Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
0 Hour(s) 0 Minute(s)
Language
English
Taught by
Sundog Education by Frank Kane, Sundog Education Team
Rating
Course Overview
Test your readiness for the newest, toughest AWS certification (MLS-C01) with a full-length, realistic practice exam.
UPDATED FOR 2024 with updated questions & answers, more detailed explanations, and more realistic questions.
Nervous about the AWS Certified Machine Learning Specialty exam? You should be! It's arguably the toughest certification exam AWS offers, as it not only tests AWS-specific knowledge, but your practical experience in machine learning and deep learning in general. It's tough to know what to expect on the exam before going in.
This practice exam offers a realistic, full-length simulation of what you can expect in the AWS MLS-C01 exam. It's not a "brain dump," but a complete, 65-question, 3-hour practice exam with original questions of the same style, topics, difficulty, and breakdown of the real exam. It's a great test of your readiness before you decide to invest in the real exam, and a great way to see what sorts of topics the exam will touch on. We also include a 15-question warmup test that will give you a rough idea of your readiness in just a half an hour.
The author of this exam, Frank Kane, is a popular machine learning instructor on Udemy who passed the AWS Certified Machine Learning exam himself on the first try - as well as the AWS Certified Big Data Specialty exam, which the Machine Learning exam builds upon. Frank spent over 9 years working at Amazon itself in Seattle, as a senior manager specializing in some of Amazon's early machine learning development.
Just like the real exam, this practice exam tests four different domains:
Data Engineering
Exploratory Data Analysis
Modeling
Machine Learning Implementation and Operations
You'll need deep and broad knowledge of SageMaker and AWS's other machine learning services, including Rekognition, Translate, Polly, and Comprehend. You'll need to know how to process big data using Kinesis, S3, Glue, and Athena. And you'll need a strong knowledge of AWS security, including use of KMS and IAM.
But AWS knowledge is not enough to pass this practice exam, or the real thing! You also need deep knowledge on data science, feature engineering and tuning your machine learning models. Do you really understand regularization techniques and how to use them? Do you really understand precision, recall, and AUC? Do you know how different deep learning models work, and how they are used? This practice exam will let you find out. Every question includes an explanation of the correct answer as well.
Don't risk hundreds of dollars and hours of your time on the AWS Certified Machine Learning Exam until you're sure you're ready for it. This practice exam is a good test of that readiness, and a good taste of what to expect. It's worth the effort - this AWS certification is the most elite one there is right now!
This practice exam is a bargain compared to the official AWS practice exam, and it's three times as long! Buy it now, and get some extra peace of mind as you head into your testing center.
Course Content
- 1 section(s)
- Section 1 Practice Tests
What You’ll Learn
- Determine readiness for the AWS Certifified Machine Leaning - Specialty Exam
- Test your knowledge on Data Engineering, Exploratory Analysis, Modeling, and Machine Learning Implemenation and Operations
- Learn what sorts of questions to expect on the real certification exam
- Get comfortable with the length and format of the real exam
Skills covered in this course
Reviews
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PPrabu Rethinakumar
Very nicely laid-out preparatory course! Highly recommend it
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FFrancisco M. Gómez S.
The questions contain a lot of mistakes and they are not corrected even when people have pointed those out months ago in the comments. Basically, you can't rely on the answers. Also, some questions seem to have been corrected but the explanations don't make sense because they still refer to the options' previous versions.
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EElvern Neylmav Tanny
This course is complete garbage. When he says he’s going to explain things blablabla and we all not need to worry, all he actually does is read off the slides (and the slide is useless too), without ever covering the why, the how, or giving any examples. Pointless material gets repeated over and over, while the actually important concepts are left untouched. The course is also poorly organized, and the materials are outdated, with no proper sources cited. I’m saying this as someone with an ML background. I still passed the exam tho, but only because I went through the documentation on my own. For those preparing, prioritize mastering the fundamentals of Machine Learning (ML). Begin with Data Preparation, which includes statistical analysis, handling missing values, addressing data imbalance, and managing outliers. Next, focus on Feature Engineering, like data splitting, transformation, encoding, scaling, and embedding techniques. Afterward, move on to Modeling, both in ML and DL, followed by Model Training, where you should understand hyperparameter tuning, overfitting, underfitting, and convergence. Within the modeling section, focus your attention to the 17 Amazon SageMaker Built-In Algorithms. Once you’re comfortable with those, study the AWS AI services that can serve as managed alternatives. For example, while you can build a seq2seq model for machine translation, you can also use Amazon Translate for a managed solution. Next, dive deeply into Amazon SageMaker itself to understand its environment, such as Amazon SageMaker Domain, Studio, Notebook Instances, and Jobs, as well as its supporting services like Ground Truth, Canvas, Data Wrangler, and Clarify. Finally, expand your knowledge to include data engineering services, such as AWS Glue, Amazon Athena, and Amazon Kinesis.
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UUlises Rey
Disappointed that they reused questions from their training course on ML-C01.
