OG0-091 learning - TOGAF 9 Part 1 Updated: 2023
|Review OG0-091 dumps question before you step through examination|
Exam Code: OG0-091 TOGAF 9 Part 1 learning November 2023 by Killexams.com team|
OG0-091 TOGAF 9 Part 1
TOGAF® 9 Part 1 Exam
Exam Name: TOGAF® 9 Part 1 Exam
OG0-091 - English
OG0-094 - Brazilian Portuguese
OG0-096 - Simplified Chinese
OG0-F91 - French
OG0-S91 - Latin American Spanish
Qualification upon passing: TOGAF 9 Foundation (and partial credit towards the TOGAF 9 Certified qualification)
Delivered at: Authorized Examination Provider Test Centers and via Online Proctored/
Open Book: No
Exam type: Multiple choice
Number of questions: 40
Pass score: 55% (22 out of 40 questions)
Time limit: 60 minutes (*)
Retake policy: If you fail the test you must wait one month before another attempt
Examination Fee: See Fees
Recommended Study: A Study Guide is available. The practice test included with the Study Guide is also available on its own.
|TOGAF 9 Part 1|
The-Open-Group TOGAF learning
Other The-Open-Group exams0G0-081 TOGAF 8 Certification for Practitioners
OG0-081 TOGAF 8 Certification for Practitioners
OG0-091 TOGAF 9 Part 1
OG0-092 TOGAF 9 Part 2
OG0-093 OG0-093 TOGAF 9 Combined Part 1 and Part 2
OG0-061 IT4IT Part 1
|Simply experience our OG0-091 Questions answers and sense guaranteed around the OG0-091 exam. You will pass your OG0-091 test at Excellent Marks or your money back. We have latest database of OG0-091 Dumps from genuine test to have the capacity to give you a prep to get ready and pass OG0-091 test at the first attempt. Our OG0-091 vce test simulator is best to practice OG0-091 braindumps.|
OG0-091 Real Questions
OG0-091 Practice Test
OG0-091 dumps free
TOGAF 9 Part 1
Which one of the following best describes the implications of TOGAF being a generic framework?
A. The organization must utilize an architecture tool in order to tailor the templates for use
B. It must be adapted to satisfy organization specific requirements
C. It can be utilized by most enterprises without further customization
D. It can only be used for enterprise level architecture projects
E. It should only be employed under the supervision of highly trained consultants
Which of the following is the architecture domain that describes the logical software and hardware capabilities?
A. Application Architecture
B. Business Architecture
C. Data Architecture
D. Technology Architecture
Which section of the TOGAF document describes the processes, skills and roles to establish and operate an
architecture function within an enterprise?
A. Part II: Architecture Development Method
B. Part III: ADM Guidelines and Techniques
C. Part IV: Architecture Content Framework
D. Part VI: TOGAF Reference Models
E. Part VII: Architecture Capability Framework
Which one of the following is NOT an element of an architecture framework?
A. A common vocabulary
B. A list of recommended standards
C. A method for designing an information system in terms of building blocks
D. A set of structuresWhich can be used to develop a broad range of architectures
E. A system development lifecycle method for software engineering
Which one of the following describes classification methods for architecture and solution artifacts within the
A. Architecture Landscape
B. Architecture Vision
C. Enterprise Continuum
D. Governance Log
E. Standards Information Base
Which one of the following statements about the structure of the TOGAF 9 document is true?
A. Part I describes the TOGAF approach to Enterprise Architecture
B. Part II describes the definitions of terms used and the changes between versions of TOGAF
C. Part III describes requirements management and is considered to be the core of TOGAF
D. Part IV describes the ADM: a collection of guidelines and techniques used in TOGAF 9
According to TOGAF, Which one of the following best describes an enterprise architecture?
A. An architecture of a commercial organization
B. An architecture that consists of more than one subsidiary company
C. An architecture that crosses multiple systems, and multiple functional groups within the enterprise
D. The highest level of architecture that can be achieved in a given organization
In TOGAF, What is the difference between an artifact and a deliverable?
