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000-455 - High Volume Storage Fundamentals V3 - Dump Information

Vendor : IBM
Exam Code : 000-455
Exam Name : High Volume Storage Fundamentals V3
Questions and Answers : 73 Q & A
Updated On : November 17, 2017
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000-455 Questions and Answers

000-455


The customer’s requirements include a 2U controller. 24300 GB 15k drives, with host connections of SAS and 10Gb SCSI. The customer has no requirement for data migration. Which system will meet the customer’s requirements?


  1. DS3524

  2. DS5020

  3. StorwizeV3700

  4. StorwizeV7000


Answer: A


QUESTION: 64

In a pre-sales meeting with a customer, the customer is not sure if the database application causes many I/Os or a large amount of MB/s on the storage system. What question is most important to ask?


  1. How may users will use the database concurrently?

  2. Is the application a farm of small individual web servers?

  3. What type of transactions are generated from the database?

  4. Is the application a video storage library for online video streaming?


Answer: C


QUESTION: 65

A customer is interested in a Storwize V3700 or Flex System solution, but they want to protect the investment they recently made in top-of-rack network switching. What should the technical specialist recommend?


  1. FC pass-thru modules

  2. Ethernet switches

  3. Managed switches

  4. PC pass-thru switches Answer: A QUESTION: 66


Using current technology, which type and speed of drives are best allocated for archival purposes?


  1. SSD

  2. FC15k

  3. Near line SAS 7.2k

  4. IDE 5400


Answer: C


QUESTION: 67

What interlaces can be configured on LTO-6 tape drives?


  1. 8Gb Fibre Channel or SCSI

  2. 4Gb Fibre Channel or 6Gb SAS

  3. 8Gb Fibre Channel or 6Gb SAS

  4. 16Gb Fibre Channel or 6Gb SAS


Answer: C


QUESTION: 68

Which two of the following are types of FlashCopy? (Select two)


  1. Peer-to-peer

  2. Child-parent

  3. Incremental

  4. Multi-source

  5. Reverse


Answer: C, E


QUESTION: 69

A small customer needs to upgrade their tape environment from Ultrium 4 to current technology. The technical specialist is recommending the TS2900 to meet current and future needs. Which of the following is the business value of the proposed solution?


  1. TS2900 supports replication

  2. LTO technology provides for tape pooling

  3. TS2900 can connect to System x, Power systems, and System z

  4. LTO standard provides for backward compatibility


Answer: D


QUESTION: 70

On a DS3524, what is an advantage of Dynamic Disk Pools?


  1. Uses Easy Tier to increase throughput

  2. Significantly faster return to optimal state following a drive failure

  3. Uses compression to define which drives should be used for segment placement

  4. Uses thin provisioning to define which drives should be used for segment placement


Answer: B


QUESTION: 71

Calculating how quickly the business needs to recover will determine what kind of preparations are necessary when implementing a business continuity plan. This target for the resumption of product, service or activity delivery after an incident is defined as:


  1. RTO

  2. RPO

  3. RTC

  4. RTP


Answer: A


QUESTION: 72

The technical development team at a local customer has recommended the IBM entry storage solution that was presented. The next presentation will include IT management and the 010. and they will need to see the business value in order to get the approvals needed for the purchase order. What tool would provide the information needed for this presentation?


  1. Disk Magic

  2. Alinean

  3. Power Calculator

  4. Capacity Magic


Answer: B


QUESTION: 73

Which characteristic makes the IBM Storwize V3700 more efficient in fully loaded data centers compared to Dell EqualLogic - PS4100E/4110E?


  1. the Storwize V3700 offers more host interlaces

  2. the Storwize V3700 offers different drive options

  3. the Storwize V3700 is more efficient due to lower height of the system

  4. the Storwize V3700 is more efficient because it produces less C02e kg pa


Answer: D


IBM 000-455 Exam (High Volume Storage Fundamentals V3) Detailed Information

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  • SPSS statistics analysis Examples SPSS data analysis ExamplesProbit Regression

    Probit regression, also referred to as a probit mannequin, is used to model dichotomous or binary outcome variables. within the probit model, the inverse usual common distribution of the probability is modeled as a linear mixture of the predictors.

