Shadowing Practice: AWS Machine Learning Engineer Associate: What You Need to Know - Learn English Speaking with Video
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The AWS Certified Machine Learning Engineer Associate is one of the hottest and newest certifications in all of tech.
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If you're considering this certification or curious about how you prepare, in this video I'm going to tell you exactly what you need to know.
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If you're new here, I'm Greg, creator of Thoughtful Techie Cloud.
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And each week I'm bringing you a simple to understand video to help you navigate your AWS cloud and tech journey.
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If you haven't already, what are you waiting for?
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Go ahead and subscribe right now.
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built up enough confidence to actually schedule it.
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This video, my aim is to give you the system
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that you can put together so you can come up with
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that to make your decision whether or not this is a certification for you.
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And I'm going to highlight tons of resources and techniques that you can use to prepare for this.
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And I promise you, this video will not disappoint.
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Now, I'm going to go over a lot of information here, but I want you to sit back,
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relax, and just chill because you know I'm going to put those links for you in the description below.
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I got you.
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I always like to start these videos by going over what I call the AWS certification hierarchy.
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If you're new to AWS, there's going to be four buckets of certification.
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There's going to be the foundational bucket.
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These are going to be knowledge-based certifications for foundational understanding of AWS cloud.
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No prior experience is needed.
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So this is kind of like just starting out.
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Then we're going to go to the associate level.
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These are role-based certifications that showcase your knowledge and skills on AWS and help you build your credibility on AWS cloud.
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As you notice here, the machine learning engineer associate fits into the associate level.
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As you progress throughout your studies, you can bump up to the professional level.
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Right now, solution architect professional as well as DevOps engineer professionals are a choice.
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These are also role-based certifications, but they're going to validate your advanced skills and knowledge required to design secure,
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optimize, and modernize applications and automate processes on AWS.
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And then specialty, there are three right now.
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This is going to allow you to dive deeper and position yourself as a trusted advisor in specific areas.
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But our focus today is going to be on the Machine Learning Engineer Associate.
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We're in an interesting phase for this certification right now.
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It is currently in beta.
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The AWS Certified Machine Learning Engineer Associate is going to validate your technical ability in implementing machine learning workloads
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and production and operationalizing them.
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I really want to focus your attention on that implementing those ML workloads and operationalizing them.
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This certification is not just about knowing what something is or knowing when to use a service in this particular use case.
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You need to have implementation level knowledge.
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This is definitely going to boost your career profile and credibility if you manage to clear this exam.
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And you're going to position yourself for in-demand machine learning job roles, which are all the rage right now.
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Super in-demand.
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The exam duration while this thing is in beta is 170 minutes there are 85 questions
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and the current cost is 75 USD.
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Intended candidates are individuals with at least one year of experience using Amazon SageMaker and other ML engineering AWS services.
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And the candidate role examples of somebody that may be interested in taking this would be back-end software developers,
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DevOps engineers, data engineers, ML ops engineers, and data scientists.
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Now, if none of that rings a bell or aligns directly with you.
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Don't worry.
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You don't need to throw your hands up and say, this is not for me.
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You can still power through.
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You just may need to be able to take some additional training to be able to supplement where you have any gaps.
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So not to worry there.
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One of the greatest things you can do as a first
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step in preparation for this certification is to review the exam guide.
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This full exam guide for this certification is 23 pages.
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Now, don't worry.
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I'm not going to read everything word for word, but I am going to highlight your attention to things
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that you should pay particular close attention to and get you familiar with the overall structure of this exam guide.
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I highly encourage you after you watch this full video to go and first thing you do,
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download this exam guide and read it from start to finish because it's going to help you out immensely.
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Again, I want to reiterate here that this exam will validate your ability to build,
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operationalize, deploy, and maintain machine learning solutions and pipelines using AWS Cloud.
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Now, I really want to be specific here with these tasks.