A. An artifact contains one or more deliverables
B. Artifacts and deliverables are synonymous; there is no difference between them
C. Deliverables are prepared by the Project Manager, whereas artifacts are defined by the Architect
D. Deliverables are reusable, whereas artifacts are unique to a given architecture project
E. Deliverables are specified as contractual outputs from a project, whereas artifacts are not
Which one of the following lists the main components within the TOGAF Architecture Repository?
A. Organizational Metamodel, Architecture Capability, Architecture Landscape, Best Practices, Reference
Library, Compliance Strategy
B. Architecture Metamodel, Organizational Capability Model, Application Landscape, SIB, Reference
Library, Governance Model
C. Business Metamodel, Architecture Capability, Architecture Landscape, SIB, Reference Library,
D. Architecture Metamodel, Architecture Capability, Architecture Landscape, SIB, Reference Library,
According to the TOGAF Document Categorization Model, Which category describes a technique that is
referenced by processes categorized as TOGAF Core and TOGAF Mandated?
A. TOGAF Guidelines and Techniques
B. TOGAF Recommended
C. TOGAF Supporting
D. TOGAF Extension
Which of the following reasons best describes why the ADM numbering scheme for versioning output is an
example and not mandatory?
A. To show the evolution of deliverables
B. To permit adaptation as required
C. To enable use with the Architecture Content Framework
D. To support change management
According to TOGAF, Which of the following are the architecture domains that are commonly accepted subsets of
an overall enterprise architecture?
A. Application, Business, Data, Technology
B. Capability, Segment, Strategic
C. Context, Definition, Governance, Transformation
D. Definition, Realization, Transition, Vision
For More exams visit https://killexams.com/vendors-exam-list
Kill your test at First Attempt....Guaranteed!
Open Learning initiatives and programs provide anyone with access to the Internet with the ability to learn about virtually any subject. All of these programs are free and readily available to anyone who wants to learn, regardless of location and prior knowledge or education. They vary in from lectures to genuine online classes with homework, and some classes even offer certificates upon completion of a course.
Udacity provides free quarterly courses in mathematics, science and technology. During each quarter users can sign up for any number of courses they choose. The professors at Udacity are qualified volunteers who provide lectures and homework throughout the course, as well as a certificate once the courses is completed.
Carnegie Mellon University
The Carnegie Mellon University Open Learning initiative is a service that offers free online courses. These courses are taught by qualified educators who aim to fuse research, high quality courses, and student and instructor feedback to Boost secondary education.
Khan Academy boasts over 3800 videos on a wide variety of Topics ranging from mathematics to world history to chemistry and finance. Videos are all available, with no sign-up required, however, registering account enables user to earn badges, track their learning progress and stats and access a unique, adaptive learning environment that changes according the user's individual pace and needs.
The Harvard Extension School provides students with an opportunity to take courses, earn certificates, or work toward a degree.
Queen's University Belfast
Queens University Belfast’s Open Learning Program offers a wide variety of part-time courses that are open to all adults, regardless of qualifications or experience. These courses are offered during the day, evenings and weekends and 3 programs are available each academic year.
University of Guelph
The Open Learning program at the University of Guelph is a distance-only mode of study that provides individuals with access to degree-credit university courses. These courses are for individuals who are interested in personal enrichment, professional updating, or eventual application to a degree program.
Coursera offers free online courses from some of the top universities in the world with certificates available upon completion of some courses.
Open Yale Courses are open, free introductory courses that are taught by Yale teachers and Scholars.
The site provides a variety of learning options. There are 16 free classes.
Enterprise Cloud Platform Strategic Account Manager
Thani Sokka has over 17 years of experience in systems engineering, enterprise architecture, design and development, software project management, and data/information modeling, working with the latest IT systems technologies and methodologies. He has spent significant time designing solutions for the public sector, media, retail, manufacturing, financial, biomedical, and social/gaming industries. At Google, Thani is a Strategic Account Manager focused on empowering Google Cloud Platform’s largest customers derive the most from Google’s cloud technologies, including it’s compute, storage, and big data solutions. He also works closely with the Google Cloud Platform Product Management and Product Engineering teams to help drive the direction of Google's Enterprise Cloud Platform business. Prior to Google, Thani was an enterprise architect at Oracle focused on helping Federal organizations implement SOA (Service Oriented Architecture) solutions. Thani also worked as a senior IT consultant at Booz Allen Hamilton, a lead software architect at Thomson Reuters, and a software engineer at MicroStrategy. Thani has achieved various IT certifications from organizations such as MicroStrategy, Oracle, and The Open Group (TOGAF). He holds a M.S. degree in Computer Science from Johns Hopkins University and a B.S. degree in Computer Science, Biomedical Engineering, and Electrical Engineering from Duke University.
