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Test Name : Oracle Accounting Hub Cloud Service 2018 Implementation Essentials
Vendor Name : Oracle
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Exam Title : Oracle Accounting Hub Cloud Service 2018 Implementation Essentials
Exam ID : 1Z0-1019
Exam Duration : 105 minutes
Questions in test : 60
Passing Score : 64%
Format : Multiple Choice Questions
Exam Center : Pearson VUE
Real Questions : Oracle Accounting Hub Cloud 2018 Certified Implementation Specialist (OCS)
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Transaction type life cycle - Analyze and identify transaction type life cycle: transaction type life cycle from the beginning to closing. For example, booking reservation, confirm, pay, cancel. Modeling considerations - Analyze other modeling considerations: transaction type, consolidated vs separated subledger view, pass through, rule-based, primary ledger only, primary plus secondary ledger, legal entities Register Source System and PaaS (ICS) integration - Register Source System and PaaS (ICS) integration (to include "Transaction Information" and "Line Information" worksheets, and Transaction Objects) Transaction data and automation with web service - Upload transaction data, correct when there are errors, and perform automation with web service Adjust Journal Entries - Adjust journal entries: override, create manual adjustment entries Accounting, reporting, audit, and reconciliation requirements - Analyze accounting, reporting, audit, and reconciliation requirements for accounting transactions: journal entries, accounts
- Analyze accounting, reporting, audit, and reconciliation requirements for reporting transactions: journal and line descriptions, reporting attributes
- Analyze accounting, reporting, audit, and reconciliation requirements for reconciliation transactions: reconciliation reference attribute Accounting rule configuration - Analyze the accounting rule configuration: Journal entries, accounting method, chart of accounts, accounts derivation, intercompany Update Registered Source System - Update Registered Source System: accounting attribute assignments, sources, event types Reporting and Audit - Perform Reporting and Audits: create custom OTBI report, create custom BIP report, security, period close readiness and closing exception reporting and dashboard; FRC (GL's reporting center: Studio and Smart View); and security (Security includes GL and Accounting Hub securities) Diagnostics - Perform diagnostics (Subledger Accounting Diagnostic - diagnose how subledger sources and attributes are used in the create accounting process)



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If there’s one element that loads of small microcontrollers hate (and that includes the AVR-primarily based Arduini), it’s floating-point numbers. And if there’s another issue they hate it’s division. for instance, dividing 72.3 by means of 12.9 on an Arduino UNO takes round 32 microseconds and 500 bytes, while dividing 72 by 13 takes 14 microseconds and 86 bytes. Multiplying seventy two with the aid of 12 takes slightly beneath 2.2 microseconds. So roughly speakme, dividing floats is twice as slow as dividing (sixteen-bit) integers, and dividing at all is five to seven instances slower than multiplying.

There’s lots of the time that you just don’t care about velocity. as an instance, in case you’re doing a calculation that most effective runs infrequently, it doesn’t matter in case you’re the usage of floats or sluggish division routines. but if you ever end up in a decent loop that’s the use of floating-point math and/or doing division, and you should get somewhat more speed, I’ve got some information for you.

Some of those information (in selected the integer division tricks at the end) are arcane wizardry — best to be used when the situation in reality requires it. but when you’re doing the identical calculations time and again, that you may commonly profit plenty simply by way of giving the microcontroller numbers within the structure it natively is aware. Have a bit sympathy for the terrible little silicon beasties trapped inside!

glide Avoidance

that you can commonly fully avoid floating aspect numbers just through considering a little bit. effortlessly seeing a number with a decimal place is not any motive to tug out the floats. for instance, if you have a volume, just like the temperature on an LM75-style temperature sensor, that’s correct all the way down to 1/8 of a level, you fully positively don’t need floating aspect numbers. How can you deal with 21.125 degrees without floats? the exact same approach the LM75 does — it pre-multiplies every thing by 8.

heading off floating aspect numbers is regularly as handy as figuring out what the herbal units of the issue are. Is that $12.34 or is it 1,234 cents? i will best reduce with a hand saw accurately to about a millimeter. If I measure board lengths in millimeters, I’ll never want a decimal factor. sure, the numbers get bigger, which capacity that you simply could need to make use of the next-greatest integer storage size, however microcontrollers don’t mind that very nearly as an awful lot as floating point math.

So in case you’re taking a studying from a 10-bit ADC, and it’s latitude is 3.3V, the closing thing you are looking to do is convert that at once to volts after measurement (notwithstanding it may make your code examine nicer.) hold the ADC price round — it’s a nice 10-bit integer — and most effective convert out to specific volts when you absolutely should, after all the other math has been performed. And if you do, be aware that the correct devices to your problem are in terms of the minimum-feasible measured difference, which is 3.three V / 1023 = 3.23 millivolts. if you happen to need to convert to physical gadgets for the end-user, you’ll multiply by means of this cost.

are attempting redefining units of size any time you’re running into fractions or floating aspect numbers. You’ll be shocked how commonly you didn’t want them within the first place. There’s one warning here notwithstanding: be cautious when converting returned to the favored units. A square centimeter isn’t 10 rectangular millimeters, it’s 100. each “millimeter” within the conversion brings an influence of ten with it. And ten millimeters divided via one millimeter is simply ten, and not using a contraptions left, and no want for conversion.

mounted-factor Math

FixedPointNumbers

while with no trouble getting away from the slower float class will also be enough of a speedup for some situations, there’s an extra layer of optimization accessible, when you want it. a typical alternative, as an example, on small microcontrollers is to effortlessly multiply all values by way of 256. this is referred to as “eight:8 mounted element” notation, because you’re the usage of eight bits for the fractional part, and eight for the integer part.

