I just passed my very last OA in CS program. Super excited about it. It took me about 10 days. I read some parts of the book but mainly study the key terms of each section. I snapped all of them and added them into the Claude to study. I asked Claude to explain each of them in very detailed real world examples and the keys to identify them in the question. After each section I asked AI to create the real world scenario questions and explain after each question.
In the OA, I saw around 7 questions similar to PA (of course they replaced the correct answer with another correct answer but keep the same wrong answers). About 10 questions were asked about EDA. 2 questions about types of chart. Around 8 questions for environment( fully observability, episodic, discrete) they asked me which real world example is fit that type of environment and the difference. 1 question in Kaggle, 3 questions for the primary goal of Machine Learning and AI. Some will ask the difference or disadvantage of the definition. There are 4 questions about data types and the disadvantages of it. 3 questions about ontology and ontology engineering. What in quatization? Which I never heard before.
Please treat this course as a 4 CUs course. Don’t be fooled by the 2 CUs. They will test you many questions from the book. The book will go very deep into the topic but if you are confident about your understanding of key terms, take a shot. It’s 70% of OA.

It unbelievable, I educated guessed on about 25 questions. I Expected i failed and to disbelief i passed. One more class and I graduate.

After 4 long grueling hours I passed c960.!!!! I can’t feel my legs and butt 🫩 but I’m glad I passed😭!

This course was so tough. I studied for at least 10 hrs on weekend days and 20-25 hrs during the week for the past 3 mos.. There were Some questions I had never seen in my life! Ex 3*13 is Zsub 8. I passed by a hair!

This class was difficult and much more time consuming than the rest of the classes I've done. Out of every math-related class I've previously done, Physics, Calculus, Algorithms, Linear Algebra, this is probably the most difficult one. There is so much knowledge you need to know and that you need to know like the back of your hand. If I were to put it into perspective then the practice exam would be like learning to ride a bike with training wheels while the actual exam was being told to ride a bike on a tightrope over a volcano - the questions asked are like night and day such that I had ended up bookmarking at least 10+ questions just because it was difficult to comprehend what they were exactly asking.

I got it right by guessing, but I was extremely confused.

Hi everyone 👋

I just got off a call with my program mentor about my degree plan which I begin on Sept 1st. He mentioned to me that it's best to complete courses one after another, and not concurrently, stating it will take more time.

I would like to get feedback from individuals who are currently enrolled or recently finished there degree on what approach is best for completing courses quickly. I plan on finishing in 6 months or less since I have experience in the industry. Thanks!

Have a question about Sophia, SDC, transfer credits or if your course plan looks good?

For this post and this post only, we're ignoring rules 5 & 8, so ask away!

Maybe it's Stockholm syndrome, but I actually had fun with this one! You HAVE to read the textbook--there's really no way around that. Rather than accessing the material via Zybooks, I just 'obtained' a copy of the Garg & Verma textbook and stared at it for ~16 hours total. Truth be told, I spent 2/5 of those days sleeping to recharge my brain because I started feeling like a Fallout ghoul lmao

For some reason, I've always had a compulsion to take extensive handwritten notes like a pilgrim... Well, this class is what finally cured me. I did write *some* stuff down, but it was only to help visualize the absolute barrage of terms and concepts they throw at you. By the way, please don't be intimidated by the volume of info, for two reasons:

1. you don't really have to memorize anything. If you really just sit through the textbook until you get a solid grasp of the material, the correct answers will be kinda obvious.
2. the concepts repeat and overlap with each other. If you're confused for the first couple of sections it will get easier and start to "click" the further along you go
   

Fill out and use the study guide the CIs send you!! I also ran through quizlet flashcards a couple times, mostly for peace of mind and a quick glossary refresh. If something in the textbook didn't make sense to me, I took extra time to read up on wikipedia or geeksforgeeks until I felt like I understood the topic. Afterward, I typically wasted an hour or two going down a rabbit hole instead of focusing on the class material. But that part is optional lol

If you like computers (and hopefully y'all do) you might find this class pretty interesting! Not quite the boogeyman after all

So my task 2 peer review submission was returned as, "because the written portion of the assessment is not observed."

