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IBM C2090-543 : DB2 9.7 Application Development Exam

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Test Number : C2090-543
Test Name : DB2 9.7 Application Development
Vendor Name : IBM
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C2090-543 test Format | C2090-543 Course Contents | C2090-543 Course Outline | C2090-543 test Syllabus | C2090-543 test Objectives


Exam Title : IBM Certified Application Developer - DB2 9.7 for Linux Unix and Windows
Exam ID : C2090-543
Exam Duration : 90 mins
Questions in test : 60
Passing Score : 60%
Exam Center : Pearson VUE
Real Questions : IBM DB2 Application Development Real Questions
VCE Practice Test : IBM C2090-543 Certification VCE Practice Test

Database Objects
- Demonstrate knowledge of naming conventions of DB2 objects
Aliases
Views
Tables
- Describe when to use SQL routines, functions and modules
How to use DB2's built-in routines @ Functions @ Stored procedures
- Demonstrate knowledge of data types
Integer
Floating point
Strings
Date/time
LOBs
XML data type 11%
Traditional Data Manipulation
- Demonstrate knowledge of authorities needed to access data in an application
- Describe the differences between dynamic and static SQL
- Demonstrate knowledge of how to query databases across multiple tables and views
- Given a scenario, demonstrate knowledge of changing data
Insert
Update
Delete
- Demonstrate knowledge of cursors
Types of cursors @ Read only @ Updatable @ Scrollable
Scope of cursors
Create and manipulate cursors
- Given a scenario, demonstrate the ability to manipulate large objects
Locators
- Demonstrate the ability to manage a unit of work
Isolation levels
COMMIT
ROLLBACK
SAVEPOINT 32%
XML Data Manipulation
- Given a scenario, demonstrate knowledge of XML schema validation
XML schema evolution
- Demonstrate the ability to use XML functions
XMLPARSE @ Whitespace handling
XMLSERIALIZE
XMLTRANSFORM
Functions for preparing XML documents from relational data
- Given a scenario, demonstrate knowledge of how to use XQuery expressions and evaluate the results
- Given a scenario, describe how to construct queries that retrieve both traditional and XML data 15%
Core Concepts
- Describe how to bind and rebind a package
- Given a scenario, demonstrate knowledge of using parameter markers
- Demonstrate knowledge of how to connect to a database (may use scenarios)
JDBC
ADO.NET
CLI/ODBC
php_ibm_db2
Embedded SQL
- Demonstrate the ability to submit an SQL statement
JDBC
ADO.NET
CLI/ODBC
php_ibm_db2
Embedded SQL
- Describe how to manipulate result sets
JDBC
ADO.NET
CLI/ODBC
php_ibm_db2
Embedded SQL
- Demonstrate knowledge of problem determination
JDBC
ADO.NET
CLI/ODBC
php_ibm_db2
Embedded SQL 27%
Advanced Programming
- Given a scenario, describe how to create and register external stored procedures
- Given a scenario, describe how to create external functions
OLE DB table
External table
- Given a scenario, demonstrate knowledge of how changing data will work when referential constraints are involved
- Demonstrate knowledge of distributed unit of work (two phase commit)
- Demonstrate knowledge of trusted contexts
- Demonstrate knowledge of using advanced database objects
Global declared temporary tables
Sequences
MQTs 15%



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IBM 9.7 Practice Questions

AI nonetheless struggles to recognize hateful memes, however’s slowly enhancing | C2090-543 Practice Test and Cheatsheet

When it involves client expectations, the pandemic has changed every thing Register here

fb in may also launched the Hateful Memes problem, a $100,000 competitors geared toward spurring researchers to boost techniques that can determine memes intended to hurt individuals. the primary section of the one-yr contest these days crossed the halfway mark with over three,000 entries from a whole bunch of groups everywhere. however whereas progress has been encouraging, the leaderboard suggests even the correct-performing techniques battle to outdo people when it comes to deciding upon hateful memes.

Detecting hateful memes is a multimodal difficulty requiring a holistic realizing of photographs, phrases in pictures, and the context across the two. unlike most computing device gaining knowledge of methods, humans intrinsically keep in mind the mixed meaning of captions and photographs in memes. for instance, given text and an image that seem innocuous when considered apart (e.g., “look what number of people love you” and an image of a barren desolate tract), individuals recognize that these points can take on potentially hurtful meanings when they’re paired or juxtaposed.

using a labeled dataset of 10,000 photos facebook provided for the competitors, a group of humans informed to recognize hate speech managed to precisely identify hateful memes eighty four.70% of the time. As of this week, the proper three algorithms on the public leaderboard attained accuracies of eighty three.four%, 85.6%, and 85.8%. whereas these numbers superior the sixty four.7% accuracy the baseline visual BERT COCO mannequin performed in might also, they’re most effective marginally more desirable than human performance on absolutely the highest end. Given 1 million memes, the AI gadget with eighty five.eight% accuracy would misclassify 142,000 of them. If it were deployed on facebook, as an example, untold numbers of users may be exposed to hateful memes.