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You're going to need to be familiar with things like ingest, transform, validate, and prepare data for ML modeling.
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Because guess what?
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You can't just take raw data and train your models against it.
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You really need to go through that phase to clean that data to get it ready for ML modeling.
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You're going to need to be able to select general modeling approaches, how to train models, tune hyper parameters, analyze model performance, and manage model versions.
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What about deployment and choosing endpoints and infrastructure to support those endpoints?
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You're going to need to have familiarity with that.
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Things such as provisioning the necessary compute resources and configuring auto-scaling policies based on requirements for your workload.
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Continuous integration and continuous delivery pipelines to automate and orchestrate your ML workflows are also gonna be needed for this exam.
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As I mentioned in the overview, monitoring models, data, and infrastructure to detect any issues in your ML ops pipelines also necessary.
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And one thing we can't forget, security of your ML systems and resources through access controls, compliance features, and security best practices.
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Now let's take a look at some recommended general IT knowledge.
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The target candidate should have the following general IT knowledge.
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A basic understanding of common ML algorithms and their use cases is going to be beneficial to you.
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Data engineering fundamentals, including knowledge of common data formats, the ingestion and transformation to work with ML data pipelines.
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I mentioned that earlier, that whole ingest and transform that whole bit there.
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That's the data engineering fundamentals and knowledge of querying and transforming data.
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Additionally, knowledge of software engineering best practices for modular reusable code development,
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deployment, debugging, familiarity with provisioning and monitoring cloud on-premises, ML resources, the CI-CD pipelines I mentioned earlier,
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and experience with code repositories for version control for those CI-CD pipelines.
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Recommended AWS knowledge, again, there's gonna be some overlap here with what we've talked about, but this is from an AWS perspective.
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Knowledge of SageMaker capabilities and algorithms for building and deployment.
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Knowledge of AWS data storage and processing services.
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Again, that's gonna support preparing the data for the modeling.
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Familiarity with deploying applications and infrastructure on AWS.
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Monitoring tools, logging and troubleshooting ML systems.
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Knowledge of AWS services specifically to help you automate and orchestrate a CI CD pipeline.
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And I mentioned security, but from an AWS perspective, things such as identity and access management,
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encryption and data protection within AWS gonna help you out big time.
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Now, just as important as it is to understand what tasks are necessary for you to know to prepare for the exam,
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these are actually some job tasks that are out of scope for the target candidate.
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It's not an exhaustive list, but just a few things you can cross off the list of things you need to worry about.
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You don't need to worry about designing and architecting full end-to-end ML solutions.
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You don't need to worry about setting up best practices and guiding ML strategies.
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You don't need to worry about handling integration with a wide array of services or new tools and technologies.
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You don't need to know how to work deeply into a more ML domains, for example, natural language processing and computer vision.
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Thank goodness there.
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And you don't need to worry about quantizing models and analyzing the impact on accuracy.
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Thank goodness to all of those.
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Although I think you'll still have your hands full with some of the information that we mentioned earlier.
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Super important exam content as it relates to question types.
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Now, AWS certifications have always had the multiple choice and multiple response,
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but with this certification, AWS is introducing some additional question types that you might not be familiar with, so listen closely.
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So multiple choice, these have been around for a while, it's gonna have one correct response and three incorrect responses known as distractors.
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Multiple response is gonna have two or more correct responses out of five, and you must select all the correct responses to receive credit for the question.
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Let's talk about these new question types, ordering.
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So ordering questions are gonna have a list of three to five responses to complete a specific task,
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and you must select the correct responses and literally place the responses in the correct order to receive credit for the question.
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Matching.
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Now, this is going to be questions where you have a list of responses to match.
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It can be three to seven prompts, and you must match all those pairs correctly to receive credit for the question.
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Case studies, final question type here, is you're going to have a scenario, and it's going to have two or more questions associated with the scenario.
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The scenario is the same for each question in the case study, and each question in the case study will be evaluated separately.
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you'll receive credit for each question that you answer correctly in that case study.
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Something else you do not want to gloss over are the domains.
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There's going to be four domains on this test.
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And the domains are simply the areas that you need to focus on in preparation for the exam.
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And the weightings, which means the scored content, how much of this topic you can find out of 100% on the exam.
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So for example, domain one is data preparation for machine learning.
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it's going to be 28% of scored content.
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Domain two, ML model development, 26% of scored content is going to be based on domain two.
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Domain three, deployment and orchestration of ML workflows, going to be 22% of scored content.
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And then domain four, ML solution monitoring, maintenance, and security worth 24% of scored content.
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Now, as an example, I want to go over domain one
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because this exam guide really drills down into each of these domains.
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Under those domain categories are what are known as task statements.
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There are a handful of task statements for domain
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that give you the knowledge that you need to be familiar with as well as skills in the map to this.
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I'm not going to go over these word for word
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but I just want to show you how this is formatted
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and the fact that you do need to download this
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and read it in its entirety
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because this is going to be a goldmine of insight to help you direct your energies in your preparation.
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So task statement 1.1 aligned with domain 1 is focusing on the ingesting and storing data.
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And we talked about this earlier in some of those tasks.
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Things like be familiar with data formats and ingestion mechanisms,
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how to use core AWS services like Amazon S3 and Amazon Elastic File System and Amazon FSX,
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how to use AWS streaming data sources to ingest data, AWS storage options including use cases and trade-offs in making those choices.
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In the skills department, extracting data from storage,
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choosing appropriate data formats, ingesting data in Amazon SageMaker Data Wrangler and SageMaker Feature Store.
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A ton of insight in each one of these task statements as it aligns to the domain.
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I recommend you read this document in its entirety.
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And you follow this format for all the domains.
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For example, domain two, which is focused on ML model development.
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Task statement 2.1 is choosing a modeling approach.
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It has the knowledge as well as the skills that align to that task statement for that domain.
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Make sure you check these out for the entire exam guide.
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And real quick, before we move away from this exam guide, there's an appendix right it has in scope aws services
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and features that you should know about
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so they break these out into different categories you have analytics
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category application integration cloud financial management compute containers database developer tools
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and within each one of these categories you'll have a list
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of aws services additionally you have out of scope aws services and features
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and these will be considered out of scope for this exam this is about working smart not working hard now
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if you're still here got a couple of surprises for you
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got some free training for you i want to point your
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attention to the standard exam prep aws certified machine learning engineer associate training this is around a seven
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and a half hours worth of free training it's going to
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include introduction to machine learning art of the possible planning a machine learning project
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and an amazon bedrock getting started training as well as the exam prep standard course itself.
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That course is going to go over getting to know the exam and the exam style questions.
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It's going to talk about refreshing your AWS knowledge and skills, review and practice, and then it's going to go into domain one,
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two, three, and four in detail to really help you prepare for this certification.
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And if you're still here, I've got another surprise for you.
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AWS Power Hour for ML Engineer Associate.
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This is a training that AWS Training and Certification puts on.
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You can watch these AWS Power Hours live if it aligns with your schedule.
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If not, they are recorded so you can watch them on demand as well.
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There are six episodes starting with the episode one exam introduction and then every episode beyond that,
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episode two, three, four, and five is aligned with the domains of the certification itself.
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And then finally, episode six will include review and practice.
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We covered a ton of stuff.
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All the links to everything we talked about are in the description below.
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Before you go, check out this video right here, and I'll see you in the next video.
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Vocabulary and speaking notes for this lesson
This video has 177 sentences and 2433 words to shadow. The speech runs for 15:08. The speaker talks at a natural 161 words per minute, close to everyday conversation. Only 81% of the words are among the 3,000 most common in English, so the vocabulary is demanding.
Key vocabulary in this video
15 less common words from the video, with pronunciation and meaning:
- examination /ɪɡˌzæm.ɪˈneɪ.ʃən/ (noun) — The act of examining.
- domain /ˌdəʊˈmeɪ̯n/ (noun) — A geographic area owned or controlled by a single person or organization.
- certification /ˌsɜː.tɪ.fɪˈkeɪ.ʃən/ (noun) — The act of certifying.
- associate /əˈsoʊʃi.ət/ (noun) — A person united with another or others in an act, enterprise, or business; a partner or employee.
- cloud /ˈklaʊ̯d/ (noun) — A visible mass of water droplets suspended in the air.
- pipeline /ˈpaɪpˌlaɪn/ (noun) — A conduit made of pipes used to convey water, gas or petroleum, etc.
- align /əˈlaɪn/ (verb) — To form a line; to fall into line.
- format /ˈfɔːɹ.mæt/ (noun) — The layout of a publication or document.
- monitor /ˈmɑ.nɪ.tɚ/ (noun) — Someone who watches over something; a person in charge of something or someone.
- preparation /ˌpɹɛpəˈɹeɪʃ(ə)n/ (noun) — The act of preparing or getting ready.
- scope /skəʊp/ (noun) — The breadth, depth or reach of a subject; the extent of applicability or relevance; a domain, purview or remit.
- deployment /dɪˈplɔɪmənt/ (noun) — An arrangement or classification of things.
- infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/ (noun) — An underlying base or foundation for a building, organization, or system.
- select /sɪˈlɛkt/ (verb) — To choose one or more elements of a set, especially a set of options.
- integration /ˌɪn.tɪˈɡɹeɪ.ʃən/ (noun) — The act or process of making whole or entire.
Phrasal verbs you will hear
- go over (verb) — To look at carefully; to scrutinize; to analyze.
- talked about (adjective) — Discussed by many people.
- talk about (verb) — Used to draw attention to the speaker's characterization of someone or something.
- build up (verb) — To erect; to construct.
- check out (verb) — To record the departure or withdrawal of someone or something (such as guests, employees, books, etc.).
- come up with (verb) — To manage to produce, deliver, or present (something) by inventing, creating, thinking of, or obtaining it.
- cross off (verb) — To strike out; to cross out; to draw a line through.
- fit into (verb) — To be of the right size and shape to be placed in a location.
Pronunciation to watch
The speaker uses 45 contractions and reduced forms, such as you're, don't, I'm. Say them the short way, as you hear them.
- The “sh” and “zh” sounds: examination /ɪɡˌzæm.ɪˈneɪ.ʃən/, certification /ˌsɜː.tɪ.fɪˈkeɪ.ʃən/, associate /əˈsoʊʃi.ət/, preparation /ˌpɹɛpəˈɹeɪʃ(ə)n/, integration /ˌɪn.tɪˈɡɹeɪ.ʃən/
- Long words — get the stress right: examination /ɪɡˌzæm.ɪˈneɪ.ʃən/, certification /ˌsɜː.tɪ.fɪˈkeɪ.ʃən/, associate /əˈsoʊʃi.ət/, preparation /ˌpɹɛpəˈɹeɪʃ(ə)n/, infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/
How to practise with this video
- Listen to the whole video once without speaking and note the words you do not know.
- Start at 0.75× speed, shadow it sentence by sentence, then go back to normal speed once it feels easy.
- Record yourself and compare with the original, paying attention to words like examination, domain, certification.
Grammar in this video
The structures the speaker uses most, with the exact words from the video:
| Structure | In the video |
|---|---|
| Passive voice be + past participle — the focus is on what happens, not who does it | is needed · be needed · will be evaluated |
| Relative clauses who / which + clause — extra information about a person or thing | roles, which are · weightings, which means · two, which is |
| Present perfect have/has + past participle — a past action that still matters now | we've talked · have been |
What is the Shadowing Technique?
Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.