    Please word: The goal of this web page is to demonstrate how to use a considerable number of facts analysis commands. It does not cowl all points of the research method which researchers are anticipated to do. In selected, it does not cowl facts cleansing and checking, verification of assumptions, mannequin diagnostics and abilities follow-up analyses.

    Examples

    example 1:  consider that we are interested in the factors that impact even if a political candidate wins an election.  The outcomes variable is binary (0/1);  win or lose.  The predictor variables of interest are the amount of cash spent on the campaign, the amount of time spent campaigning negatively, and whether the candidate is an incumbent.

    example 2:  A researcher is interested in how variables, equivalent to GRE (Graduate record examination rankings), GPA (grade aspect usual), and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don't admit, is a binary variable.

    Description of the records For our facts evaluation under, we are going to extend on example 2 about getting into graduate school. we have generated hypothetical records, which may also be acquired with the aid of clicking on binary.sav. which you can save this anywhere you adore, however our examples will anticipate it has been saved in c:\information. First, we examine the records file into SPSS. get file = "c:\records\probit.sav".

    This facts set has a binary response (influence, elegant) variable called admit. There are three predictor variables: gre, gpa and rank. we are able to deal with the variables gre and gpa as continual. The variable rank is ordinal, it takes on the values 1 via 4. institutions with a rank of 1 have the maximum status, while these with a rank of four have the lowest. we will treat rank as specific. Lets birth by looking at descriptive statistics.

    descriptives /variables=gre gpa. Descriptive records N minimal optimum mean Std. Deviation gre 400 220 800 587.70 115.517 gpa 400 2.26 4.00 3.3899 .38057 valid N (listwise) four hundred frequencies /variables = rank admit. information rank admit N legitimate four hundred 400 lacking 0 0 Frequency desk rank Frequency p.c valid % Cumulative p.c legitimate 1 sixty one 15.three 15.3 15.three 2 151 37.eight 37.8 fifty three.0 three 121 30.3 30.three eighty three.three 4 67 sixteen.eight 16.8 one hundred.0 total 400 one hundred.0 a hundred.0 admit Frequency percent legitimate % Cumulative % legitimate 0 273 68.three 68.3 68.three 1 127 31.eight 31.eight a hundred.0 complete 400 a hundred.0 100.0 crosstabs /tables = admit by way of rank. Case Processing abstract situations valid missing complete N % N percent N percent admit * rank 400 a hundred.0% 0 .0% four hundred 100.0% admit * rank Crosstabulation count number rank total 1 2 3 4 admit 0 28 97 ninety three 55 273 1 33 fifty four 28 12 127 total 61 151 121 sixty seven 400 analysis strategies you may trust

    below is a listing of some analysis strategies you can also have encountered. probably the most strategies listed are somewhat cost-effective while others have either fallen out of favor or have boundaries.

  • Probit regression, the focal point of this web page.
  • Logistic regression. A logit mannequin will produce outcomes an identical probit regression. The option of probit versus logit relies upon generally on individual preferences.
  • OLS regression.  When used with a binary response variable, this model is referred to as a linear chance model and may be used as a method to explain conditional probabilities. youngsters, the blunders (i.e., residuals) from the linear probability mannequin violate the homoskedasticity and normality of error assumptions of OLS regression, leading to invalid regular error and hypothesis assessments. For a extra thorough discussion of these and different complications with the linear probability mannequin, see lengthy (1997, p. 38-40).
  • Two-group discriminant feature evaluation. A multivariate method for dichotomous influence variables.
  • Hotelling's T2.  The 0/1 outcome is became into the grouping variable, and the previous predictors are grew to become into influence variables. this could produce an normal verify of magnitude however will now not give particular person coefficients for each and every variable, and it's unclear the extent to which each "predictor" is adjusted for the have an effect on of the different "predictors."
  • Probit regression

    under we use the plum command with the subcommand /hyperlink=probit to run a probit regression mannequin. After the command identify (plum), the result variable (admit) is adopted with by rank which suggests that rank is a specific predictor, followed by with gre gpa, indicating that the predictors gre and gpa should still be handled as continuous.

    plum admit with the aid of rank WITH gre gpa /hyperlink=probit /print= parameter abstract.