The Goal of Study Group Learning
It is believed that students learn by doing. As opposed to being spoon-fed knowledge in lecture, study groups encourage students to go above and beyond what is being taught and to develop their own understanding of subject material. The goal of study group learning is to help students take ownership of course material; to learn to learn.
Benefits of Study Group Learning
As you can see from this list, being in a study group can be really helpful. BUT these benefits come only to those who are serious about making their group work well together and serious about learning. So, although I expect you to have fun (eh, there is no reason why you can't have a study group meeting at a pub once in a while) the study group component of your course is serious stuff.
Are you interested in joining a group but don't know how to start? Talk to your classmates or instructor.
What to do at Your First Meeting
We hope that, 10 years from now, you will look back upon this first meeting of your study group with good sentiments. We realize that not all of you will form those "college-days" relationships that TV glamorizes, but some of you will. You just never know what might happen. Here are some things that will help get started on the right track:
Check off this list as you go.
What to Do at Other Meetings
Below is a list of things you should/can do at your meetings. Suggestions in bold I would recommend you do at every meeting.
Your Role in Your Group
This page contains a section called Problems and Solutions. It's a good idea to read it so you know what can happen and so that you can recognize it when a problem arises. A lot of the problems can be prevented if you work hard not just at the math, but at making your group work. Below is a list of guidelines that, when followed, help to avoid the common pitfalls.
Common Problems and Possible Solutions
There are some problem situations in which a study group might find itself. Occasionally a group just doesn't have any interaction among its members. More frequent is the problem of one student is doing all the work, either because no one else will or because he or she doesn't trust the other group members to do a good job. These problems undermine the whole idea behind study groups and are actually detrimental to learning. Not to over-dramatize this but: BE AWARE!!! Just knowing about what can happen helps to prevent or nip in the bud serious problems.
The following list is offered to increase your awareness of potential problems as well as to offer advice on how to deal with them. We cannot stress enough how important it is to discuss your problems in your group. This is why it is so important to establish an open working environment in which you can be objective about how things are going in your group and comfortable enough to point out problems.
Problem: Lack of interaction
Possible Cause: Lack of Experience with Learning in Study Groups
Possible Cause: You Feel Coerced to Participate
Possible Cause: Physical Arrangement
Problem: Group members are participating unequally
Possible cause: Intolerance of Silence
Some people feel a strong need to fill in moments of silence with speech. In the same way that nature abhors a vacuum, some people abhor silence in conversations.
Possible Cause: Dominant Speakers Monopolize the Discussion.
Possible Cause: A Group Member Has No Interest in Speaking.
Some students feel that they learn better by listening than by talking. Others feel that speaking and helping others requires too much effort.
Problem: A Group Member is Doing All the Work
Possible Cause: Lack of Trust between Members to Work
Possible Cause: The Rest of Your Group is Slacking Off.
Possible Cause: A Group Member is the Brightest Student In the Group.
Problem: A Group Member is Being Uncooperative.
Problem: Reinforcing Misconception
It is quite easy for a group of students to mistakenly agree, for example, that when they get zero over zero they can cancel to get one. Or to misread what it means for a function to be continuous. Or to confuse "if" with "only if." Who will be around to point out these errors?
Study Groups Locations at UMass Lowell
We explore and develop the capacity for algorithms to learn and make decisions and predictions from their environment. We follow a series of complementary approaches within the group, from biologically inspired computational models to probabilistic modelling and dimensionality reduction.
GPy - An open-source framework for Gaussian Processes (GP) written in Python.
GPyOpt - An open-source library for Bayesian Optimization using GPy, written in Python.
Rodent Data Analytics (RODA) - A MATLAB suite of algorithms and a software for analysis and classification of rodents trajectory data in the Morris Water Maze.
PyKale - A PyTorch library that provides a unified pipeline-based API for knowledge-aware machine learning on graphs, images, texts, and videos to accelerate interdisciplinary research.