The challenges of multimodal getting to know

Why does classifying hateful memes proceed to pose a problem for AI programs? in all probability as a result of even human experts on occasion struggle with the task. The annotators who attained 84.70% accuracy on the Hateful Memes benchmark weren’t inexperienced; they acquired 4 hours of training in recognizing hate speech and accomplished three pilot runs in which they were tasked with categorizing memes and given feedback to enrich their efficiency. regardless of the prep, every annotator took a standard of 27 minutes to work out even if a meme was “hateful.”

realizing why the classification difficulty is more acute within the realm of AI requires potential of how multimodal systems work. In any given multimodal equipment, computing device imaginative and prescient and herbal language processing models are customarily proficient on a dataset collectively to be trained a mixed embedding area, or an area occupied by means of variables representing particular elements of the photos and textual content. To construct a classifier that may notice hateful memes, researchers need to mannequin the correlation between pictures and text, which helps the gadget find an alignment between the two modalities. This alignment informs the system’s predictions about no matter if a meme is hateful.

Above: pattern memes from fb’s hateful memes dataset.

Some multimodal systems leverage a “two-movement” architecture that approaches visual and language counsel earlier than fusing them together. Others adopt a “single-movement” architecture that directly combines both modalities in an previous stage, passing photographs and textual content independently via encoders to extract facets that will also be fused to operate classification. regardless of architecture, state-of-the-art systems employ a way known as “attention” to mannequin the relationships between graphic regions and phrases in line with their semantic meaning, increasingly targeting only the most valuable regions in the a lot of photographs.

lots of the Hateful Memes problem contestants have yet to detail their work, however in a brand new paper, IBM and school of Maryland scientists clarify how they included an image captioning workflow into the meme detection technique to nab 13th region on the leaderboard. inclusive of three components — an object detector, graphic captioner, and “triplet-relation community” — the gadget learns to distinguish hateful memes through photograph captioning and multimodal aspects. a picture captioning model trains on pairs of images and corresponding captions from a dataset, whereas a separate module predicts whether memes are hateful via drawing on graphic points, picture caption facets, and features from photo textual content processed through an optical character cognizance model.

The researchers accept as true with their triplet-relation community may be prolonged to other frameworks that require “robust attention” from multimodal signals. “The performance increase introduced by way of graphic captioning extra shows that, due to the prosperous effective and societal content material in memes, a realistic answer should still also agree with some more information related to the meme,” they wrote in a paper describing their work.

different suitable-ranking groups, each of which had to conform to terms of use particular to fb’s hateful memes dataset with a purpose to access it, are expected to present their work all over the NeurIPS 2020 computer gaining knowledge of convention next week.

primary shortcomings

expertise like herbal language understanding, which humans acquire early on and practice in some situations subconsciously, existing roadblocks for even proper-performing models, specially in areas like bias.

In a analyze authorised to last 12 months’s annual meeting of the association for Computational Linguistics, researchers from the Allen Institute for AI found that annotators’ insensitivity to alterations in dialect could lead on to racial bias in automatic hate speech detection models. A separate work came to the same conclusion. And in line with an investigation by NBC, Black Instagram users within the U.S. have been about 50% more more likely to have their debts disabled by way of automated hate speech moderation techniques than these whose endeavor indicated they have been white.

These kinds of prejudices can become encoded in laptop imaginative and prescient fashions, which are the accessories multimodal programs use to categorise photos. again in 2015, a application engineer discovered that the photograph attention algorithms deployed in Google photos, Google’s photo storage service, were labeling Black individuals as “gorillas.” a college of Washington examine discovered women have been drastically underrepresented in Google photo searches for professions like “CEO.” Google’s Cloud imaginative and prescient API these days mislabeled thermometers held via people with darker skin as guns. And countless experiments have proven that image-classifying models knowledgeable on ImageNet, a popular (however complicated) dataset containing pictures scraped from the internet, instantly be trained humanlike biases about race, gender, weight, and greater.

Audits of multimodal systems like visual query answering (VQA) models, which incorporate two records kinds (e.g., text and pictures) to reply questions, exhibit that these biases and others negatively have an effect on classification efficiency. VQA methods generally lean on statistical relationships between words to answer questions no matter images. Most fight when fed a query like “What time is it?” — which requires the skill of being in a position to examine the time on a clockface — however be capable to reply questions like “What color is the grass?” as a result of grass is commonly eco-friendly in the dataset used for practicing.