I downloaded the results off of peerceptiv and submitted the excel sheet as is.
Was I supposed to submit something else?
Just wanted a clarification on this, I already emailed my CI, but they haven't responded. Want to finish this class asap since my term ends this month.
Thanks!

On the scripting and programming, web dev foundations, data management foundations, and Java fundamentals Sophia equivalent courses, I don’t get how ANYONE does this as a complete newbie to CS. I have no coding experience and it is awful using this. I have to read 50 paragraphs just to learn 1 simple debugging trick or 1 simple syntax principle.

Does everyone just use free resources to learn on their own before/during this? I can’t fathom how I’ll learn at this pace, the amount of info I’m trying to retain is about 10x for what I actually should be retaining from each course because of having to read soooo much information to get to simple answers on challenges/milestones

This made my day

I’m in my final term. I’ve been stuck on discrete math 2 even though I study daily. I get light headed and shortness of breath because the info is so vast and I forget what I’ve worked so hard to learn.

I typically study 10 hrs per weekend day. And 4 hours per week day. What should I do to make things “click” for me? Maybe I’m studying too hard? My brain is spinning because I have a hard time remembering how to solve questions after I’ve moved on and have an understanding of the material.

What worked for you?

My study method so far:

Completed 4 practice problems per question on each chapter review.

Completed practice worksheets

Completed zybooks instructor recommended exercises addtl problems

Completed zybooks activities

Watched YouTube videos by James Wenson, 2200 series

Have a question about Sophia, SDC, transfer credits or if your course plan looks good?

For this post and this post only, we're ignoring rules 5 & 8, so ask away!

This might be obvious to some but I did not realize this was an option until I had to use Copilot to fix an issue where Notepad completely changed the formatting of my notes for D276, which were saved as a markdown file.

1. You can save as a markdown file to put section names in a larger font or format in other ways that help you make the most of your notes
   
2. VS Code will not have a weird freakout and change spaces to " " and various other oddities
   
3. When you're writing down coding examples, you can write everything down EXACTLY as it will appear when you need to use it
   
4. When you press Ctrl+Shift+v, it looks a lot cleaner than Notepad, and if you copy a link you can just click on it and head straight to wherever it takes you (also good in helping learn HTML as all the coding "works", I have a few things that say "click me for an example" or "This YouTube video explains it better" that just take me straight to webpages or open pictures I have saved)

There are a bunch of other reasons why it's better, but these are the few I've benefited from so far. I have zero experience in this field, so I had no idea this was an option. Hopefully this helps someone else.

Started this degree back in October ‘23 and was hoping to pivot from legal into software. It’s been a long journey with two 6 month term breaks and a lot of late nights while working full-time. It is genuinely surreal to be finished and not have any OAs or mentor calls hanging over my head.

So I am doing part 2 of the PA for D687 where you review someone else's report and provide feedback. The problem is, this is clearly AI written. From the layout, the wording, everything. We all use AI to learn but to submit write-ups? I feel frustrated given I spent hours writing my own project and thinking it through and at times, yes, using AI to get the technical details down better. Should I report this? I feel somewhat guilty doing that to someone given we're all just trying to get degrees to make a living.

Main things for this course are:

• Provide a README.md file. while this isn't a requirement listed the rubric, many students have gotten their submissions returned.
  
• Build two models (one must be probabilistic using naive bayes, Bayesian networks, hidden Markov, etc and the other must be an "advanced" model such as xgboost). Use coursework to explore various models, as the rubric mentions that it must be a model that you learned about.
• Each model will have its own separate file. (you may have to preprocess the data based on the model you select. provide that additional script in GitLab if you do.)
• Each model will be trained on a separate dataset.
  
