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Exam Number : 98-382
Vendor Name : Microsoft
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Format | 98-382 Course Contents | 98-382 Course Outline | 98-382 test
Syllabus | 98-382 test
Complete or debug code that uses assignment and arithmetic operators
Assignment; increment; decrement; addition; subtraction; division; multiplication; modulus; compound assignment operators
Comments; indentations; naming conventions; noscript; constants; reserved keywords; debugger keyword; setting breakpoints; console.log
Evaluate the use of inline and external scripts
When to use, how to use, and what happens when both are used
Implement exception handling
try; catch; finally
Complete and debug code that interacts with the Browser Object Model (BOM)
Manage state; display dialogs; determine screen size
Program with Variables, Data Types, and Functions (25-30%)
Declare and use variables of primitive data types
Number; Boolean; String; Null; Undefined; typeof operator; type checking functions; use strict; converting between data types; formatting numbers; string operations; single quote vs double quote (nesting); initialization
Declare and use arrays
Single-dimensional arrays; multi-dimensional arrays; iteration; initialization; define an array; sort and search an array; use push, pop, shift, and unshift methods; use the length property; access an array element;
Complete and debug code that uses objects
Properties; methods; instantiation; Date object; retrieve date and time parts; localize date format (MM/DD vs DD/MM); add and subtract dates
Complete and debug code that uses built-in Math functions
Random; round; abs; floor; ceiling; min; max; pow; sqrt
Complete and debug a function that accepts parameters and returns a value
Reusable code; local versus global scope, redefine variables, pass parameters, value versus reference, return values
Implement and Analyze Decisions and Loops (20-25%)
Evaluate expressions that use logical and comparison operators
==; !=; <, >; <=; >=; !; &&; ||
Complete and debug decision statements
if; else if; switch; nested if
Complete and debug loops
for; while; do; break; continue
Interact with the Document Object Model (15-20%)
Identify and construct the Document Object Model (DOM) tree
window; document; body; other HTML elements
Identify and handle HTML events
onchange; onmouseover; onload; onclick; onmouseout; onkeydown
Complete and debug code that outputs to an HTML document
Complete and debug code that locates, modifies, and adds HTML elements and attributes
getElementByld; getElementsByTagName; getElementsByClassName; setAttribute; createElement
Interact with HTML Forms (5-10%)
Complete and debug code that retrieves input from forms and sets form field values
Retrieve form values; identify the DOM path; get values from different types of elements; prepopulate values; mask values
Complete and debug code that performs input validation
Case; string comparisons; Not-A-Number (NaN)
Describe the form submission process
onsubmit; post versus get; potential targets for submission
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once in a while, in the event you feed an AI content from the web, it learns herbal language. on occasion, it reads the entire contents of GitHub and learns to produce elementary snippets of code.
this is the story of what occurs when the AI does both.
Neural networks are all the rage these days. From Siri to self-riding, to protein folding and scientific diagnostics, the potent duo of machine learning and large facts is taking on. Neural nets all started out as one-trick ponies: a Markov chain generated a gobbledygook whitepaper that got authorized as a non-reviewed paper to the (“spammy and having low standards”) 2005 WMSCI convention. A Markov chain generated the Kafkaesque glory of “Garkov.” an additional turned into capable of making trippy pictures the place every little thing seemed like an eyeball or a cat.
The attain of AI nevertheless exceeds its draw close. screw ups and successes in facial attention software expose each AI’s boundaries and how even computer discovering is prone to the implicit biases of datasets and programmers. but we’ve come a protracted manner from SmarterChild. each year, they construct on what we’ve executed before.
which you can train an AI to supply prose so near natural language that humans have main issue telling who wrote it: man or laptop. remaining yr, individuals at OpenAI Labs (with which Microsoft is an unique companion) cooked up a mannequin referred to as GPT-three that may weblog, Tweet, and argue. They proficient it the usage of a part of the general Crawl dataset, which includes Wikipedia and a whole ton of books, among other subsets of prose and code. however the ordinary Crawl also indexes GitHub. When GPT-3 changed into exposed to the titanic swathes of general Crawl information, it learned to provide prose — nevertheless it also realized with the aid of osmosis to provide snippets of intelligible desktop code.
Intrigued, the OpenAI group made an extra edition of the GPT-3 model and dubbed it Codex, and expert it on a truly tremendous set of prose from the regular Crawl and laptop code from GitHub and elsewhere. (Codex has entered deepest beta, however the know-how is in reality already in use by way of GitHub, which makes use of it to vigour an clever code-suggestion device named Copilot.) OpenAI Codex is a fluent AI that can take a herbal-language on the spot as input, and generate code for the assignment it become given. It’s exceptional — on occasion.
