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IBM C2090-558 : Informix 11.70 Fundamentals Exam

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Exam Number : C2090-558
Exam Name : Informix 11.70 Fundamentals
Vendor Name : IBM
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C2090-558 test Format | C2090-558 Course Contents | C2090-558 Course Outline | C2090-558 test Syllabus | C2090-558 test Objectives

Exam Code : C2090-558
Exam Name : Informix 11.70 Fundamentals
Number of questions: 60
Number of questions to pass: 38
Time allowed: 90 mins
Status: Live

The test contains six sections totalling approximately 60 multiple-choice questions. The percentages after each section title reflect the approximate distribution of the total question set across the sections.

Section 1 - Planning and Installation 16%
Identify data access restrictions
Describe database workloads
Describe data type concepts for database planning
Demonstrate knowledge of how to configure and install Informix
Demonstrate knowledge of embeddability considerations and the deployment utility

Section 2 - Security 3%
Demonstrate knowledge of authentication
Demonstrate knowledge of authorizations

Section 3 - DBMS Instances and Storage Objects 14%
Describe how to identify and connect to Informix servers and databases
Demonstrate knowledge of how to create and configure storage objects
Demonstrate general knowledge of the system databases and database catalogs

Section 4 - Informix Tables, Views and Indexes 15%
Given a scenario, describe how to create a table
Describe when referential integrity should be used
Describe methods of data value constraint
Describe the differences between a table, view, sequences, synonyms, and indices
Describe triggers and appropriate uses
Demonstrate knowledge of schema commands

Section 5 - Informix Data using SQL 14%
Describe how to use SQL to SELECT data from tables
Describe how to use SQL to UPDATE, DELETE, or INSERT data
Demonstrate knowledge of transactions
Describe how to call a procedure or invoke a user defined function

Section 6 - Data Concurrency and Integrity 10%
Identify isolation levels and their effects
Identify objects on which locks can be obtained
Describe transaction integrity mechanisms

Section 7 - Tools and Utilities 15%
Describe onstat, oninit, and onmode utilities
Demonstrate knowledge of data movement utilities
Describe sysadmin database and its functionality
Demonstrate knowledge of the features or functions available in Informix tools

Section 8 - Backup and Restore 8%
Demonstrate knowledge of backup procedures
Demonstrate knowledge of recovery procedures

Section 9 - Replication Technologies 5%
Describe the purpose of the different replication technologies

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IBM Informix test Questions

IBM CodeNet: synthetic Intelligence that can program computer systems And solve A $a hundred Billion Legacy Code difficulty | C2090-558 PDF Questions and Study Guide

notion for AI for Code


computer scientists have lengthy toyed with the conception of creating computer systems that may write courses for different computer systems. synthetic intelligence is an evident technology for the assignment. It has been up to now used for programming on a small scale however alas the effects had been restrained.

artificial intelligence is considered one of our most powerful and versatile technologies in use these days. it may be aware and generate speech, analyze files, respect photos and characters, force automobiles, pilot battle planes, write papers, and operate thousands of other constructive operations. 

Dr. Ruchir Puri is an IBM fellow and Chief Scientist for IBM research. For the past two and a half years, Dr. Puri and a group from IBM research and the MIT-IBM Watson AI Lab have labored on a massive code-intensive AI for Code assignment.  At this year’s IBM feel 2021 convention, Arvind Krishna, Chairman and CEO of IBM, revealed the effects of that two-year effort and introduced an excellent piece of analysis called challenge CodeNet - a big dataset aimed toward educating AI to code.

a big fraction (over 50%) of the code samples used in CodeNet are commonly used to assemble and run ... [+] as it should be on the prescribed test instances. supply: "project CodeNet: a big-Scale AI for Code Dataset for discovering a diversity of Coding tasks," by means of Ruchir Puri et al. 2021 may additionally 25,


CodeNet is similarly named after ImageNet, a groundbreaking AI for computing device imaginative and prescient dataset. ImageNet includes over 14 million images scattered throughout 20,000 categories. ImageNet is the leading reason for imaginative growth that has been made in AI for computing device vision algorithms and compute.  CodeNet dataset likewise has the capability to pressure innovation and growth in future AI for Code items.

CodeNet is remarkable

A evaluation of CodeNet with two familiar and similar datasets supply: Ruchir Puri et al. 2021 ... [+] may 25,


There are a couple of accessible datasets for source code, however most are best beneficial for a couple of targeted tasks. Two commonplace datasets, GCJ and POJ, are compared to CodeNet within the above chart. GCJ became accrued from submissions to Google's coding competition from 2008 to 2020. POJ-104 uses code samples gathered from an educational web page designed for programming competitions. whereas these datasets are narrow in scope, CodeNet is designed for wide and complete use.

CodeNet is unheard of. it is a huge-scale open-supply dataset with 14 million code samples and 500 million traces of code. CodeNet additionally has over 4,000 problem descriptions and 55 programming languages, from up to date ones like C++, Java, Python, and Go to legacy languages like COBOL, Pascal, and FORTRAN. 

in addition to its rich range of languages and complications, eighty% of the complications have over a hundred options each. IBM has annotated the code samples with a rich collection of assistance, equivalent to code dimension, reminiscence footprint, CPU run time, and standing, indicating acceptance or error forms. 

