Download group 2 re exam 2020 answer key

Hello all. I just passed the PMP exam last week with AT/T/T/T/AT a rough pass and not what I was aiming for but it’s a pass nonetheless.
A little background on me:
I am a former bootcamp software developer (AS in compsci with honours, BA in poli sci, MA in Communications). For the last year I have been working as a Product Manager at a poorly managed ed tech company. I have no experience with formal project management and sought out the exam and skillset because:
a. It was a gate to further advancement in the field.
b. I REALLY needed to instill some semblance of order in my workplace.
Here are my resources. Note: None of this is an endorsement just what I used YMMV. If people ask I can post specific links but not sure if that’s allowed first post.
To get my pdu’s, and prep for what to experience I bought the INFOSEC PMP Bootcamp Prep. This includes your PMP membership, a print copy of the PMBOK, a free test voucher and a FREE test retake voucher if you fail AND you attended at least 90% of the bootcamp AND you score at least 90% on their mock exam. This cost around $2500. Subsidized nicely by my stimulus check. $2500
I downloaded a copy of PMBOK 6th edition online onto my tablet (it was risky and I hope I don’t have some crazy malware now). FREE
I bought the PMP PrepCast PM Formulas PDF and Cheat sheet along with the 105 sample questions(~$50)
I downloaded the following Apps: PMP EXAM 2020 ($12, iPhone), Professional Prep PMP ($19 monthly, iPad), PMP Prep(iPad) I only had each subscription for less than a month but more about that below.
The Vargas Holy Grail of videos (found out about that one here at pmp). FREE
Vargas’ free downloadable charts of the Process Areas. FREE
Project Management Videos by Edward Shehab on Youtube. FREE
A few youtube videos by Praizion. FREE
With my resources outlined I will discuss my process as well and in the following section explain tweaks to that process that I recommend:
My Process
I paid for my bootcamp on September 1 (this year) for a start date of September 28 to an end date of October 2. INFOSEC recommends taking the exam on the Friday after the bootcamp. I obviously didn’t do this. I also was starting from literal zero as well.
I received my materials (the PMBOK textbook) around September 6th and began my PDU prestudy on September 10th. I rushed through these videos in order to get to the end so I could qualify for my bootcamp. INFOSEC has a decent question bank which I used about 400 questions on daily reviews. They are a bit on the easy side though but great for reinforcing basic conceptual knowledge.
A week before the bootcamp on September 28th I watched the Vargas video and then began using the prestudy videos to go through each of the Processes outlining the Purpose, Key Benefit, ITTOs as well as key terms. I did this by going through each Process in its entirety and NOT by Knowledge Area.
The Vargas video was instrumental in opening the door to understanding the interrelationships of each process which was key to me passing.
I did the bootcamp which was just an overview of the same content in the PDU prestudy with a group of other people. We had the opportunity to explain concepts to each other and do a few questions. All in all it was a nice refresher if not a bit pricey.
I then worked through the PMBOK and the toughest area/longest slog was obviously Planning. Once Planning was done I then consolidated the rest of the processes by knowledge area and drilled down that way.
At around the same time I started the bootcamp- 28 September I began using the apps to go over questions. The apps were useful that they tracked incorrect questions and enabled me to return to them.
Whenever I got a question wrong I went and looked it up in the book and I added it to my “Punishment” log. My personal rule was to review the punishment log three times a day.
Following the bootcamp. I set the exam to be about three weeks later- the 23rd of October as I intended to really drill down and master everything in hopes of running the tables with AT’s (yeah right).
I took several quizzes a day even before I finished prepping all the knowledge areas. This was helpful as it forced me to try and think ahead on some questions and bridge the relationships between the individual Processes. Most quizzes I took were 12 questions. I took several 20 question quizzes and as I moved further away from the first two knowledge areas I covered (Integration and Project Stakeholder management) I incorporated them into my quizzes so I was staying fresh on that content. I kept adding in a few questions from knowledge areas I had already covered to keep warm on them.