Sheffield Machine Learning Network
Our group coordinates the Sheffield Machine Learning Network. The aim of the network is to promote collaboration and to provide support for researchers and students who are working with or have interest in machine learning topics. It is open to anyone within the Sheffield University, and aims to foster an accessible environment. More details about the network are available.
I use Group Policy Editor to configure a lot of settings on Windows 11 or Windows 10. Recently when I tried opening it from Run prompt or directly through Control Panel, I received an error stating—Failed to open the Group Policy Object on this computer. You might not have the appropriate rights — unspecified error. If you get the same error, then here is how you can quickly fix the issue and gain back access to the Group Policy Editor.
The message was surprising because I had not changed anything that could have resulted in the error message. When I navigated to C:\Windows\System32\GroupPolicy, it had all the policies intact, but the Group Policy Editor wasn’t working. So here is what I did to resolve the issue. Make sure that your user account has Admin privileges.
There is one more way to fix this.
You can choose to delete all the files inside the Machine folder instead of renaming it. Windows will automatically recreate the required files when you relaunch the policy editor.
The reason behind Failed to Open Group Policy Object error
After going through Microsoft and Technet forums, I noticed some users reporting about the same, and one of them shared about the corruption of Registry.pol with Event ID 1096. The file stores Registry-based policy settings, which include Application Control Policies, Administrative Templates, and more. There was a log in the Event Viewer which pointed towards this corruption. The description stated:
It affirms the user’s report, and what you can do is delete the Registry.pol file available inside the Machine folder, and launch Group Policy again.
I hope this helps you resolve the error.
Now read: How to reset all Local Group Policy settings to default in Windows 11/10.
Welcome to Smithsonian Open Access, where you can download, share, and reuse millions of the Smithsonian’s images—right now, without asking. With new platforms and tools, you have easier access to more than 4.9 million 2D and 3D digital items from our collections—with many more to come. This includes images and data from across the Smithsonian’s 21 museums, nine research centers, libraries, archives, and the National Zoo.
What will you create?
Remixes by: Access Smithsonian, Amazon Web Services-Sumerian, Amy Karle, An Open Book Foundation, AstroNuts, Autodesk Tinkercad, Cesium, Chris Funk & N M Bodecker Foundation, Creative Commons, Duke University-MorphoSource, Georgetown University Maker Hub in Lauinger Library, Google Arts & Culture, The Khronos Group, MHz Foundation, Michael Joo, Matthew Putman, and James J. Williams III, Sketchfab, Smithsonian Center for Learning and Digital Access, Smithsonian Data Science Lab, Smithsonian Libraries & Museum in a Box, Wikimedia DC
The Smithsonian Open Access launch event is presented in partnership with:
Data hosting provided by AWS Public Dataset Program
The Smithsonian Open Access launch event is presented in partnership with:
Data hosting provided by AWS Public Dataset Program
In 2019, Amazon upgraded its Alexa assistant with a feature that enabled it to detect when a customer was likely frustrated — and respond with proportionately more sympathy. If a customer asked Alexa to play a song and it queued up the wrong one, for example, and then the customer said “No, Alexa” in an upset tone, Alexa might apologize — and request a clarification.
Now, the group behind one of the data sets used to train the text-to-image model Stable Diffusion wants to bring similar emotion-detecting capabilities to every developer — at no cost.
This week, LAION, the nonprofit building image and text data sets for training generative AI, including Stable Diffusion, announced the Open Empathic project. Open Empathic aims to “equip open source AI systems with empathy and emotional intelligence,” in the group’s words.
“The LAION team, with backgrounds in healthcare, education and machine learning research, saw a gap in the open source community: emotional AI was largely overlooked,” Christoph Schuhmann, a LAION co-founder, told TechCrunch via email. “Much like our concerns about non-transparent AI monopolies that led to the birth of LAION, we felt a similar urgency here.”
Through Open Empathic, LAION is recruiting volunteers to submit audio clips to a database that can be used to create AI, including chatbots and text-to-speech models, that “understands” human emotions.
“With Open Empathic, our goal is to create an AI that goes beyond understanding just words,” Schuhmann added. “We aim for it to grasp the nuances in expressions and tone shifts, making human-AI interactions more authentic and empathetic.”