    The output from the plum command is broken into a couple of sections, each and every of which is discussed beneath

    Case Processing abstract N Marginal percent admit 0 273 sixty eight.three% 1 127 31.eight% rank 1 sixty one 15.three% 2 151 37.eight% three 121 30.3% 4 sixty seven 16.8% valid four hundred 100.0% lacking 0 total four hundred
  • The plum output is labeled as an ordinal regression, however, we can verify below (see the notice in the next set of tables) that the probit link characteristic turned into used. note that a model with a binary result may also be view as a unique case of an ordinal mannequin, the place there are most effective two classes.
  • The table above comprises frequencies for the two categorical variables admit (the effect) and rank (one of the predictors).
  • we can see that every one four hundred observations were used. Fewer observations would were used if any of our variables had missing values.
  • model becoming suggestions mannequin -2 Log likelihood Chi-rectangular df Sig. Intercept best 493.620 ultimate 452.057 forty one.563 5 .000 link feature: Probit. Pseudo R-square Cox and Snell .099 Nagelkerke .138 McFadden .083 link function: Probit.
  • The table labeled mannequin fitting advice contains two rows, one for the model we requested (labeled ultimate) and one for a so known as null mannequin (Intercept most effective). The -2 log likelihoods may also be used to compare model healthy of the two models. The remaining -2 log chance for our mannequin is 452.057. The intercept-most effective mannequin has a -2 log chance of 493.620.
  • The chi-square look at various statistic of 41.563 is the difference between the two -2 log likelihoods, this test statistic, with 5 levels of freedom and an associated p-cost of less than 0.0004 tells us that the present model matches improved than a mannequin with simply an intercept.
  • Pseudo-R-squared values are yet another technique to assess mannequin fit. Three diverse pseudo-R-squared are given within the output, however  various measures of pseudo-R-squareds exist. all of them try to provide tips similar to that supplied via R-squared in OLS regression; youngsters, none of them can also be interpreted precisely as R-squared in OLS regression is interpreted. For a dialogue of a variety of pseudo-R-squareds see lengthy and Freese (2006) or our FAQ page What are pseudo R-squareds?
  • Parameter Estimates Estimate Std. Error Wald df Sig. 95% self belief Interval decrease certain upper bound Threshold [admit = 0] 3.323 .663 25.090 1 .000 2.023 four.623 location gre .001 .001 4.478 1 .034 .000 .003 gpa .478 .197 5.869 1 .015 .091 .864 [rank=1] .936 .245 14.560 1 .000 .455 1.417 [rank=2] .520 .211 6.091 1 .014 .107 .934 [rank=3] .124 .224 .305 1 .581 -.315 .563 [rank=4] 0a . . 0 . . . link feature: Probit. a. This parameter is determined to zero since it is redundant.
  • within the desk labeled Parameter Estimates, we see the coefficients, their average errors, Wald check statistic with associated df and p-values, and the 95% self belief interval of the coefficients. The variables gre, gpa, and the phrases for rank=1 and rank=2 are statistically huge. The probit regression coefficients give the change within the z-ranking (also referred to as the probit index) for a one unit exchange in the predictor.
  • For a one unit enhance in gre, the z-score raises by using 0.001.
  • For every one unit raise in gpa, the z-ranking increases via 0.478.
  • The phrases for rank have a slightly diverse interpretation. for example, having attended an undergraduate institution with a rank of 1, versus an establishment with a rank of four (the reference community), increases the z-ranking by means of 0.936.
  • We may additionally also are looking to test the average impact of rank, we can do that the usage of the verify subcommand. The examine subcommand is followed via the name of the variable we need to test (i.e., rank), and then one value for every stage of that variable (including the left out class). the first line of the check subcommand rank 1 0 0 0 suggests that we need to look at various that the coefficient for rank=1 is 0. To perform a distinctive diploma of freedom look at various, we include distinct lines within the test subcommand, all but the ultimate line is separated by means of a semicolon. The 2nd and third rows point out that we are looking to test that the coefficients for rank=2 and rank=three are equal to 0. word that there isn't any need to encompass a row for the fourth category of rank.

    plum admit through rank with gre gpa /hyperlink=probit /print= parameter abstract /verify rank 1 0 0 0; rank 0 1 0 0; rank 0 0 1 0.

    because the models are the same, many of the output produced via the above plum command is an identical as earlier than. The best change is the extra output produced through the verify subcommand, handiest this portion of the output is shown beneath.