I did about thirty of the math questions. And I used the online Pearson whiteboard to practice doing them so I wouldn’t be thrown off by using it in the exam.
The Exam (23 October)
The Exam focused heavily on making real world decisions based on the PMBOK strategies for handling given situations. To explain it better, many of the answers could NOT be directly found in the book- rather you had to use the knowledge from the book and rank the decisions for the MOST appropriate course of action and go with it.
85% of the questions had two answers that could have been the answer. I was honestly shocked and horrified by this. So please if anything- think situational (more on that later).
I took the exam at home. I had to take a picture of my workspace, disconnect my second monitor, you are recorded audio and video through the entire thing so try not to mutter curses under your breath when you get a tough ETC question like I did. When you start you get a single time and even though you can take a 10 minute break whereby the exam timer is paused after around 90 questions, beware as the sections are not individually timed. I made sure to clarify with the invigilator on the deal with the timer as I would have spent the entire time trying to refine answers and been left with little to nothing for the second portion!
The timer stops during the break but I was already heated from the question quality so I opted to forge ahead as I wasn’t sure what I’d be allowed to do in the break (you cannot even have a drink with you during the exam).
Refinements to my Process
Before you open the PMBOK
Start with Vargas. Download his visual sheets. Refer to them frequently. Make sure you understand how it all comes together. After watching his video and digesting it. Write an essay as if you were doing a complex job application or school course review on how you think it all works to test if you truly understand it. Once you do. Good. Time to move on to the Process Groups and Knowledge Areas.
Jump into the Shehab videos and bust open your PMBOK. You are going to knock out the Process Groups by the sub-process group which will keep your thought process linear and related and prevent confusion. Watch one Shehab video on a topic. Read it through in the PMBOK then do a writeup of the entire Process. Rinse and repeat until you have finished Planning.
Once you are done with Planning you can then deal with the rest of the Processes by Knowledge Area so you understand the relationship between Execution and Monitoring and Controlling.
As you move through, take note of three things and create a separate document to track them: Project Documents, Tools and Technique Types, and PMP Plan Areas. You want to group your types together such as Data gathering tools vs Data analysis tools, Registers and Plan Materials. You want to understand what each is and how it applies to the big picture- doing this will make the situational aspects of the exam easier. Effectively you should be able to see a particular problem that comes up, mentally place that problem within a particular process (if that info is not readily given) then apply the requisite part of the process to resolve the issue presented to you.
You should be able to knock out 1-2 Process Areas in 2-3 hours of study on your basic knowledge pass. Take 20-30 minutes after your writeup time to read what you just wrote then do a 10 question quiz. Take note of what you got wrong- add it to your punishment log. Review that before bed and whenever you can.
Try to do 3-4 math questions a day even if you’re not at the math portion yet. You want to lock down the process and marry it to the concepts when you get to them. Write down the equations by hand and practice using the whiteboard as that is really good muscle memory and will add to your speed during the exam.
After you’ve finished all the process areas. Now go back and add additional notes as you test and make sure to jot down a situational hypo or two per day as you review Process areas.
Consider hypos like: I’m being asked to start the project without a key stakeholder being available to sign off on the project charter. What do?
Or
We want to review the construction processes of a contractor for quality what can we use to do this?
I think the bootcamp and ANY prep to PMBOK has to include time set aside to cover application of the knowledge you have learned to real world scenarios. You need to understand the multiple ways you can handle an issue as well as the pro’s and con’s of each of those ways and what would be considered to be the best choice given a particular fact pattern.
Godspeed and Good luck to all!

Tl;dr: A major which can be completed really quickly, but really shines with contextual/applicable knowledge.
Hello! Recent Statistics graduate here (Winter 2020), saw this thread here and thought i'd contribute my 2 cents. I'm currently a Political Science PhD student at North Campus, who does data science-y consulting work for the HR department of a Fortune 500 tech firm on the side, so I'll provide both the 'academic' and 'industry' advice on the major. Also, I was a transfer to UCLA, but I came from a Global Studies major, so my own experience is sort of all over the place.