Bias isn’t the best issue multimodal systems should deal with. A growing physique of labor suggests natural language models in certain combat to be aware the nuances of human expression.

A paper posted through researchers affiliated with fb and Tel Aviv institution found that on a benchmark designed to measure the extent to which an AI equipment can observe guidance, a favored language mannequin carried out dismally throughout all initiatives. Benchmarks time-honored in the AI and computing device gaining knowledge of analysis neighborhood, corresponding to XTREME, have been found to poorly measure models’ knowledge.

fb might disagree with this discovering. In its existing group requirements Enforcement record, the company spoke of it now proactively detects 94.7% of the hate speech it in the end eliminates, which amounted to 22.1 million textual content, picture, and video posts in Q3 2019. however critics take situation with these claims. a brand new York college study posted in July estimated that fb’s AI systems make about 300,000 content moderation error per day, and challenging posts proceed to slip via fb’s filters.

Multimodal classifiers are additionally susceptible to threats through which attackers attempt to sidestep them via editing the look of images and textual content. In a facebook paper published past this year, which treated the Hateful Memes problem as a case study, researchers managed to go back and forth up classifiers 73% of the time through manipulating both photos and text and between 30% and forty% of the time by way of editing either images or textual content alone. in a single instance involving a hateful meme referencing body smell, formatting the caption “Love the way you scent nowadays” as “LOve the wa y you smell these days” brought about a device to categorise the meme as no longer hateful.

Hateful memes adversarial attack

Above: Examples of hateful and non-hateful memes in the hateful memes dataset and adversarial photo and textual content inputs just like the ones the facebook researchers generated.

photo credit score: facebook a tricky street ahead

despite the limitations standing in the manner of developing superhuman hateful meme classifiers, researchers are forging ahead with ideas that promise to enhance accuracy.

facebook attempted to mitigate biases in its hateful memes dataset by utilizing confounders, or memes whose effect is the opposite of the offending meme. via taking an originally suggest-spirited meme and turning it into something appreciative or complimentary, the group hoped to upset whatever prejudices could allow a multimodal classifier to without problems gauge the suggest exceptional of memes. separately, in a paper ultimate year, fb researchers pioneered a new discovering strategy to in the reduction of the magnitude of essentially the most biased examples in VQA model practising datasets, implicitly forcing models to use both photographs and textual content. And facebook and others have open-sourced libraries and frameworks, like Pythia, to bolster vision and language multimodal research.

however hateful memes are a moving target as a result of “hateful” is a nebulous category. The act of endorsing hateful memes can be regarded hateful, and memes can also be indirect or subtle of their perpetration of rumors, fake news, extremist views, and propaganda, apart from hate speech. fb considers “assaults” in memes to be violent or dehumanizing speech; statements of inferiority; and requires exclusion or segregation in accordance with characteristics like ethnicity, race, nationality, immigration repute, faith, caste, sex, gender identity, sexual orientation, and incapacity or sickness, in addition to mocking hate crime. however regardless of its huge attain, this definition is probably going too slim to cowl all types of hateful memes.

emerging tendencies in hateful memes, like writing text on colored background photographs, additionally threaten to stymie multimodal classifiers. beyond that, most specialists agree with extra analysis will be required to superior be aware the relationship between photos and textual content. This could require better and greater distinct datasets than fb’s hateful memes collection, which draws from 1 million facebook posts however discards memes for which substitute pictures from Getty photos can’t be found to prevent copyright considerations.

even if AI ever surpasses human efficiency on hateful meme classification by means of very plenty could be immaterial, given the unreliability of such techniques at a scale as colossal as, say, fb’s. but if that comes to pass, the strategies may well be utilized to different challenges in AI and computer discovering. research firm OpenAI is reportedly developing a gadget knowledgeable on images, text, and different statistics the use of massive computational components. The enterprise’s management believes this is probably the most promising course towards artificial customary intelligence, or AI that may gain knowledge of any assignment a human can. within the close time period, novel multimodal methods could lead to more suitable efficiency in tasks from image captioning to visible dialogue.

“Hate speech is an important societal issue, and addressing it requires improvements in the capabilities of contemporary laptop researching systems,” the coauthors of facebook’s common paper write in describing the Hateful Memes problem. “We found that results on the task reflected a concrete hierarchy in multimodal sophistication, with greater superior fusion models performing enhanced. still, present state-of-the-artwork multimodal fashions operate pretty poorly on this dataset, with a large gap to human performance, highlighting the problem’s promise as a benchmark to the community.”


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