    custom speculation exams 1 distinction Coefficients C1 C2 C3 Threshold [admit = 0] 0 0 0 area gre 0 0 0 gpa 0 0 0 [rank=1] 1 0 0 [rank=2] 0 1 0 [rank=3] 0 0 1 [rank=4] 0 0 0 distinction consequences Contrasts Estimate Std. Error examine cost Wald df Sig. 95% self assurance Interval reduce sure upper certain C1 .936 .245 0 14.560 1 .000 .455 1.417 C2 .520 .211 0 6.091 1 .014 .107 .934 C3 .124 .224 0 .305 1 .581 -.315 .563 hyperlink characteristic: Probit. look at various results Wald df Sig. 21.361 3 .000 link feature: Probit.
  • the primary table above, labeled distinction Coefficients, suggests the hypotheses we're checking out.
  • The 2d desk offers the distinction consequences, as a result of each row in the test subcommand exams that a coefficient in the model is the same as 0, these estimates, general error, etc. are equal to those from the table labeled Parameter Estimates basically a part of the output. The one difference during this table is that the column labeled test which explicitly gives the null hypothesis, in our case, that each of the phrases is the same as 0. (word that other null hypotheses will also be special.)
  • The ultimate table produced by using the check subcommand, labeled test results, offers the multiple degree of freedom look at various we are interested in, the Wald look at various statistic of 21.361, with three degrees of freedom, and an linked p-value of below  0.001, tells us that the usual effect of rank is statistically massive.
  • The table labeled Parameter Estimates offers speculation assessments for variations between each level of rank and the reference category. we are able to use the verify subcommand to examine for differences between the other levels of rank. as an instance, we may wish to look at various for a change in coefficients for rank=2 and rank=3. in the syntax below we've added a second look at various subcommand. This time, the values given are 0 1 -1 0 this indicates that we are looking to calculate the change between the coefficients for rank=2 and rank=3 (i.e., rank=2 - rank=3).

    plum admit by means of rank with gre gpa /link=probit /print= parameter abstract /check rank 1 0 0 0; rank 0 1 0 0; rank 0 0 1 0 /check rank 0 1 -1 0.

    once again the output from the model, as smartly as the output linked to the first check subcommand are just like these shown above, so that they are neglected.

    custom speculation checks 2 distinction Coefficients C1 Threshold [admit = 0] 0 location gre 0 gpa 0 [rank=1] 0 [rank=2] 1 [rank=3] -1 [rank=4] 0 distinction effects Contrasts Estimate Std. Error verify cost Wald df Sig. 95% confidence Interval lower sure higher certain C1 .397 .168 0 5.573 1 .018 .067 .726 hyperlink feature: Probit.

    within the table labeled distinction results we see the difference in the coefficients (i.e., 0.397). The Wald verify statistic of 5.573, with one diploma of freedom, and associated p-price of less than 0.02, indicates that the change between the coefficients for rank=2 and rank=three is statistically gigantic. as a result of only 1 estimate turned into distinct in the examine subcommand, the distinct diploma of freedom test (i.e. the test effects desk) isn't printed.

    things to agree with
  • Empty cells or small cells:  you'll want to verify for empty or small cells by using doing a crosstab between specific predictors and the effect variable.  If a telephone has only a few cases (a small mobilephone), the model may also become unstable or it may no longer run in any respect.
  • Separation or quasi-separation (often known as ideal prediction), a situation in which the outcomes doesn't fluctuate at some stages of the impartial variables. See our web page FAQ: what is comprehensive or quasi-comprehensive separation in logistic/probit regression and the way can we cope with them? for tips on models with perfect prediction.
  • sample size:  each logit and probit models require more circumstances than OLS regression as a result of they use optimum chance estimation techniques.  it is additionally critical to bear in mind that when the outcome is rare, even though the ordinary dataset is enormous, it can be elaborate to estimate a logit model.
  • Pseudo-R-squared:  various measures of pseudo-R-squared exist. all of them try and give tips comparable to that provided by R-squared in OLS regression; despite the fact, none of them may also be interpreted precisely as R-squared in OLS regression is interpreted. For a dialogue of a variety of pseudo-R-squareds see lengthy and Freese (2006) or our FAQ web page What are pseudo R-squareds?
  • Diagnostics:  The diagnostics for logistic regression are distinctive from those for OLS regression. For a dialogue of model diagnostics for logistic regression, see Hosmer and Lemeshow (2000, Chapter 5). note that diagnostics completed for logistic regression are comparable to those finished for probit regression.
  • See additionally References
  • Hosmer, D. & Lemeshow, S. (2000). utilized Logistic Regression (2nd version). long island: John Wiley & Sons, Inc.
  • long, J. Scott (1997). Regression models for express and restricted dependent Variables. Thousand very well, CA: Sage Publications.
  • The content of this web web site should now not be construed as an endorsement of any particular net web page, booklet, or utility product by means of the tuition of California.


