.Order. The Dimension.SalesTerritory and Dimension.Customer tables are frequently updated. The Fact.Order table is optimized for weekly reporting, but the company wants to change it to daily. The FactOrder table is loaded by using an ETL process. Indexes have been added to the table over time, but the presence of these indexes slows data loading.
All data in the data warehouse is stored on a shared SAN. All tables are in a database named DB1. You have a second database named DB2 that contains copies of production data for a development environment. The data warehouse has grown and the cost of storage has increased. Data older than one year is accessed infrequently and is considered historical.
* Implement table partitioning to improve the manageability of the data warehouse and to avoid the need to repopulate all transactional data each night Use a partitioning strategy that is as granular as possible.
* Partition the FactOrder table and retain a total of seven years of data.
* Partition the Fact.Ticket table and retain seven years of data. At the end of each month, the partition structure must apply a sliding window strategy to ensure that a new partition is available for the upcoming month, and that the oldest month of data is archived and removed.
* Optimize data loading for the Dimension.SalesTerritory, Dimension.Customer, and Dimension.Date tables.
* Incrementally load all tables in the database and ensure that all incremental changes are processed.
* Maximize the performance during the data loading process for the Fact.Order partition.
* Ensure "that historical data remains online and available for querying.
* Reduce ongoing storage costs while maintaining query performance for current data.
You are not permitted to make changes to the client applications.
End of repeated scenario
You need to optimize data loading for the Dimension.SalesTerritory, Dimension.Customer, and Dimension.Date tables.
Which technology should you use for each table?
To answer, select the appropriate technologies in the answer area.


Answer:
Explanation:

Explanation

Box 1: Temporal table
Box 2: Temporal table
Compared to CDC, Temporal tables are more efficient in storing historical data as it ignores insert actions.
Box 3: Change Data Capture (CDC)
By using change data capture, you can track changes that have occurred over time to your table. This kind of functionality is useful for applications, like a data warehouse load process that need to identify changes, so they can correctly apply updates to track historical changes over time.
CDC is good for maintaining slowly changing dimensions.
Scenario: Optimize data loading for the Dimension.SalesTerritory, Dimension.Customer, and Dimension.Date tables.
The Dimension.SalesTerritory and Dimension.Customer tables are frequently updated.
References:
https://www.mssqltips.com/sqlservertip/5212/sql-server-temporal-tables-vs-change-data-capture-vs-change-track
https://docs.microsoft.com/en-us/sql/relational-databases/tables/temporal-table-usage-scenarios?view=sql-server

NEW QUESTION: 4
Given this enum declaration:

Examine this code:
System.out.println(Letter.values()[1]);
What code should be written at line 5 for this code to print 200?
A. String toString() { return "200"; }
B. public String toString() { return String.valueOf(Letter.values()[1]); }
C. public String toString() { return String.valueOf(ALPHA.v); }
D. public String toString() { return String.valueOf(v); }
Answer: D
Explanation: