# Amazon ML Summer School 2026 | SOP submission| Previous Year Questions

https://www.youtube.com/watch?v=mG-w-CfoBiI

[00:00] Today we'll be talking about the Amazon ML Summer School which is your chance to learn machine learning from Amazon scientists, win some amazing swag, and also get interview opportunities for applied scientist role, full-time and internship opportunities with Amazon.
[00:14] Most of the people get a direct test link in this case.
[00:15] So, it's a great opportunity and the registrations are open.
[00:19] So, check out the link below.
[00:21] Before starting the video, I just have one request.
[00:23] Please like and subscribe to my channel because I have been making content regularly and I have included a lot of details in this video.
[00:33] You will not find such a detailed video anywhere on the internet.
[00:35] So, do like and subscribe before starting.
[00:41] First of all, let's start with why you should apply.
[00:44] This is your chance to secure a spot in the Amazon ML Summer School program, which is basically the program where they will teach you machine learning.
[00:51] Also, you will get some amazing swags and you'll get networking opportunities with Amazon scientists.
[00:56] So, you can talk to them.
[00:58] They will mentor you.
[01:00] acknowledgement letter or a certificate.
[01:02] where it states that, you know, you have cleared this Amazon ML Summer School, so you can add it to your resume.
[01:09] And the best part is the fast-track interview opportunities.
[01:10] A lot of people through this program, although it's not mentioned, but a lot of people have gotten opportunities for applied scientist internships and applied scientist full-time jobs through this Amazon ML Summer School program.
[01:21] So, do apply because this is your chance to get a job at Amazon.
[01:28] Let's talk about the important details.
[01:30] The registration deadline is started on 1st June and it ends on 14th June 2026.
[01:36] SOP submission is 14th June.
[01:38] What is SOP?
[01:40] We'll talk about later.
[01:40] And it's just 4:00 p.m. to 8:00 p.m.
[01:43] So, it's just 4-hour window where you have to submit your SOP.
[01:44] And then there's a selection test on 28th of June, which will start on 28th June and end on 28th July.
[01:54] So, you can give it anytime between 28th of June and 28th of July.
[01:56] And finally, the program, the ML Summer School program, which is which start on
[02:01] 4th July and end and run till 26th of July.
[02:05] So, whether or not you receive the selection test link will be based on the registration deadline and the SOP submission.
[02:10] So, first of all, let's talk about the eligibility criteria.
[02:12] Engineering students enrolled in bachelor's, master's, PhD degree from any institute in India are expected to graduate in 2027 and 2028 are eligible to enroll.
[02:22] So, if you're graduating in 2027 or 2028, bachelor's, master's, PhD, everyone is eligible.
[02:27] So, it's great.
[02:29] There is no gender-based, no college-based shortlisting, so everyone can register.
[02:33] But, only if you're in an Indian institute.
[02:36] If you're studying outside India, then you are not eligible.
[02:38] Registration Also, this is an important thing.
[02:40] Shortlisting will be done based on your resume, so make sure that you submit your resume and try to add some ML skills in your resume because they want people who have some basic knowledge of ML at least to get started.
[02:52] So, your resume will matter a lot, so make sure you polish it before registering.
[02:59] At the same time, a lot of people say that, "You know what, Amisha, I do not
[03:02] have AI skills.
[03:02] Where do I learn?
[03:05] What do I add in my resume?
[03:07] So, real talk, AI is everywhere right now, and if you're not learning it, you're already falling behind.
[03:10] But, don't stress.
[03:12] I've got you covered.
[03:12] Let me show you where to start.
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[03:20] They already assume you have a PhD.
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[03:54] If you want to learn AI properly, the structured, practical, career-focused way, Simply Learn is where you go.
[04:00] It's very easy.
[04:02] Just Google Simply Learn AI courses.
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[04:21] So, do check out Simply Learn.
[04:22] The link will be in the description.
[04:24] And let me know if you have any questions.
[04:26] So, post the registration, what happens?
[04:28] So, post the registration, you will have the SOP submission.
[04:30] What is SOP?
[04:32] SOP is the statement of purpose.
[04:34] So, it's basically a 500 words of essay which you will write.
[04:36] And for this you have just 4 hours.
[04:39] So, 14th June is the date when you have to submit it.
[04:42] So, I will tell you that you start writing it as soon as you can.
[04:44] Start writing it today.
[04:46] So, framework will be communicated.
[04:48] So, they will give you more details.
[04:50] So, it will basically be an essay where you say why you want to be selected, why they should select you.
[04:56] You convince them, right?
[04:58] Because they don't want to just select anyone at random.
[05:00] So, I will be giving you tips on how to write a
[05:03] Great SOP. So, let's get there.
[05:05] So, this is these are personal tips.
[05:07] By the way, this is the first time they have introduced such a round.
[05:08] So, there is no previous sample that you can see.
[05:10] But I will just give you some generic advice based on my experience of seeing other people who have gotten selected.
[05:17] I also had to write a statement of purpose when I got selected for my Flipkart Runway program.
[05:22] So, based on that I'll give you some tips.
[05:24] So, make sure it's personal and not copy-pasted.
[05:28] You can use AI to see what all to write, what all to include, but do not copy-paste because it should feel personal.
[05:33] Everyone can copy-paste, right?
[05:35] So, why should they select you?
[05:36] So, you should add introduction and goals.
[05:38] Basically, you should mention how long you've been interested in machine learning or AI.
[05:41] You should mention which college you're from, if your college is good, or you can skip it.
[05:45] You can mention you know, you're a third-year student, fourth-year student, whatever.
[05:47] You can mention that.
[05:49] You know, give some brief intro and then why you're interested.
[05:53] And what are your goals for this program?
[05:54] Then you can mention your academic foundations.
[05:57] If you have any relevant course course.
[05:58] If you have any relevant coursework like probability, statistics, linear algebra.
[06:04] data structure, algorithms, so mention that you have a good base in this.
[06:08] Why? Because machine learning requires probability, stats, linear algebra.
[06:12] And also the next selection test, if you get selected based on your SOP, will be a selection test where they will ask you all of this probability, stats, linear algebra, and data structure algorithms.
[06:20] So do mention that you have a good base in them.
[06:21] Mention any relevant coursework you have done.
[06:23] If you've gotten some good grades, any projects you've made, etc.
[06:26] Highlight any key certifications from like Coursera, any open courseware, um or any academic achievements.
[06:32] Let's say if you've got good GPA, you can mention that.
[06:36] Any Dean's Award if you've gotten.
[06:40] Strong mathematical ML base you have.
[06:42] So basically, they need to know they will be teaching machine learning.
[06:45] If you have a good starting point, you know the math, you know some basic ML, that would be great.
[06:50] Mention if you have any projects and hands-on experience like one to key projects where you actually built or implemented a machine learning or deep learning model.
[06:57] So again, you can ask me, "Mommy, sir, if I already knew it, then what's the point?"
[07:00] It's basically it's good if you have some base, right?
[07:03] They don't want
[07:05] to teach someone who just has no base.
[07:07] And also, since they might also hire for internships for this, so they need people who have some experience.
[07:14] And if you don't have experience in machine learning, you can mention something related.
[07:17] If you have something related to AI or even if not that, maybe DSA, maybe just programming in general.
[07:22] So whatever you have done, even if you attended a workshop, just mention it.
[07:26] It should feel personal.
[07:28] And try to show them why you're the best candidate for this.
[07:31] Or mention why what you would do if you get selected.
[07:35] So coming to the last point, why Amazon ML Summer School?
[07:37] Tell why you're passionate and how you will use this.
[07:39] So you can mention that you know, ever since I have got to know about AI, you know, I have been interested and if given a chance I would use this to maybe solve some Amazon related problems.
[07:49] You can mention you have you are an Amazon customer or you can mention that you know, you will actually solve some real world problems using machine learning.
[07:57] So be creative, think out loud, but make it personal.
[07:59] It should not be just another copy pasted statement of purpose, okay?
[08:03] I'm just giving you ideas.
[08:04] I wouldn't actually tell you what
[08:06] to write, but these are the tips which will definitely help you.
[08:10] Coming to the next part, the selection test.
[08:11] So if you clear the registration and the SOP submission, then there will be selection test.
[08:16] I'm assuming a lot of people will get get the selection test because till last year everyone got it irrespective of their resume.
[08:22] This year they have mentioned they will be shortlisting, but I hope they will give it to at least 70-80% of the people.
[08:27] So what is the selection test?
[08:29] It will be 60-minute selection test comprising of two sections.
[08:31] The first part is going to be MCQs.
[08:34] So 20 MCQs on basic ML concepts, probability, statistics, linear algebra.
[08:37] Then part B will be two programming questions.
[08:40] Data structure and algorithm based programming questions to assess coding and problem-solving skills.
[08:44] Okay, so what questions will they ask?
[08:47] That I'm mentioning, but also you need to make sure that your speed is good because since it's MCQs, so one point is you know, getting the MCQs right, the other point is getting them right as fast as you can.
[08:59] So make sure that you click the answers as fast as you can and obviously the first and the most important thing is getting them right.
[09:06] The second thing is to
[09:08] also do it fast.
[09:09] And as for the programming questions, make sure that your code runs for all the test cases you can think of.