    Exampaper OCT 2011

    This examination question paper is still the property of the university of South Africa and may now not be removed from the examination venue. This examination paper consists of seventeen (17) pages. vital instructions: 1. 2. 3. four. 5. 6. 7. This paper carries five (5) questions. All questions have to be answered in the template supplied. Please comprehensive the cowl web page of the reply ebook in full. assess that you've the correct number of pages. The questions in this evaluation need to be answered in the templates supplied. effort the questions where you think assured that you should get the most marks for first. The mark allocation is an indication of the period of time that may still be allocated to each query. answer the entire questions even though you don’t rather conclude every one. bear in mind no longer to leave out a whole query/s. Please be neat, as illegible handwriting can not be marked. you are reminded that solutions may additionally no longer be written in pencil. You need to write with blue or black pen. In practice it's unacceptable for Bookkeepers to make use of correct fluid (Tipp-ex). Marks will be deducted for using correction fluid. query 1 2 three four 5 subject 10 actual OR FALSE STATEMENTS VAT CALCULATIONS AND RETURNS 10 assorted alternative QUESTIONS PAYROLL CALCULATIONS PAYROLL money owed total MARKS 10 50 20 50 20 a hundred and fifty 8. query 1 10 authentic OR FALSE STATEMENTS (10 MARKS) point out even if here statements are genuine or False (1 mark each and every). evidently write ‘genuine’ or ‘FALSE’ in the reply column. There is no negative marking so don't depart any questions unanswered. remark 1. 2. 3. four. 5. 6. 7. eight. 9. belongings are supplies managed via the enterprise from which future economic benefits are more likely to circulation to the business. The abilities construction levy is a VAT deductable price. An worker contributes 1 % of his or her net wages to the Unemployment insurance Fund. cellphone allowances are included in gross remuneration at one hundred %. certain foodstuffs, petrol, diesel and exports are exempt elements. Donations made through a company are VAT deductible. When a female is because of have a child she may additionally declare 17 weeks of dedicated UIF maternity merits. workers working between 6:00 at evening and 6:00 within the morning must receive additional pay or be able to work fewer hours for a similar amount of cash. The credit score to the EMP201 manage account for UIF will be the employee contribution most effective. answer 10. If an organisation submits their EMP201 via E-submitting then the business want simplest submit the return on the 14th day of the month following the month of the return. [10] question 2 VAT CALCULATIONS AND THE VAT RETURN (50 MARKS) E. Estridge is the owner of Zacharia CC buying and selling as Pete's Pottery. The company is a registered VAT seller and debts for VAT on the bill groundwork. You have been supplied with right here information for the September/October 2011 VAT length. enterprise guidance:        address: 78 Dalesley Park, Abigail Rd , Imhoff, 7945 VAT quantity: 4852986349 VAT area code: 41 Tax duration code: 10/eleven SARS workplace mobilephone number: - 021 788 4587 enterprise cell number: - 021 783 7451 enterprise fax quantity: - 021 783 7452 E. Estridge completes and will pay all her VAT returns on the penultimate day for rendering returns/funds. She pays the return by cheque. deliver advice. All quantities include VAT unless VAT is not relevant. Transactions with non-companies are indicated with an asterisk (*). Cheques issued: Cheque Particulars no. 653 properly equipment (Pty) Ltd 654 655 656 657 658 659 660 661 662 663 664 665 666 Zancor Catering Electro-com Renshaw existence Ester Municipality Zebra tremendous Stationers SARS Buzuki Audio World Paper Loot speed features Couriers SARS Ritas Restoration Allocation buying and selling inventory entertainment expenditure electrical energy lifestyles insurance Property assessment rates facilities/capabilities Petrol Stationery (for business use) EMP201 price start motor cycle personnel tv Printer consumables charge to creditor VAT 201 charge Repairs and protection* quantity R 38 475.00 1 620.00 2 835.00 12 one hundred fifty.00 2 160.00 930.15 121.50 1 620.00 10 125.00 93 a hundred and fifty.00 1 687.50 1 917.00 7 560.00 20 250.00 3 375.00 Debits on the bank observation:     Debit order for monthly web carrier issuer, R 60 750.00. bank costs, R a hundred 575.00. government levies, R 29 seven-hundred.00. stop order for personnel room DSTV, R 34 560.00. credit on the financial institution remark:  Receipt no’s 3677 to 4331 for money revenue, R 1 282 500.00.  received R2 000.00 from A. Smith in part payment of his account. Receipts now not covered above:  On 24 October 2011, an amount of R 742 500.00, changed into transferred from Zambia for items, which were sold there.  On 23 October 2011 an long-established money bill turned into issued for delivery profits, R sixteen 875.00.  On 25 October 2011 an equipment became sold for money, R 76 545.00.  On 28 October 2011 the company obtained an coverage payout on stock damaged in transport, R 105 030.00 other transactions  credit Losses, R 349 515.00.  credit notes issued (on ordinary-rated items up to now bought), R 359.15.  On 31 October 2011 E. Estridge handed a journal entry for activity on the late account of a debtor, F.Mduma for R 33 750.00.  On 26 October 2011 P.O'connell purchased goods from Zacharia CC for R 270 000.00 on credit. The volume changed into nevertheless surprising on 25 November 2011. Required: (a) (b) Calculate the full input and output VAT which E. Estridge can claim or account for on the above outlined transactions for September/October 2011. finished the VAT return for remittance offered in your answer ebook. [50] Calculation of input tax: VAT enter total input VAT – The sum of all the VAT input quantities. Calculation of output tax: VAT output total output VAT – The sum of all the VAT output quantities. query 3 10 varied option QUESTIONS (20 MARKS) select probably the most acceptable option from the alternate options offered in each and every of the following questions. Write down only the LETTER of your choice next to the corresponding quantity in the answer book. distinctive option query 1. The component of the simple circumstances of Employment Act that regulates working hours doesn't observe to all the following apart from … people in senior management worker's that don't pay salary tax employees who work less than 24 hours in a month revenue personnel who commute and regulate their own working hours answer A B C D 2. At current it's illegal for any enterprise in South Africa to require or allow an worker to work for greater than.. forty five hours average time in a given week 8 hours in any given day if the employee works for 5 or fewer days in every week 9 hours in any given day if the worker works for greater than 5 days in every week 15 hours overtime in a given week A B C D three. in terms of meal intervals, an employee must be remunerated for all of the following except … for a meal interval the place the worker is required to work for a meal interval the place the worker is required to be accessible for work for a meal interval where the worker is required to stay on the premises for any element of a meal interval that is in extra of 75 minutes A B C D four. A B C D the entire following are real with respect to go away except … An employee need to be granted 21 consecutive days of annual go away on full pay in appreciate of each depart cycle The 'in poor health go away cycle' often refers to a period of 36 months employment with the same corporation An worker is entitled to 4 consecutive months maternity depart An employee that has been employed with the identical employer for a length of longer that 2 months is entitled to 3 days household responsibility depart for the birth of his child 5. Remuneration for the purposes of the fundamental circumstances of Employment Act include none of right here apart from … Reimbursable enterprise kilometres travelled enterprise payments for UIF relocation allowances housing allowance The primary situations of Employment Act requires the entire following minimum counsel about our personnel to be stored by means of employers except … A B C D 6. A B C D a employees identify and job a people name of subsequent of relations the period of time worked the remuneration paid 7. all of the following counsel are non-statutory advice necessities except … employee's contract employee's CV employee's earnings tax number employee's marital reputation