LAION, an acronym for “Large-scale Artificial Intelligence Open Network,” was founded in early 2021 by Schuhmann, who’s a German high school teacher by day, and several members of a Discord server for AI enthusiasts. Funded by donations and public research grants, including from AI startup Hugging Face and Stability AI, the vendor behind Stable Diffusion, LAION’s stated mission is to democratize AI research and development resources — starting with training data.
“We’re driven by a clear mission: to harness the power of AI in ways that can genuinely benefit society,” Kari Noriy, an open source contributor to LAION and a PhD student at Bournemouth University, told TechCrunch via email. “We’re passionate about transparency and believe that the best way to shape AI is out in the open.”
Hence Open Empathic.
For the project’s initial phase, LAION has created a website that tasks volunteers with annotating YouTube clips — some pre-selected by the LAION team, others by volunteers — of an individual person speaking. For each clip, volunteers can fill out a detailed list of fields, including a transcription for the clip, an audio and video description and the person in the clip’s age, gender, accent (e.g. “British English”), arousal level (alertness — not sexual, to be clear) and valence level (“pleasantness” versus “unpleasantness”).
Other fields in the form pertain to the clip’s audio quality and the presence (or absence) of loud background noises. But the bulk focus is on the person’s emotions — or at least, the emotions that volunteers perceive them to have.
From an array of drop-down menus, volunteers can select individual — or multiple — emotions ranging from “chirpy,” “brisk” and “beguiling” to “reflective” and “engaging.” Noriy says that the idea was to solicit “rich” and “emotive” annotations while capturing expressions in a range of languages and cultures.
“We’re setting our sights on training AI models that can grasp a wide variety of languages and truly understand different cultural settings,” Noriy said. “We’re working on creating models that ‘get’ languages and cultures, using videos that show real emotions and expressions.”
Once volunteers submit a clip to LAION’s database, they can repeat the process anew — there’s no limit to the number of clips a single volunteer can annotate. LAION hopes to gather roughly 10,000 samples over the next few months, and — optimistically — between 100,000 to 1 million by next year.
“We have passionate community members who, driven by the vision of democratizing AI models and data sets, willingly contribute annotations in their free time,” Noriy said. “Their motivation is the shared dream of creating an empathic and emotionally intelligent open source AI that’s accessible to all.”
The pitfalls of emotion detection
Aside from Amazon’s attempts with Alexa, startups and tech giants alike have explored developing AI that can detect emotions — for purposes ranging from sales training to preventing drowsiness-induced accidents.
In 2016, Apple acquired Emotient, a San Diego firm working on AI algorithms that analyze facial expressions. Snatched up by Sweden-based Smart Eye last May, Affectiva — an MIT spin-out — once claimed its technology could detect anger or frustration in speech in 1.2 seconds. And speech recognition platform Nuance, which Microsoft purchased in April 2021, has demoed a product for cars that analyzes driver emotions from their facial cues.
Other players in the budding emotion detection and recognition space include Hume, HireVue and Realeyes, whose technology is being applied to gauge how certain segments of viewers respond to certain ads. Some employers are using emotion-detecting tech to evaluate potential employees by scoring them on empathy and emotional intelligence. Schools have deployed it to monitor students’ engagement in the classroom — and remotely at home. And emotion-detecting AI has been used by governments to identify “dangerous people” and tested at border control stops in the U.S., Hungary, Latvia and Greece.
The LAION team envisions, for their part, helpful, unproblematic applications of the tech across robotics, psychology, professional training, education and even gaming. Schuhmann paints a picture of robots that offer support and companionship, virtual assistants that sense when someone feels lonely or anxious and tools that aid in diagnosing psychological disorders.
It’s a techno utopia. The problem is, most emotion detection is on shaky scientific ground.
Few, if any, universal markers of emotion exist — putting the accuracy of emotion-detecting AI into question. The majority of emotion-detecting systems were built on the work of psychologist Paul Ekman, published in the ’70s. But subsequent research — including Ekman’s own — supports the common-sense notion that there’s major differences in the way people from different backgrounds express how they’re feeling.