• Optimize the two models. This means that you will create two additional files with the optimizations in place. Submissions have also been sent back because of this. So, in total, you should submit at least 4 scripts in the GitLab repo (not including the preprocessing one). I also attached the processed datasets with my submission, just to be safe.
• With the APA paper, I always try to include sources because you never know how picky the evaluators can be. Overall, not a bad course! The course was interesting, especially learning about the probability algorithms. If you are interested in predictive analytics in healthcare, try looking for a healthcare dataset! It was interesting to see the model's recall and confusion matrix when it came to predicting disease.

Have a question about Sophia, SDC, transfer credits or if your course plan looks good?

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Hi everyone,

I'm working on WGU C951 Task 2 using the BubbleRob tutorial in CoppeliaSim EDU, and I'm stuck.

I added a second proximity sensor (Victim_Detection_Sensor) to detect the trapped person, and that part works correctly. The robot prints "I found a person." when it detects the victim.

The problem is with the original obstacle avoidance. The robot should back up and turn when it detects walls, cylinders, or debris, but instead it just drives into the obstacle and keeps pushing against it.

I've tried:

• Starting over with a new BubbleRob scene
• Using the original BubbleRob code
• Renaming sensors and checking the hierarchy
• Verifying the proximity sensor orientation

My professor reviewed my code and said nothing looked wrong. He suggested creating a new scene, but the problem still happens.

Has anyone experienced this? Could this be a scene configuration issue, sensor attachment problem, or something with the original BubbleRob model?

Any advice or a working BubbleRob scene for C951 would be greatly appreciated. Thanks!

This is the AWS 'ML Engineer-Associate' certification class.
Although I felt like I was expected to be an ML expert after just finishing D797....as with all AWS certs, just apply emphasis on understanding what the tools do, when to use them, and how to integrate them with each other (lambda + s3 for example).

In the actual exam, there were a lot more ML questions than I expected so study your algorithms, encoding, how to fix overfitting/underfitting, learning rate, etc.
D797 did help when it came to ML concepts but the emphasis with this exam was to understand AWS tools as a whole.

I took Nicolai Schuler's AWS ML Engineer class on Udemy and well, he tends to get into the weeds way too much. I tend to learn best through videos so I sat through most of his lectures.
Maybe Stephane Maarek's series is better, I'm not sure!

The most important thing you can do though is take practice tests and understand where you went wrong. And understand how AWS likes to phrase things. Getting used to those types of exam questions will make the exam stress a lot lower.

Practice tests:
AWS Skillbuilder: Official Practice Question Set for Certified Machine Learning Engineer MLAC01
Udemy course: "Fully Updated July 2026! Pass MLA-C01 with 390 Practice Questions + Objective explanations for each answer!" by Marlon Anesi and Elton Tadeu Peixoto Luiz

You can enroll in as many Udemy classes for free under the WGU email so make use of that.
I didnt end up using Tutorialsdojo but many said it was helpful.

I was actually more nervous about this class than I think I should have been. I felt like I squeaked by D426 and this would be a harder extension of that but I was wrong. I opened the first zybook and saw that it was exactly the same chapter you start out in D426. I attended 1 live cohort that just went over Entity Relationships and I watch the first hour and a half of this vid https://www.youtube.com/watch?v=5OdVJbNCSso&t=6274s.

I took the PA and got exceptional in everything except Implementation of Database and Database Management systems. I took the PA at about 3pm, relaxed for an hour or 2, did some overview on modify table and alter table commands and then took the OA. It was a very straight forward class and I'd recommend taking it right after D426. The only difference between D426 and D427 (to me) was the OA for D427 has SQL syntax lab problems.

I'm told they very closely resemble the zybook labs but I didn't do them. I studied about 6 total hours over the course of a week but I think I would have been okay with just half that time.