A B C D eight. A B C D The three documents required for VAT registration are … VAT101, VAT127, VAT130 VAT201, VAT402, VAT130 VAT117, VAT402, VAT130 VAT101, VAT117, VAT130 9. A B C D The advantage building Act ninety seven of 1998 came into effect as a way to … enhance the advantage of the South African workforce increase the degrees of investment in schooling reduce the unemployment rate all of the above 10. A B C D The UI fund offers all the following advantages except … unemployment advantages affliction merits merits for state pensioners maternity advantages [20] SUMMATIVE assessment – UNISA 2nd SEMESTER 2011 JUNIOR BOOKKEEPER: PAYROLL AND monthly SARS RETURNS question 4 (50 MARKS) PAYROLL CALCULATIONS You were offered with right here advice for brand spanking new Yorker Ltd for the month of October 2011. 1. the following tax sliding scale for people applied for the 12 months ended 28 February 2012: TAXABLE profits R R 0 — one hundred fifty a hundred and fifty — 235 000 001 235 — 325 000 001 325 — 455 000 001 455 — 580 000 001 580 and over 001 charges OF TAX R 27 000 forty eight 250 75 250 a hundred and twenty 750 168 250 + + + + + 18% of each R1 of the quantity 25% over of the quantity 30% over of the quantity 35% over of the amount 38% over of the quantity 40% over R 150 000 235 000 325 000 455 000 580 000 2. both O.Olive and J.Smith acquire a 13th cheque. The enterprise ‘smoothes’ the tax on the bonus right through the yr. 3. F.Von Bargen has the whole use of a company motor vehicle. This vehicle (a delivery vehicle) had can charge the enterprise R 175 000.00 (including VAT) when it became bought and allocated to him ultimate 12 months. based on the relevant tax legislations constructive from 1 March 2011, the cost of the monthly taxable fringe improvement of use of a company car is three.50% of the charge of the car (together with VAT) i.e. R 6125.00. F.Von Bargen’s company have to encompass 80 % of the month-to-month taxable fringe improvement i.e. R three 837.72 in his remuneration for the functions of selecting PAYE payable. four. permanent personnel make a contribution 6.50% of their fundamental income to the vegetables Pension Fund (13 th cheques are not pensionable). New Yorker Ltd contributes to the pension fund on a rand-for-rand basis. 5. L.Spheris and F.Apleni are half time personnel that worked for 35 and 22 hours for the month respectively. 6. personnel are not entitled to housing, mobile phone or desktop allowances. 7. There aren't any impartial contractors or personnel on registered learnerships. 8. simplest eighty% of the travel allowance is taxable 9. assume a highest UIF funding revenue of R 12 478.00. 10. All permanent employees belong to the Medak scientific aid, a registered scientific scheme and have all offered sufficient proof of membership to their business enterprise. The enterprise will pay the entire contribution to the clinical support. right here tax concessions practice to scientific fund contributions:  R 720.00 for the only member.  R 720.00 for the primary additional dependant.  An additional R 440.00 is allowed as a deduction per additional dependant apart from the primary dependant. All contributions paid for an employee aged 65 or older are allowed as a deduction. SUMMATIVE evaluation – UNISA 2nd SEMESTER 2011 JUNIOR BOOKKEEPER: PAYROLL AND monthly SARS RETURNS eleven. The personnel participants have the following number of dependants and medical support contributions: worker R.Tshabalala O.Olive J.Smith F.Von Bargen No. of dependan ts 0 2 3 four complete monthly contribution paid to scientific assist R 955.50 R 2 252.10 R 2 456.88 R 2 627.forty six R.Brennan 0 R 941.76 12. The rebates allowed in the South African tax gadget are as follows for the 2011/2012 tax 12 months:  basic rebate (64 years and more youthful) R 10 755.00  Secondary rebate (sixty five – seventy four years) R 6 012.00  Tertiary rebate (75 years and older) R 2 000.00 Required: (a) (b) complete the table supplied to your reply book. Make use of the sliding scale schedule. complete the abbreviated EMP201 in your answer publication. [50] note: reveal your entire calculations SUMMATIVE evaluation – UNISA 2nd SEMESTER 2011 JUNIOR BOOKKEEPER: PAYROLL AND month-to-month SARS RETURNS (a) October 2011 Ag e income R6 825.00 R 37 535.00 R 20 474.00 R 23 886.00 R7 848.00 R7 848.00 