AWS Certified Machine Learning Specialty Full Practice Exam from Udemy

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.

What's inside

Syllabus

This 15-question warmup test should give you a good idea of how prepared you really are for the full practice exam, and for the real one - without investing 3 hours in the process. We chose these questions to be representative of the domains covered by the real exam, and some of the more difficult topics you'll be expected to know on it. If you're surprised by the topics and level of detail you encounter, you know you have more preparation and studying to do.The AWS Certified Machine Learning Specialty exam goes beyond AWS topics, and tests your knowledge in feature engineering, model tuning, and modeling as well as how deep neural networks work. You need to both have expert-level knowledge of AWS's machine learning services (especially SageMaker), and expert-level knowledge in machine learning and AI in general. Exam content copyright (c) 2019-2021 Sundog Software LLC. All rights reserved worldwide.

Simulate the real AWS Certified Machine Learning Specialty exam experience. You'll encounter the same number of questions, the same breakdown of topics, and a similar format to the real thing. Your ability to pass or fail this exam should be a good indicator of your readiness for the real thing.When taking the real exam, some questions might sound similar to some of these practice questions, since they cover the same material. But they are not the same. If you see a question on the real exam that looks familiar, don't automatically select the same answer that you remember from this practice exam. Odds are the question is actually a little different, and has a different answer. The actual exam is timed and you will have 3 hours, with no breaks, to complete it. You should find that 3 hours is more than sufficient, so take your time and read each question and possible answer carefully.Copyright (c) 2019-2021 Sundog Software LLC. All rights reserved worldwide.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Offers a realistic simulation of the AWS MLS-C01 exam, which helps learners gauge their readiness and identify areas for improvement before investing in the actual certification

Requires a strong understanding of AWS services like SageMaker, Rekognition, Translate, Polly, Comprehend, Kinesis, S3, Glue, Athena, KMS, and IAM, which may require additional study for those new to the AWS ecosystem

Tests knowledge of data science, feature engineering, and model tuning, which are essential skills for machine learning practitioners beyond just AWS-specific knowledge

Includes a 15-question warmup test, which provides a quick assessment of readiness and helps learners identify knowledge gaps early on in their preparation

Features questions that cover data engineering, exploratory data analysis, modeling, and machine learning implementation and operations, which are the core domains of the actual exam

Updated for 2024 with updated questions and answers, more detailed explanations, and more realistic questions, which ensures the practice exam remains relevant and aligned with the current exam content

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in AWS Certified Machine Learning Specialty Full Practice Exam with these activities:

Review Machine Learning Fundamentals

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Solidify your understanding of core machine learning concepts. This will help you better grasp the AWS-specific implementations and nuances tested in the exam.

Browse courses on Regularization Techniques

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Review key concepts like bias-variance tradeoff and model evaluation metrics.
Practice applying these concepts to different types of machine learning problems.

Create Flashcards

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Memorize key concepts and definitions related to AWS machine learning services. This will help you quickly recall information during the exam.

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Create flashcards for each AWS service mentioned in the course description.
Include definitions, use cases, and key features on each flashcard.
Review the flashcards regularly to reinforce your memory.

Read 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow'

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Gain a deeper understanding of machine learning algorithms and their implementation. This will complement your AWS knowledge and improve your ability to answer exam questions.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read the chapters relevant to the exam domains, focusing on model building and evaluation.
Experiment with the code examples to solidify your understanding.

Four other activities

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SageMaker Practice

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Reinforce your understanding of SageMaker functionalities. This will help you answer questions related to model deployment, training, and optimization.

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Deploy a pre-trained model using SageMaker inference endpoints.
Train a model using SageMaker's built-in algorithms.
Optimize model performance using SageMaker's hyperparameter tuning capabilities.

Read 'Designing Machine Learning Systems'

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Understand the challenges of deploying machine learning models in production. This will help you answer questions related to scalability, reliability, and monitoring.