General college advice is to decide what you want out of college: it doesn't have to be very specific, but it would provide you a general direction for your time at UCLA. This is a massive university, in a massive city, with massive amounts of things to do – don't accidentally drown yourself in everything by following crowds.
College life isn't exclusively about 'career stuff' or 'party stuff' (go try both, seriously) , and if you really want to nail an awesome job out of graduation and hold on to it, competency and proactivity reign supreme.You can stuff your resume and stack it to the moon, but if your competencies aren't there, it's going to bite you in butt even if you pass the interviews. And in a hub city like LA, if you're good at what you do and asking around, your professional network will expand pretty rapidly.
Didn't really have much regrets as an undergrad other than that I didn't get to go on a date before covid took a sledgehammer to my plans of 'free-and-easy' final quarters at UCLA. Dammit covid.
Note: All classes taken here was B.C (before covid). The virtual class experiences After Disaster (A.D) may be different.
Prereqs/Lower Divs
STAT 10 - That first Stats class you'll take. Or not, if you skipped it with AP.
If you're not sure if Stats is your thing, but 'DATA SCIENZE' sounds amazing to you, go take this class. It's probably the easiest Stats class, but it teaches the intuition that would form the foundation of all the big, bad, scary stuff later down the road. If by the end of the class, you like the particular way of thinking (i.e. it's not a pain in the tush to figure out 'what' those concepts are), then you should be solid to check out the bigger, badder, scarier stuff.
Math 31A/31B/32A/32B/33A - The Basic Calculus Stuff
The introduction to woe and agony for STEM students. Doesn't matter where you take it, and you probably get pretty solid general advice on who to take and how to survive it. That being said, here's some major specific advice: Linear Algebra is going to be big daddy. Knowing this stuff by the back of your hand will make your quality of life so so much better. You'll see it everywhere later down the road. Neural Networks? Linear Algebra. Least Squares Regression? Linear Algebra. If you barely scraped through knowing Linear Algebra, it would be handy to brush up on the conceptual understanding of it. You can survive the major with 'passing' levels of Linear Algebra, but if you want to thrive, you can't run from being competent at it.
STAT 20 - OH GAWD NO?
Introduction to Statistical Programming with R
Search this sub and you'll find some pretty wild stuff being said about this class recently (2019 onwards). Disclaimer, I took this class in 2018, and it was pretty straightforward, but clearly the intensity rocketed after I took it. Also I did not get an A then either because I decided to just cruise through it. That being said, I've later taken upper div classes TA-ed by the 'notorious' TA of Stat 20. He's not evil/sadistic/sacrifice-undergrad-souls-to-the-regression-gods, but definitely on the stricter side of things.
Remember how I said I cruised through it? I got ripped a new butthole at STAT 102A, because I cruised through it. Again, I didn't take the 'new' version of STAT 20, but looking back, it would have been a better idea to get my shit together then.
You're not going to cakewalk through this class, but don't get depressed taking this class either – give it your best shot and just let it be. If you're taking this class just for GPA, you might qualify for a lifetime sponsorship of stress balls.
If you made it to this point, and decide that you want the remainder of your undergrad to feel like the mix of the lower div prereqs, then welcome to the Statistics major! We have cookies at the end of the road here.
Upper Divs
STAT 100A - Introduction to Probability
I took it with Sanchez, everything up to the first midterm was pretty much basic statistical logic and statistical knowledge review. Then buckle up buttercup, because you're in for a wild ride. You'll get very quickly acquainted to her 7-min-before-end-of-lecture quizzes (yes, me and my friend timed it), which effectively serves as a check if you were listening in class. Don't sweat it if it looks like greek to you, the actual ding on your overall grade for bombing these isn't massive. But definitely go back to look through her slide printouts once you're done, and go to her office hours if you're lost. I took this class in person, pre-covid, but her office hours were 1-person-at-a-time, and the queue can grow pretty fast, so don't leave it till the last minute unless you want to wait outside her office till 6pm. She also has a habit of calling people to answer questions mid-lecture, so don't spend too much time daydreaming, and try to be on the ball. Again, it's ok if you slip here and there, but that's her style of ensuring students don't end up hopelessly lost by the end of the quarter.