Modernizing our application infrastructure is basic.



IBM’s efforts to boost AI for Code is a component of a larger aim. there's an urgent need to modernize legacy utility infrastructure infused all over everything of world enterprise and government. a huge component of these days’s business code originated from multi-yr efforts of programming groups whose particular person memberships fluctuated over time. consequently, a various quantity of concept and planning went into constructing large monoliths of code that now exist as legacy utility. For those reasons, it's complicated for today’s developers to modernize legacy code or make adjustments to enrich performance, accommodate new features, or realign it for new regulations.

COBOL, a legacy significant 

there are many programs written in a legacy programming language referred to as COBOL (an acronym for "usual company-oriented language").  COBOL was developed and closely promoted via the U.S. branch of defense within the early Nineteen Sixties. through the years, it has grown into an industry regular. It remains used in many enterprise functions, banking transactions and government administrative mainframes.  

since it is so generic, manually upgrading COBOL programs would be a major engaging in in human effort, cash, and scope. COBOL was as soon as considered an advanced programming language. although, as it is a huge a part of our old-fashioned digital infrastructure, it's time for COBOL programs to be modernized.

simple problem statement, however an important assignment 

The difficulty is that a massive legacy base of COBOL must be migrated to new structures and rewritten into greater modern programming languages. It’s a large assignment because IBM estimates about 200 to 220 billion lines of legacy COBOL code has to be rewritten.  on the cost of 32 to 50 cents a coded line using human materials, the modernization of COBOL represents a $one hundred billion difficulty. How lengthy it could take for an entire rewrite using human programmers? That has yet to be described. 

The problem is extra advanced as a result of COBOL is much less prevalent than it once become. Many expert COBOL coders have already retired or are about to retire. The extent of a reported shortage in skilled COBOL programmers first grew to become evident firstly of Covid-19. 

Dr. Puri said, “Nothing brought the shortage of COBOL programmers to the forefront greater than the pandemic. we're nonetheless in the center of a pandemic in many elements of the realm.  fortunately, in the united states we are starting to flip the nook, however you nonetheless see many headlines that COBOL coders are obligatory for the Corona virus fight.”

Dr. Puri also pointed out a further refined however big factor that impacts the availability of expert COBOL programmers. He referred to americans entering the software engineering building enviornment pick not to work on COBOL initiatives. they might somewhat work with new languages in its place.

One step at a time

Translating legacy code into a brand new language is complicated. Complexity stems from the contextual nature of programming languages. Most languages use statements to direct operations to be carried out with the aid of the computing device. each commentary is carried out within the nuanced context of a collection of situations essential to perform the operation. Context in human language may extend to a couple of paragraphs, but for code translations, context can be scattered throughout distinct libraries. consequently, making a choice on context is complex for artificial intelligence. in addition, the greater the program, the more problematic the interpretation becomes. 

CodeNet comes with a collection of developer equipment and synthetic intelligence models for code understanding in order to continue to be more suitable with more advantageous algorithms to extract context and translate from a legacy code to a latest language. For this, CodeNet has trial enter and output verify units for over 7 million code samples. 

Early success proves CodeNet’s capabilities and value 

The IBM site highlights an early instance of how CodeNet was used to modernize legacy code:

“as an instance, a big automotive customer approached IBM to support replace a $200 million asset along with 3,500, multi-generation Java data. These files consisted of more than 1000000 traces of code, developed over a decade with numerous generations of Java know-how.

It became a complex monolithic utility code, not conducive to cloud-based mostly environments. by way of applying our AI for Code stack, we decreased the enterprise's year-long ongoing code migration procedure all the way down to just four weeks, modernizing and producing over 25 new cloud-native microservices by way of refactoring the legacy monolithic utility code."

near-time period knowledge

based on Dr. Puri, CodeNet dataset can enable AI breakthroughs so that you can make developers greater productive and contribute to the evolution of AI for Code that promotes the advent of imaginitive algorithms. “These capabilities all relate to AI’s skill to consider code,” he noted. “In a extensive feel, over time, AI will make builders more productive with the aid of coping with routine tasks they now do on a daily foundation.”

sooner or later, he says AI for Code will evolve just like the route taken by AI for human language knowing, besides the fact that children during this case it is going to build upon the wealthy body of abilities in software evaluation. applied sciences might be developed that transcend the range of programming languages via reasoning concerning the underlying control and information stream representations of the code. in place of simply executing working statements, artificial intelligence will turn into greater effective because it acquires a deeper figuring out of the essence of a programming language.  additionally, as AI starts off to take on more and more of the pursuits every day initiatives, developers will stream up to the next stage and commit more time to developing new recommendations and algorithms.  

here's what Dr. Puri believes application development will look like the usage of breakthroughs enabled by means of CodeNet and its AI tools:

  • If a code is not purposeful, AI will analyze the code and supply options that the developer can use to make it useful, given the constraints of examine records.
  • If the code doesn't run quickly ample, then AI will supply thoughts for larger efficiency to make the code run faster.
  • AI will have the skill to enhance a code’s reminiscence consumption. If code consumes too a lot memory, AI will deliver suggestions to cut back the code’s reminiscence footprint. 
  • AI can search for equivalent codes. It will not be constrained to searching within the language of the program being coded. it might probably also seek identical code across distinctive languages.  
  • Breakthroughs enabled through CodeNet will augment existing software evaluation learn how to bring some of the most powerful features — developers can be in a position to translate code into many different languages. 
  • sooner or later, can CodeNet AI software itself?