[09:15] There can be hidden test cases which they run later on.
[09:17] So you might think that you know, okay, my code is working for these five test cases, but there will be more test cases.
[09:23] So make sure that your code runs for all the test cases and if not all, at least it runs for a few test cases.
[09:28] So you write some brute force something, but it will be best if you can make it run for everything.
[09:34] What topics will be covered based on previous year questions?
[09:35] Probability, conditional probability, base theorem, independent events, this is something you should know for sure.
[09:41] Statistics, mean, median, mode, variance, standard deviation, central limit theorem, and correlation.
[09:47] Linear algebra, matrices, matrix multiplication, rank, eigenvalues, dot product, basically everything they are just taught in linear algebra course in the college.
[09:55] Python basics, sometimes they ask list, dictionary, list comprehension, NumPy basics, time complexity, maybe they will give you a piece of code in Python and tell the time complexity.
[10:06] So, they are not This is not always asked, but sometimes can be asked.
[10:09] Machine learning basics will be definitely asked, so read about these concepts.
[10:12] Just theoretical uh concepts, so supervised versus unsupervised, regression, classification, linear and logistic regression, decision trees, random forest, KNN, bias-variance tradeoff, overfitting, underfitting, gradient descent, loss functions, train-test split.
[10:29] So, by the way, these seem a lot, but they are mostly just a few slides worth of content.
[10:33] So, you can get the basics from if you even if you put this on AI, it will give you some basic idea, so do read it.
[10:40] Cross-validation, feature engineering, regularization, precision, recall, F1 score, these are all just some basic scores, ROC AUC, confusion matrix, clustering, uh and PCA basics.
[10:52] So, it is a lot, so so you might need to take a basic AI course.
[10:54] So, you can do that before the selection test because selection test is anyways like in end of June, but the main thing for now should be focusing on the registration and statement of purpose.
[11:05] Previous year questions are in next slide, so let's go there.
[11:06] So, these are 2025 pro MCQs and
[11:10] programming questions, 2024, 2024 resources, tips, MCQs and programming questions.
[11:15] This is by the way medium article 2024 discussion.
[11:18] There's a Reddit discussion on Reddit where everyone is discussing how many test cases they did and so on.
[11:23] Then these are some good YouTube videos.
[11:25] I just opened the 2025 MCQs to give you an idea.
[11:26] Rest of the links, please open them on your own.
[11:29] I We put this slide in the description, so do check it out.
[11:33] So, this is the 2025 exam questions.
[11:36] So, you can see this is a MCQ questions.
[11:38] This is very simple simplification.
[11:39] Uh okay.
[11:43] What are the coordinates of the minimum point?
[11:45] This is probability.
[11:47] How many students play neither cricket nor football?
[11:50] Uh which encoding strategy to use?
[11:53] Cross-validation.
[11:54] Which of these cross-validation techniques are useful?
[11:56] Uh two dices are rolled.
[11:58] The posterior distribution is proportional to who's again ML.
[11:59] Which of this best describes the concept of Bayesian inference?
[12:03] Uh which of these is a valid assumption of PCA?
[12:06] Uh in reinforcement learning, what is a policy defined?
[12:11] How does gradient boosting differ from bagging?
[12:14] So, you can see that they've actually asked a lot of it machine learning basics.
[12:17] So, you will need a machine learning course at least.
[12:20] Which of these is a problem commonly addressed by using batch normalization neural networks?
[12:24] They've also asked neural network.
[12:26] So, it's not just basic ML.
[12:28] It's go on to neural networks.
[12:28] Uh cost function.
[12:30] Again, ML.
[12:31] Then, coding section is
[12:34] They've also mentioned that your code submission will be checked for plagiarism, so do not copy.
[12:39] Uh and they Please don't uh switch the tabs or windows.
[12:44] So, these are the real assessment questions, and Amazon does not release it.
[12:48] And again, uh different candidates can have different questions.
[12:52] So, that is something you need to keep in mind because it's going to run for 1 month.
[12:56] They will definitely not give the same answer question answers to everyone.
[12:59] So, try to find as many questions of previous years as you can because they can be different for everyone.
[13:06] So, do go through all of this.
[13:08] I won't go through it because the video will be long, but yeah.
[13:11] And then finally, if you get selected in
[13:12] ML summer school, you will have these supervised learning deep neural networks.
[13:16] Uh you will be taught all of these concepts from July 4th to July 26th.
[13:21] And hopefully, you will also get some interview opportunities.
[13:23] With that, we come to the end of this video.
[13:24] If you have any comments, any questions, check out the links in the description and also you can comment me or tell me what other videos you're looking for.
[13:35] And I hope that you get selected and see you in another video.