Getting really familiar with her homework questions will be key towards your grade. Her final was effectively a race against time, if you're familiar with the 'style' of the questions, you'll be able to nail what formula to use when. Also show up early for the midterms/final, she arranges student seating manually in the room.
STAT 100B - Introduction to Mathematical Statistics
Took this with Sanchez again, so it was a rinse and repeat of everything I wrote for 100A. The choices I had then was either her or Christou (who I took for 100C), they both have their pros and cons. Sanchez's powerpoint slides are always handy, even waaaay after you finish the class. Christou will be good if you're planning to go to grad school, because the way he teaches is more theoretical than applied. Think "the intuitions to " vs "the formulas for ".
You're likely to get a higher grade with Christou, but his abstract nature of teaching means that there is a non-zero chance you walk away from the class learning zilch.
STAT 100C - Linear Models
I took this class with Christou, apparently it was his favorite class to teach. The learning curve for this class is steep, and you're almost guaranteed to be lost by the time week 10 rolls around. To quote a Stats major alum that's now a PhD student in the Stats department – "Christou's exams are learning experiences". Don't sweat it, just take note of EVERYTHING he writes on the board in class, and then work it out later slowly. if you don't get it then, don't self-flagellate, because most people don't either. If you do, good for you, take it as a breather.
His homework assignments are designed to be hard, midterms are to be harder, and his finals may make you question your intelligence. Just write SOMETHING. You get credit for showing ANY thought process, and you'll be surprised at how generous the curve can get. I answered only 2 questions completely in his final, I got an A.
STAT 101A/B/C - Regression Models/Applied Statistics Classes
I grouped these 3 classes together because they're pretty much the 'applied' part of the entire major. All the funky theory you learn into 100A-C and sick R skills you learn in 102A-C gets put into practice here. All 3 classes will have you build predictive models and analyze their outputs, and the only differences across A/B/C would be the context and complexity of the models you're building. 101A was just basic OLS regression models, 101B was more or less the same stuff but with experimental design thrown in, and 101C covers the other major regression methods which cumulated in a final project which you're allowed to use any technique to build a predictive model (at least when I took it).
I took 101A/B with Almohalwas, and 101C with Gould. Almo's lectures has many tangents (which could be interesting depending if that's your kind of thing), but his exams are very standardized. He doesn't believe in drilling practice exams because he thinks rote memorization sucks, so don't expect to have too much material to practice off. The upside is that his exams follow a particular logic, so it's not that hard to prepare – make sure you know how to manually calculate all the numbers from the summary() output in R for linear models. He'd provide 1-2 numbers, and you'll have to work out the rest (i.e. he provides df and sigma^2, and you have to figure out R^2, SSE etc.). Also note what he keeps emphasizing in class, that usually pops up as qualitative questions in his exam. Depending on his grading policy for that quarter, his final may have a Hail Mary card – if you score a 100% you get an A (the max score is 120% including extra credit questions). It saved me once.
As for the project component of Almo's classes, don't sweat the ranking of prediction accuracies. If you're reasonably high (if you have a 0.93, the top is 0.95 and the average is 0.92, you'll be fineeeee), there's more to gain from making a better report than a better model. If you submit a crappy report, regardless of how good your predictions are, they're junked. Make sure your report justifies why you made certain decisions, and if your results somehow are invalid, it's fine, just explain what could have caused it to be invalid. He takes these projects as a microcosm of real life statistics work, so it's ok to submit projects that 'failed' and don't have statistical significance, as long as you know what went wrong.