For example, the expression supposedly universal for fear is a stereotype for a threat or anger in Malaysia. In one of his later works, Ekman suggested that American and Japanese students tend to react to violent films very differently, with Japanese students adopting “a completely different set of expressions” if someone else is in the room — particularly an authority figure.
Voices, too, cover a broad range of characteristics, including those of people with disabilities, conditions like autism and who speak in other languages and dialects such as African-American Vernacular English (AAVE). A native French speaker taking a survey in English might pause or pronounce a word with some uncertainty — which could be misconstrued by someone unfamiliar as an emotion marker.
Indeed, a big part of the problem with emotion-detecting AI is bias — implicit and explicit bias brought by the annotators whose contributions are used to train emotion-detecting models.
In a 2019 study, for instance, scientists found that labelers are more likely to annotate phrases in AAVE more toxic than their general American English equivalents. Sexual orientation and gender identity can heavily influence which words and phrases an annotator perceives as toxic as well — as can outright prejudice. Several commonly used open source image data sets have been found to contain racist, sexist and otherwise offensive labels from annotators.
The downstream effects can be quite dramatic.
Retorio, an AI hiring platform, was found to react differently to the same candidate in different outfits, such as glasses and headscarves. In a 2020 MIT study, researchers showed that face-analyzing algorithms could become biased toward certain facial expressions, like smiling — reducing their accuracy. More accurate work implies that popular emotional analysis tools tend to assign more negative emotions to Black men’s faces than white faces.
Respecting the process
So how will the LAION team combat these biases — making certain, for instance, that white people don’t outnumber Black people in the data set; that nonbinary people aren’t assigned the wrong gender; and that those with mood disorders aren’t mislabeled with emotions they didn’t intend to express?
It’s not totally clear.
Schuhmann claims the training data submission process for Open Empathic isn’t an “open door” and that LAION has systems in place to “ensure the integrity of contributions.”
“We can validate a user’s intention and consistently check for the quality of annotations,” he added.
But LAION’s previous data sets haven’t exactly been pristine.
Some analyses of LAION ~400M — a LAION image training set, which the group attempted to curate with automated tools — turned up photos depicting sexual assault, rape, hate symbols and graphic violence. LAION ~400M is also rife with bias, for example returning images of men but not women for words like “CEO” and pictures of Middle Eastern Men for “terrorist.”
Schuhmann’s placing trust in the community to serve as a check this go-around.
“We believe in the power of hobby scientists and enthusiasts from all over the world coming together and contributing to our data sets,” he said. “While we’re open and collaborative, we prioritize quality and authenticity in our data.”
As far as how any emotion-detecting AI trained on the Open Empathic data set — biased or no — is used, LAION is intent on upholding its open source philosophy — even if that means the AI might be abused.
“Using AI to understand emotions is a powerful venture, but it’s not without its challenges,” Robert Kaczmarczyk, a LAION co-founder and physician at the Technical University of Munich, said via email. “Like any tool out there, it can be used for both good and bad. Imagine if just a small group had access to advanced technology, while most of the public was in the dark. This imbalance could lead to misuse or even manipulation by the few who have control over this technology.”
Where it concerns AI, laissez faire approaches sometimes come back to bite model’s creators — as evidenced by how Stable Diffusion is now being used to create child sexual abuse material and nonconsensual deepfakes.
Certain privacy and human rights advocates, including European Digital Rights and Access Now, have called for a blanket ban on emotion recognition. The EU AI Act, the recently enacted European Union law that establishes a governance framework for AI, bars the use of emotion recognition in policing, border management, workplaces and schools. And some companies have voluntarily pulled their emotion-detecting AI, like Microsoft, in the face of public blowback.
LAION seems comfortable with the level of risk involved, though — and has faith in the open development process.
“We welcome researchers to poke around, suggest changes, and spot issues,” Kaczmarczyk said. “And just like how Wikipedia thrives on its community contributions, Open Empathic is fueled by community involvement, making sure it’s transparent and safe.”
Transparent? Sure. Safe? Time will tell.
OG0-091 study | OG0-091 approach | OG0-091 plan | OG0-091 test format | OG0-091 answers | OG0-091 certification | OG0-091 test | OG0-091 testing | OG0-091 learning | OG0-091 exam |
Killexams test Simulator
Killexams Questions and Answers
Killexams Exams List