R8 531.00 shuttle allowance R 1 365.00 R 0.00 R 3 071.00 R 0.00 R 1 177.00 n/a n/a Commiss ion R 853.00 R5 067.00 R 0.00 R 0.00 R 667.00 n/a n/a Taxable portion of travel allowance Taxable cost of business automobile R 0.00 Taxable component of clinical help Pension fund deduction PAYE/SDL remuneration UIF remuneratio n PAYE deduction R.Tshaba seventy six lala O.Olive J.Smith F.Von Bargen R.Brenna n L.Spheris F.Apleni 32 44 68 fifty one 23 18 R 0.00 R 0.00 R 0.00 R 0.00 R 0.00 n/a n/a n/a n/a n/a n/a n/a n/a Junior Bookkeeper: Payroll and monthly SARS Returns – Summative evaluation – UNISA 2nd Semester 2010 web page 4 of 16 -5(b) name of business: Return for: Submissio n date: charge particulars PAYE R amounts payable four manage complete (four + 5 + 6 = 7) c 5 R 7 R SDL c 6 c R SCPR01L can also/ JUNE 2011 EMP 201 "Extract" For illustrative applications most effective UIF c Calculations: ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... SUMMATIVE assessment – UNISA 2nd SEMESTER 2011 JUNIOR BOOKKEEPER: PAYROLL AND monthly SARS RETURNS ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... question 5 PAYROLL accounts (20 MARKS) The advice what follows relates to the salaried personnel of Afrika Ltd for the month of October 2011. assume a highest UIF funding salary of R 12 478.00. Ignore voluntary deductions. anticipate that all payroll collectors have been paid in full by EFT on 31 October 2011. Salaries register of Afrika Ltd - October 2011 SR 10 Employers contributions net earnings scientific assist 7 1 146.ninety nine 607.83 39 2 911.74 358.31 24 3 705.80 337.36 24 three 088.20 479.seventy four 10 1 217.64 386.80 7 508.78 8 271.75 121 950.57 12 070.37 Pension fund 661.seventy three UIF SDL Deductions employee no. & name Gross revenue UIF (264) R.Maputo (265) O.Ogilvy (266) R.Richards (267) k.Artz (268) R.Bouman (269) L.Mocke (270) T.Veldhuis PAYE complete/ Pensio balanc n fund e 9 2 124.7 1 531.sixty six 926.00 661.73 318.17 eight 60 124.7 661.00 8 17 3 21 538.23 639.sixty eight 302.sixty nine 124.78 132.12 3639.sixty eight 124.seventy eight 556.27 33 1 9 124.7 7 037.61 485.00 985.25 147.64 8 34 2 10 124.7 7 821.33 742.00 316.15 262.26 eight 11 113.6 669.00 5 407.52 1 761.03 282.20 2 638.22 2 757.25 1985.25 124.seventy eight 307.06 2316.15 124.seventy eight 340.seventy four 761.03 113.sixty five 106.04 10 101.four 2 536.seventy five 147.00 7 11 n/a 2 757.25 029.00 171 714.2 659.00 4 9 363.84 one hundred and one.47 one zero one.forty seven n/a one hundred ten.29 39 9 forty nine 630.35 363.84 708.42 1 714.24 653.ninety nine Required: Junior Bookkeeper: Payroll and monthly SARS Returns – Summative assessment – UNISA 2nd Semester 2010 - of 16 page - 6 SUMMATIVE evaluation – UNISA 2nd SEMESTER 2011 JUNIOR BOOKKEEPER: PAYROLL AND monthly SARS RETURNS pen, publish to and stability the E 201 manage (B7) within the ordinary ledger of Afrika Ltd : [20] general ledger of Junior Bookkeeper: Payroll and month-to-month SARS Returns – Summative evaluation – UNISA 2nd Semester 2010 - of sixteen web page - 7 SUMMATIVE evaluation – UNISA 2nd SEMESTER 2011 JUNIOR BOOKKEEPER: PAYROLL AND month-to-month SARS RETURNS Calculations: ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ............................................................................................................................................................................ ...... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... Junior Bookkeeper: Payroll and monthly SARS Returns – Summative evaluation – UNISA 2nd Semester 2010 - of sixteen web page - 8 SUMMATIVE assessment – UNISA 2nd SEMESTER 2011 JUNIOR BOOKKEEPER: PAYROLL AND month-to-month SARS RETURNS ............................................................................................................................................................................ ... ............................................................................................................................................................................ ... query 1: 10 MARKS query 2: 50 MARKS question three: 20 MARKS question 4: 50 MARKS question 5: 20 MARKS total : one hundred fifty MARKS Junior Bookkeeper: Payroll and monthly SARS Returns – Summative evaluation – UNISA 2nd Semester 2010 - of 16 page - 9


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