I took 101C with Gould, and it was a more project-based approach class. If you knew your 100A-C, 101A-B, 102A-B stuff going into this class, you wouldn't sweat the midterm. If not, his lectures till then are pretty much a refresheother-point-of-view of the stuff you've learnt. The final for the class was a predictive project similar to Almo's, but we were allowed to use any method we wanted. And he was serious about 'any', my group submitted some weird, frankensteined model and it was accepted. Gould's class is great to take juuuust before you hit that job interview, because you get a quick review of all the important applied stuff.
In general, do the projects for this class series early, and the TAs are great for homework/project advice. Go to discussion and you'll know why.
STAT 102A - Introduction to Computational Statistics
The big boy R class. You'll learn all the major packages for use in R, and you'll learn how R...works(?), for lack of a better term. Essentially, you should be able to do almost anything in R after this class. I took this class with Miles Chen, and it was another rather straightforward class. His homework assignments are very well-written, and if you're lost in class, the homework would go a long way to help understand what was going on, because the comments in between the code guide you. There's one homework assignment that was wild, and that was to code monopoly in R (when I took it, it probably changes around, but the concept is about the same). It is very tedious, and it will really test your understanding of how R works, so start early. Otherwise, go to lecture/discussion/office hours, and it should be another done-and-dusted class.
STAT 102B - Introduction to Computation and Optimization in Statistics
You know all the fun Neural Network/Artificial Intelligence/Machine Learning stuff you hear? It starts here. Brush up your Linear Algebra if it's been a while since you've touched it (and if it's been a while since 100B/C), because it's back. I took it with Miles, so again, everything said in 102A applies here, sans the curveball project. I took this one in Summer, and the workload didn't make me grey.
STAT 102C - Introduction to Monte Carlo Methods
Here's where the ML stuff gets cranked up a notch. Took this class with Zhou Qing, whose exams were effectively Christou-lite. He taught it very theory heavy (as compared to Miles, or so I've heard), so it took some effort to 'see' how the content taught translates into real life. That being said, it's pretty standard fare, if you're not gunning for a 4.0. You might get that A, but if you're obsessing over acing every test, you're not going to be a happy child. His final had 2 components, 1 in class, 1 take home. The in-class final is pretty standard fare, but you get 24h for his take home final. You know how all the memes in the post-covid era talk about 24h take home finals taking a while to do? It applies here too, pre-covid, so get started and cracking ASAP once he opens the final on CCLE.
STAT 140/141SL - The Capstone Consulting Classes
You made it. You're here. This is it. Once you get to STAT 140/141, you're effectively done with the major. This class is a review and cumulation of everything taught in the major, and you put into practice by 'consulting' for a project. This project can be either provided by faculty, external organizations, or one that you find for yourself. My group did a project for an external organization, and it was quite a wild trip, and you can get some very funky projects in this class (there was one that was 'analyzing eyebrow shapes' for UCLA Health when I took it).
2 Major Electives
Choose the two that will shine your skills. I chose to take Applied Sampling with Cochran, and Geostatistics with Christou, because they would give me alot of mileage in my research work as a grad student. There's no hard and fast rule on which 2 you should choose in the list, but I'd suggesting taking them nearer to graduation, because they'd be the ones you'll probably polish your resume with.
Personal Remarks and Closing Notes
I had a lot of fun as a Stats major, despite failing math throughout high school, and not even being a STEM major at the very start of college. The skills taught in this major are a great stepping stone, but that is what it is, a stepping stone. If you use the stepping stone as the top step of a podium, you're not going very far and that's it; if not, you'd be well-equipped to poke around into the realm of Data Science and develop your skills into subject matter expertise. For those looking into doing graduate studies, take the extra time given by this (relatively) short major to gain better theoretical skills (i.e. Real Analysis, Numerical Methods if you're thinking of doing a Stats PhD), or go get good at SQL/Python if you're planning to go into industry.
4 years can pass really quick, but it's also more than enough time to establish a stable foundation for your future careelife. And finally, it's cliched as heck, but GPA really doesn't matter \that* much. A Statistics degree isn't going to be an easy time, but you can make it a rewarding time.*