View AI Engineering: Building Applications with... on Amazon

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Read the chapters on data engineering, model deployment, and monitoring.
Consider how these concepts apply to the AWS environment.

Build a Machine Learning Pipeline on AWS

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Apply your knowledge to a real-world scenario. This will help you understand the end-to-end process of building and deploying machine learning models on AWS.

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Choose a dataset and a machine learning problem.
Use AWS Glue to prepare the data.
Train a model using SageMaker.
Deploy the model using SageMaker inference endpoints.

Create a Cheat Sheet

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Quickly reference key information during the exam. This will help you save time and avoid making mistakes.

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Compile a list of key concepts, definitions, and formulas.
Organize the information in a clear and concise manner.
Review the cheat sheet regularly to reinforce your memory.

Career center

Learners who complete AWS Certified Machine Learning Specialty Full Practice Exam will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A Machine Learning Engineer develops and deploys machine learning models. This role requires deep knowledge of machine learning concepts and practical implementation skills. This course provides a full length simulation of the AWS Certified Machine Learning Specialty exam, including questions that cover data engineering, exploratory data analysis, modeling, and machine learning implementation. It will give aspiring Machine Learning Engineers the practical experience in machine learning and deep learning that are crucial to this role. The practice exam covers the use of SageMaker, which is a critical part of this role, as well as other AWS services. This course can expose those seeking a career as a machine learning engineer to techniques for feature engineering and tuning models.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A Data Scientist analyzes and interprets complex data to create actionable insights for businesses. This involves machine learning techniques such as feature engineering, model tuning, and understanding metrics like precision, recall, and AUC. This course's practice exam tests knowledge of these concepts, along with deep learning models, and how to use them. Aspiring data scientists can use this course to gauge their knowledge in these areas. The course is helpful for practicing the skills required for exploratory data analysis, which is a key part of this role, and it may be beneficial for those intending to use AWS services like Sagemaker and Kinesis.

See salaries and explore the career path for Data Scientist

Artificial Intelligence Specialist

An Artificial Intelligence Specialist focuses on developing and implementing AI algorithms and systems. The work includes areas such as deep learning, feature engineering, and creating models using specific frameworks like those provided by AWS. Given that this course is designed to test knowledge of these topics, and AWS's machine learning services, an aspiring artificial intelligence specialist can evaluate their readiness for this field. The practice exam includes questions covering modeling and data science. The course can assist in evaluating knowledge of AWS services that are frequently used, such as SageMaker, Rekognition, and others.

See salaries and explore the career path for Artificial Intelligence Specialist

Cloud Solutions Architect

A Cloud Solutions Architect designs and implements cloud-based systems and infrastructure, often incorporating machine learning components. This course provides a practice exam simulating the AWS Certified Machine Learning Specialty exam. It covers several AWS services including SageMaker, Kinesis, S3, Glue, and Athena, as well as AWS security. For a Cloud Solutions Architect working with machine learning, this course may be helpful for building insight into how these services and features all work together as a total system. One who wishes to pursue architecture in the cloud with machine learning should find this practical experience beneficial.

See salaries and explore the career path for Cloud Solutions Architect

Deep Learning Engineer

A Deep Learning Engineer designs and builds sophisticated models using neural networks. This role requires a strong understanding of deep learning architectures and algorithms. This practice exam specifically tests knowledge of how different deep learning models work and how they are used. This will give an aspiring Deep Learning Engineer a chance to evaluate their preparedness and identify any knowledge gaps. This course also covers machine learning techniques like regularization which are necessary to deep learning. This course will give an indication of preparedness.

See salaries and explore the career path for Deep Learning Engineer

Machine Learning Consultant

A Machine Learning Consultant provides expert advice and guidance on machine learning projects to various clients. This role requires not only a knowledge of machine learning itself, but also an ability to understand different business needs, and how these can be addressed by machine learning applications. This course allows a consultant to test and improve upon their knowledge of AWS machine learning services, and their mastery of model tuning and evaluation, and to be familiar with AWS services. The course can enhance a consultant's understanding of the range of machine learning and deep learning techniques.