It turns out that if you be aware of the number of bits within the fractional half, that you can optimize multiplications and divisions to run even sooner than a sixteen-bit multiplication or division. here’s a nice writeup of fastened-factor math alternate options for the Arduino, as an instance. that you can examine up on a bunch of those optimizations in this Atmel software observe. And be aware that the algorithms aren’t selected to any platform.

So for those who don’t need arbitrary fractions, fastened-factor libraries can also be a real speedup over the usage of floating-point routines.

Floating factor Numbers Are lousy Anyway

There’s a fairly standard issue with floating factor numbers: you need to be very careful when trying out them for equality. That’s as a result of they’re all not represented precisely. for example, 0.1 isn’t representable exactly in floating factor, so in case you add ten of them up, it’s now not going to be exactly 1.0 both. in case you were to test sum10(0.1) == 1.0 it could turn out to be false. That’s lousy.

The other thing that’s unusual about floating aspect numbers is as a result of the way they’re developed. Floating point numbers are like scientific notation (1.23 x 103) except in binary. IEEE 754, which most compilers observe, uses 24 bits for the primary number — the variety of enormous digits — and 7 bits for the exponent. This allows you to symbolize both in reality tremendous and really small numbers. however because there is an identical variety of significant digits in all places, the gaps between representable numbers is terribly small for small numbers and doubles with each vigor of two within the exponent.

as an instance, 123456789.0 + 1.0 == 123456789.0 can be authentic because the spacing between the 24 bits of large digits is better than 1. 123456789 + 1 == 123456789 could be false if you use integers, since the spacing between integers is always one precisely.

briefly, integers are exact, and they maintain the equal distance between representable values throughout their latitude. I find this frequently corresponds very properly to the sort of inputs and outputs i take advantage of with microcontroller projects. Floating factor numbers, in spite of this, have a an awful lot wider latitude, however do so at the fee of turning out to be distances between viable values. And when floats can’t symbolize numbers precisely, comparisons don't seem to be going to work precisely.

Joyless Division

many of the lessen-conclusion microcontrollers (once more, together with the AVRs used in the Arduino) don’t have hardware division in-built. This potential that they should be the use of some variety of iterative algorithm to locate the reply. This takes ages. there's a good shortcut, at the least for integers, that make sure to know about. In binary, dividing by n powers of two is an identical as losing the least-big n bits off the quantity. x >> 2 bit-shifts x over via two places, with ease dividing via four. It’s just like dividing by way of ten in base-10. Drop the final digit.

This class of bit-shift division (and its dual, multiplication) is optimized in for you with the aid of AVR-GCC, so that you don’t even have to feel about it. nevertheless it ends up with the atypical situation that x/four runs extremely rapidly, whereas x/5 can take slightly longer. if you can pick your divisors, you should definitely all the time select them to be powers of two. if you were going to usual ten values together, your algorithm will run a great deal faster if you most effective use eight observations, and might not be all that a lot worse in case you use sixteen.

Integer Division hints Image: minifigure.orgphoto: minifigure.org

Which brings us at last to the promised darkish wizardry — two nasty tricks so you might use if you need to divide integers in a hurry. The essential caveat is that these concepts only work when the divisor is consistent. You need to understand what it is ahead of time to use this. however if you deserve to divide with the aid of thirteen time and again once more, here’s two how you can get you there as without delay as feasible.

Multiply then Divide

The fundamental idea right here is to pre-compute your division as a multiplication and a little-shift. in the same approach that you just could divide through two through first multiplying with the aid of five and then losing the least-massive digit (effectively dividing by way of ten), that you may pre-compute the binary multiplier and variety of bits to drop. The thought became laid out in this submit through Douglas Jones, and then coded up here.

In particular, to divide any eight-bit quantity with the aid of thirteen, you first multiply the quantity via 158 (leading to a sixteen-bit integer) after which drop the least-massive eleven bits. Yeah, right? it works as a result of 158/211 ~= 1/13. It’s simply math and it’s going to run about as speedy as feasible, however it’s not entirely readable.

look up desk

if you in fact, fully should divide through thirteen in a hurry, there’s a method that’s faster than the above trick: pre-computing the entire possible values formerly and storing them in an array. for instance, with 8-bit numbers, create a table of all possible 256 values divided by way of thirteen. We’ll call it thirteen_table[]. Then for those who need to know what 123 / 13 is tremendous immediately, just search for thirteen_table[123] and also you’re completed. It’s dishonest, and it uses 256 bytes of memory, but if this trick gets you out of a sticky condition, it’s worth knowing.

premature Optimization

There’s a maxim of Donald Knuth’s, “premature optimization is the basis of all evil“. The fundamental argument is that if you all the time write your division routines aiming for maximum pace, you may emerge as with an unreadable mess of code for terribly little benefit. possibly the appropriate vicinity to spend your optimization time changed into making sure that two different subsystems might run concurrently, or some thing else.

So take all the above with a grain of salt. thinking your contraptions through, and determining if it’s feasible to re-solid them into integers, is likely all the time a good suggestion in case you don’t have hardware floating-factor math. Swapping up arbitrary divisors for an influence of two is also fairly plenty always a win, if you’re already the usage of integers.

no matter if you are looking to take the subsequent step to a set-point library, or are capable of use the dirty hints for fast division with the aid of constants, depends upon your situation and your needs. If it over-complicates your code and the CPU has a lot of free time, or if it’s in a non-time-critical part, don’t hassle.

What other dirty math hints may still they know about? What’s your favorite or most-hated? Integer vs. floating point? tell us within the comments!


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