    When requested if CodeNet AI might be in a position to software itself, Dr. Puri gave an enchanting answer:  

    “I are looking to reply that query in two techniques,” he noted. “I do predict CodeNet AI to self-be taught in terms of improvements it needs to have. i might say that could occur in a couple of years or so. in spite of this, CodeNet and AI for Code is much, far-off from coming up with new algorithms by itself. I do expect AI to have self-studying where it realizes the weaker materials of its working towards and automatically corrects those gaps by way of even synthesizing one of the vital facts.  via algorithmic breakthroughs enabled via CodeNet, progress in AI for Code may be significantly accelerated, enabling builders to center of attention on extra inventive programming endeavors.”  

    Analyst Notes

  • it is hard to overstate CodeNet’s lengthy-term talents price. it is a big milestone for AI for Code. just as ImageNet changed the trajectory of AI for computer imaginative and prescient, CodeNet will do likewise for AI for Code.
  • A 2019 IEEE examine confirmed that developers most effective spend 48% of their time on actual code building. by using coping with activities projects, and featuring tools and AI innovations to enrich code, CodeNet will make that 48% vastly greater productive. 
  • it is essential that IBM developed CodeNet as open supply. That assures CodeNet may have community aid for continual improvements and new algorithms. it is going to also speed up adoption which in flip speeds up advancements.
  • using CodeNet on scale tasks will outcomes within the introduction of more inventive algorithms, which in flip will additional Strengthen the efficiency of downstream coding tasks. 
  • Given CodeNet’s predicted efficiency and pace of translation, as bulk translation of legacy code receives underway, since the assignment is so gigantic, it is probably going to have a small, but measurable, have an effect on on country wide productivity. 
  • changing legacy code to newer and greater manageable code can also have a good have an impact on on national protection and security as legacy methods are rewritten and replaced with faster, more efficient code that has a much wider pool of aid elements.
  • with the aid of streamlining the process of converting legacy code into contemporary language, more skilled programmers should be accessible to maintain and update the translated code as well as create new code.
  • i am expecting many CodeNet research papers to be published within the subsequent six months. I trust CodeNet will create a excessive stage of educational pastime. 
  • notice: Moor Insights & method writers and editors may have contributed to this article. 

    Moor Insights & strategy, like any research and analyst firms, provides or has offered paid research, evaluation, advising, or consulting to many excessive-tech companies within the trade, including 8x8, advanced Micro gadgets, Amazon, utilized Micro, ARM, Aruba Networks, AT&T, AWS, A-10 strategies, Bitfusion, Blaize, field, Broadcom, Calix, Cisco techniques, Clear software, Cloudera, Clumio, Cognitive programs, CompuCom, Dell, Dell EMC, Dell applied sciences, Diablo technologies, Digital Optics, Dreamchain, Echelon, Ericsson, severe Networks, Flex, Foxconn, frame (now VMware), Fujitsu, Gen Z Consortium, Glue Networks, GlobalFoundries, Google (Nest-Revolve), Google Cloud, HP Inc., Hewlett Packard commercial enterprise, Honeywell, Huawei applied sciences, IBM, Ion VR, Inseego, Infosys, Intel, Interdigital, Jabil Circuit, Konica Minolta, Lattice Semiconductor, Lenovo, Linux foundation, MapBox, Marvell, Mavenir, Marseille Inc, Mayfair fairness, Meraki (Cisco), Mesophere, Microsoft, Mojo Networks, national devices, NetApp, Nightwatch, NOKIA (Alcatel-Lucent), Nortek, Novumind, NVIDIA, Nuvia, ON Semiconductor, ONUG, OpenStack groundwork, Oracle, Poly, Panasas, Peraso, Pexip, Pixelworks, Plume Design, Poly, Portworx, Pure Storage, Qualcomm, Rackspace, Rambus, Rayvolt E-Bikes, crimson Hat, Residio, Samsung Electronics, SAP, SAS, Scale Computing, Schneider electric powered, Silver peak, SONY, Springpath, Spirent, Splunk, sprint, Stratus technologies, Symantec, Synaptics, Syniverse, Synopsys, Tanium, TE Connectivity, TensTorrent, Tobii know-how, T-cell, Twitter, harmony technologies, UiPath, Verizon Communications, Vidyo, VMware, Wave Computing, Wellsmith, Xilinx, Zebra, Zededa, and Zoho which could be cited in blogs and research.

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