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Test Number : C2090-623
Test Name : IBM Cognos Analytics Administrator V11
Vendor Name : IBM
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C2090-623 test Format | C2090-623 Course Contents | C2090-623 Course Outline | C2090-623 test Syllabus | C2090-623 test Objectives
Exam Title : IBM Certified Administrator - IBM Cognos Analytics Administrator V11
Exam ID : C2090-623
Exam Duration : 90 mins
Questions in test : 60
Passing Score : 38 / 60
Official Training : IBM Cognos Analytics - Enterprise Administration (v11.0)
IBM Cognos Analytics: Architecture and Logging (v11.0)
Exam Center : Pearson VUE
Real Questions : IBM Cognos Analytics Administrator Real Questions
VCE practice test : IBM C2090-623 Certification VCE Practice Test
- Describe the steps and options in content administration
- Describe the steps of creating a datasource
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- Describe using LifeCycle Manager (LCM)
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- Identify using Library management 25%
- Describe how to implement and manage audit logging
- Demonstrate troubleshooting techniques
- Describe how to implement and work with tracing
- Identify how to use additional performance tools
- Describe how to monitor system components 20%
- Identify techniques to organize report content in the portal
- Describe the request flow of report execution
- Describe how to manage and schedule reports
- Describe how to manage report execution 15%
- Describe how to secure an environment
- Describe how authentication is implemented
- Identify how authorization is achieved and implemented
- Describe how to secure content 20%
- Identify the architecture and components
- Describe installation options and configurations
- Demonstrate tasks to implement configuration and performance tuning
- Describe tasks used to manage components
- Demonstrate troubleshooting techniques in the server environment
- Describe maintaining configuration settings 20%
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IBM Practice Test
NIST benchmarks show facial attention know-how nonetheless struggles to determine Black faces | C2090-623 cheat sheet and test Questions
every few months, the U.S. national Institute of requirements and know-how (NIST) releases the consequences of benchmark tests it conducts on facial consciousness algorithms submitted by groups, universities, and impartial labs. A component of these assessments focal point on demographic performance — it truly is, how regularly the algorithms misidentify a Black man as a white man, a Black girl as a Black man, and so on. Stakeholders are brief to say that the algorithms are always improving in regards to bias, but a VentureBeat evaluation reveals a different story. truly, their findings forged doubt on the idea that facial consciousness algorithms have become more desirable at recognizing individuals of color.
That isn’t incredible, as numerous reviews have shown facial attention algorithms are prone to bias. but the latest records element comes as some companies push to expand their market share, aiming to fill the hole left with the aid of Amazon, IBM, Microsoft, and others with self-imposed moratoriums on the sale of facial cognizance systems. In Detroit this summer season, metropolis subcontractor Rank One Computing started presenting facial recognition to local law enforcement over the objections of privacy advocates and protestors. remaining November, l. a.-primarily based TrueFace became awarded a contract to install laptop imaginative and prescient tech at U.S. Air force bases. And the list goes on.
NIST makes use of a mugshot corpus gathered over 17 years to seek demographic blunders in facial awareness algorithms. mainly, it measures the fees at which:
White men are misidentified as Black men
White guys are misidentified as distinctive white guys
Black men are misidentified as white men
Black guys are misidentified as different Black men
White women are misidentified as Black women
White girls are misidentified as different white girls
Black girls are misidentified as white girls
Black ladies are misidentified as distinct Black ladies
NIST determines the error price for each and every category — also called the false suit rate (FMR) — by using recording how often an algorithm returns a wrong face for 10,000 mugshots. An FMR of .0001 implies one mistaken identity for each 1,000, whereas an FMR of .1 implies one mistake for every 10.
To get a sense of no matter if FMRs have lowered or elevated in exact years, they plotted the algorithms’ FMRs from businesses with commercial deployments, as measured by means of NIST — two algorithms per firm. evaluating the performance of the two algorithms offered us an idea of bias over time.
NIST’s benchmarks don’t account for changes vendors make earlier than the algorithms are deployed, and some providers could not ever installation the algorithms commercially. since the algorithms submitted to NIST are often optimized for most suitable basic accuracy, they’re additionally no longer necessarily consultant of how facial consciousness programs behave in the wild. as the AI Now Institute notes in its exact document: whereas current requisites just like the NIST benchmarks “are a step in the appropriate direction, it would be untimely to rely on them to investigate efficiency … [because there] is currently no standard apply to document and speak the histories and limits of benchmarking datasets … and thus no solution to determine their applicability to a particular system or suitability for a given context.”
still, the NIST benchmarks are possibly the closest aspect the trade has to an purpose measure of facial recognition bias.
Rank One Computing
Rank One, whose facial recognition software is presently getting used by the Detroit Police department (DPD), better throughout all demographic categories from November 2019 to July 2020, above all with recognize to the variety of Black girls it misidentifies. besides the fact that children, the FMRs of its latest algorithm continue to be high; NIST stories that Rank One’s application misidentifies Black guys between 1 and a pair of times in 1,000 and Black girls between 2 and three times in 1,000. That error expense could translate to vast numbers, considering roughly three.four million of Detroit’s over four million residents are Black (in accordance with the 2018 census).