Why Codex will also be So effective, and So flawed
The top notch energy of Codex is its fluency. it could generate code in 12 languages, and its resilient dealing with of natural language input is extremely effective. but that’s also a key weak point. Coding is tedious since it’s so aspect-delicate, however natural language is messy and context-dependent. Typos or bad logic can both ruin the feature of an entire piece of otherwise strong utility. And there’s decent code and unhealthy code and just undeniable weird code posted on the net.
Codex is prone to the same difficulty. not like a Markov chain, Codex keeps a checklist of its actions in a cache, but its scope remains constrained and shallow. It’s confined within the abstract with the aid of the inflexible syntax and common sense rules of programming. It’s additionally limited with the aid of the patterns and implicit guidelines within the precise data it makes use of. And if they don’t tell it to interpolate, it gained’t.
In other words, whereas Codex can return code that does what you need, it doesn’t comprehend why you desire that characteristic, and its reasoning received’t always be evident. It’s like writing a mathematical proof: there may be multiple paths to the same answer, a few of them meandering. every now and then Codex returns code that looks nothing like what a human programmer might do, but certain ample, it accomplishes the equal aspect. once in a while, its code has protection flaws or simply gained’t run at all. Thirty-seven p.c of the time, it works all of the time.
Codex is so robust partially as a result of the energy of its datasets. average Crawl indexes a good many issues, including WordPress, Blogspot, LiveJournal, archive.org, and a ton of .edu content. All of those are rich with metadata, semantic information, and inner suggestions for Codex to study, and they also tell us an awful lot about the means people use the languages they speak. Codex is fluent in many spoken languages, nevertheless it’s can additionally translate between spoken language and Python — each composing the code that does what you ask it to do, and explaining code to you in plain language. To coach Codex on programming code, only one of the numerous issues they did became feed it Python, plus eleven other essential programming languages.
I’ve maintained for a very long time that one of the vital main steps within the progression of AI should be to feed one researching algorithm to an additional. below the hood, AI rests on the conception so you might use math to discover patterns in statistics. It has the equal garbage-in-garbage-out barriers that all other code has. Even refined neural nets that do unsupervised getting to know can’t make a decision what constitutes successful condition except they tell them what to seek. but the tremendous energy of AI is new release. An AI will wade methodically through mammoth swathes of statistics, iterating on the identical issue until somebody tells it to stop. Neural nets ingest records like jet engines ingest geese.
There’s a true comparison here that may also be made between the present state of the art in AI analysis and the physiology of the human brain. The equipment has a coherent interior logic, and it is already developed to ascertain patterns as they relate to one one other. One hassle on AI is that suggestions may also be several contextual layers deep, and with existing suggestions, it takes a superb deal of computing power to approximate the consequences a human would get for a similar issue or instant. The mind uses Wolff’s law, which here means “neurons that fireplace together wire collectively,” to map associations. Codex makes use of an internal associative database that tags relationships within the datasets it procedures, and it has actually 175 billion rules. They each translate natural language into code. For the mind, the output vehicle is cortical neurons, and for Codex, it may be Python.
An AI That Improves Itself
We don't seem to be far from an intelligent gadget that can be trained to increase itself recursively. If they stand on the shoulders of giants, there are an excellent many giants beneath us, and they are rising speedy. Kurzweil discusses the “hockey stick” acceleration of human development, and positions AI because the herbal subsequent level of complexity in their collective ability tree. mighty AI methods have the capacity to integrate many information streams at the equal time. I call Codex the Singularity in jest, however all kidding aside, this actually does seem like a key point at which AI turns into able to enhance upon itself. What Codex can do looks for all of the world just like the first flickering of a lone neuron because it establishes its first synapses. the place and how it will articulate with other gaining knowledge of methods remains to be considered.
in the Skynet timeline, growing the entire runaway AI antagonist took supplies on the degree of what Microsoft is pouring into Codex today, and Codex every now and then struggles to print the equal line 5 times. It’s clear that we’re now not at that level of sophistication. as a substitute, Codex is not unlike the Aggregator, the grand correlation engine from the Thieves of fate. It’s most advantageous as a programming adjunct for experienced programmers, lots like the mechanical robots that support humans automate the drudgery out of repetitive tasks. To automate a technique with code, you have to constrain your work precisely sufficient to ensure that you and the IDE are on the same web page, in order that you get what you ask for. Codex can aid with a few of that drudgery, and that’s the place it shines.
it's wonderful to imagine one of these device studying to build on its own successes. And yet what it takes to make Codex go is an fully fabulous quantity of go-referenced records, with the entire infrastructure that information commands. It returns output in accordance with what you gave it to look at. while Codex can help programmers comprehensive tedious initiatives and make sure all the semicolons are protected, it continues to be beholden to logical errors. staggering, but brittle. might be it’s enhanced that they do a protracted beta for the Singularity until now.
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