Above: FMR rates as measured via NIST. higher is worse.
perhaps predictably, Rank One’s algorithm became thinking in a wrongful arrest that some publications mistakenly characterised because the first of its form in the U.S. (Following a firestorm of criticism, Rank One observed it might add “legal ability” to thwart misuse and the DPD pledged to limit facial focus to violent crimes and home invasions.) in the case of the arrest, the DPD violated its own procedural guidelines, which avoid the use of the equipment to lead generation. but there’s proof of bias in the transparency stories from the DPD, which show that just about all (96 out of 98) of the pictures Detroit cops have run via Rank One’s software thus far are of Black suspects.
Detroit’s three-12 months, $1 million facial consciousness expertise contract with DataWorks Plus, a reseller of Rank One’s algorithm, expired on July 24. but DataWorks agreed final 12 months to lengthen its service contract via September 30. past that, there’s nothing combating the city’s IT department from servicing the utility itself in perpetuity.
TrueFace’s expertise, which early subsequent 12 months will start powering facial recognition and weapon identification systems on a U.S. Air force base, grew to be worse at opting for Black ladies from October 2019 to July 2020. The latest edition of the algorithm has an FMR between 0.015 and nil.020 for misidentifying Black women compared with the old version’s FMR of between 0.010 and nil.015. U.S. Air drive Personnel middle statistics exhibit there were greater than forty nine,200 Black provider individuals enlisted as of January 2020.
Above: FMR charges as measured by way of NIST. bigger is worse.
RealNetworks and AnyVision
Equally troubling are the effects for algorithms from RealNetworks and from AnyVision, an alleged employer for Israeli military checkpoints in the West financial institution.
AnyVision, which these days raised $43 million from undisclosed traders, instructed Wired its facial cognizance application has been piloted in a whole lot of web sites all over, together with faculties in Putnam County, Oklahoma and Texas metropolis, Texas. RealNetworks offers facial consciousness for defense force drones and physique cameras via a subsidiary referred to as SAFR. After the Parkland, Florida college shooting in 2018, SAFR made its facial focus tech free to schools throughout the U.S. and Canada.
while AnyVision’s and RealNetworks’ algorithms misidentify fewer Black women than earlier than, they operate worse with Black guys. involving different demographic organizations, they exhibit little to no growth when measured against FMR.
Above: FMR fees as measured by using NIST. larger is worse.
Above: FMR charges as measured via NIST. larger is worse.
NtechLab’s algorithm exhibits a comparable regression in FMR. The enterprise, which gained notoriety for an app that allowed clients to fit images of americans’s faces to a Russian social network, these days got a $3.2 million contract to install its facial focus equipment all through Moscow. NtechLab also has contracts in Saint Petersburg and in Jurmala, Latvia.
Above: FMR costs as measured with the aid of NIST. better is worse.
whereas the company’s most exact algorithm performed reductions in FMR for white men and ladies, it performs worse with Black guys than its predecessor. FMR in this class is nearer to 0.005, up from just over 0.0025 in June 2019.
yet another contender is Gorilla applied sciences, which claims to have installed facial attention know-how in Taiwanese prisons. NIST records suggests the company’s algorithm grew to become measurably worse at deciding on Black ladies and guys. The latest edition of Gorilla’s algorithm has an FMR rating of between 0.004 and 0.005 for misidentifying Black girls and a ranking of between 0.001 and nil.002 for misidentifying white ladies.
Above: FMR prices as measured via NIST. larger is worse.
These are just a number of examples of facial recognition algorithms whose biases have been exacerbated over time, at the least according to NIST facts. The style elements to the intractable difficulty of mitigating bias in AI methods, above all laptop vision techniques. One difficulty in facial awareness is that the data sets used to train algorithms skew white and male. IBM discovered that 81% of americans within the three face-image collections most generally mentioned in tutorial experiences have lighter-coloured dermis. teachers have found that photographic know-how and techniques can also desire lighter dermis, together with every thing from Sepia-tinged film to low-distinction digital cameras.
The algorithms are sometimes misused within the box, as well, which tends to extend their underlying biases. A report from Georgetown law’s center on privacy and technology details how police feed facial focus application wrong records, including composite sketches and photographs of celebrities who share physical features with suspects. The ny Police branch and others reportedly edit pictures with blur outcomes and 3D modelers to make them extra conducive to algorithmic face searches.
whatever the explanations for the bias, an increasing variety of cities and states have expressed issues about facial attention technology — mainly in the absence of federal guidelines. Oakland and San Francisco in California; Portland, Oregon; and Somerville, Massachusetts are among the many metros where legislations enforcement is prohibited from using facial consciousness. In Illinois, corporations have to get consent earlier than amassing biometric counsel, including face photos. And in Massachusetts, lawmakers are on the grounds that a moratorium on govt use of any biometric surveillance system within the state.
Congress, too, has put forth a bill — the Facial recognition and Biometric know-how Moratorium Act of 2020 — that could sharply limit federal govt officers’ use of facial attention systems. The invoice’s introduction follows the eu commission’s consideration of a 5-year moratorium on facial cognizance in public areas.
“Facial focus is a uniquely unhealthy variety of surveillance. here's now not just a few Orwellian expertise of the future — it’s being used via legislations enforcement companies across the country presently, and doing damage to communities at the moment,” battle for the future deputy director Evan Greer observed previous this yr in a statement involving proposed law. “Facial focus is the superb expertise for tyranny. It automates discriminatory policing … in their deeply racist crook justice equipment. This legislation without difficulty bans legislations enforcement use of facial cognizance within the u.s.. That’s exactly what they need at this time. They provide this invoice their full endorsement.”
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