See salaries and explore the career path for Machine Learning Consultant

Data Engineer

A Data Engineer focuses on the design and development of data pipelines, ensuring data is available for analysis and machine learning applications. The work a data engineer performs commonly includes data processing, and the use of data storage solutions. This practice exam covers the use of AWS services like Kinesis, S3, Glue, and Athena, which enable these operations. This course may be useful to learn the application of these data processing and storage technologies, as well as to test one's knowledge for potential application of this knowledge in the role of data engineer. One such engineer may find this helpful for becoming familiar with machine learning systems.

See salaries and explore the career path for Data Engineer

AI Researcher

An AI Researcher performs theoretical and practical research to advance the field of artificial intelligence. A deep understanding of machine learning models and techniques is necessary, as well as a strong awareness of a variety of algorithms. This course is designed to test your knowledge in feature engineering, model tuning, and the use of deep neural networks, all of which are relevant to AI research. The practice exam simulates the AWS Certified Machine Learning Specialty exam, and may be useful in furthering the knowledge of a researcher, especially one who intends to focus on AWS technologies. This course may be useful to understand the practical side of machine learning.

See salaries and explore the career path for AI Researcher

Business Intelligence Analyst

A Business Intelligence Analyst analyzes data to identify trends and create insights to improve business performance. This role involves data analysis and an understanding of how machine learning techniques can be applied. This course helps build familiarity with data analysis techniques and machine learning concepts by testing knowledge of exploratory data analysis, modeling, and evaluation metrics. Those who aspire to be a Business Intelligence Analyst may find that this course builds a foundation in machine learning, and improves data analysis skills by giving them opportunity to learn about practical applications of these techniques in the context of AWS products.

See salaries and explore the career path for Business Intelligence Analyst

Software Developer

A Software Developer develops and maintains software applications, which increasingly integrate machine learning and AI capabilities. This course's focus on machine learning implementation and operations, along with its coverage of AWS services such as SageMaker, may be helpful for a software developer seeking to integrate machine learning into their applications. The practice exam provides a good overview of the practical application of machine learning on the AWS platform. A software developer may find value in becoming familiar with AWS's services, and how they can be used in practical ways.

See salaries and explore the career path for Software Developer

Analytics Manager

An Analytics Manager oversees data analysis and insight generation within an organization. This role requires a baseline knowledge of machine learning and data analysis techniques to understand the work of their team. This course covers exploratory data analysis, modeling, and metrics such as precision, recall and AUC. While an Analytics Manager may not implement machine learning models, this course helps them understand the underlying concepts, especially in the context of AWS. They may find it helpful to become fluent in concepts such as regularization, and in the use of services such as SageMaker.

See salaries and explore the career path for Analytics Manager

Quantitative Analyst

A Quantitative Analyst, often in the finance industry, uses statistical and mathematical techniques to develop financial models. The work includes predictive modeling and data analysis, which can be enhanced with machine learning techniques. The course provides a practice exam that covers modeling, feature engineering, tuning and evaluation, and may enhance a quantitative analyst's understanding of these tools. This may be useful to explore AWS services like SageMaker, and other technologies that they would be required to use to implement their models.

See salaries and explore the career path for Quantitative Analyst

Research Scientist

A Research Scientist, often in academia or research labs, conducts experiments and analyses to gain insights in a specific scientific field. This role requires analytical skills and a strong foundation in data analysis. The course's focus on topics such as exploratory data analysis, model tuning, and machine learning implementation may be useful to a research scientist who wants to add computational tools to their repertoire. The course may also help them to understand some of the specific services offered by AWS, such as SageMaker.

See salaries and explore the career path for Research Scientist

Technology Consultant

A Technology Consultant provides advice and assistance to businesses on the use of technology to improve their processes. This role may involve understanding machine learning, and how to apply it in various contexts. This course may be useful to build an understanding of this by covering AWS products related to this technology. The practice questions will test knowledge of topics such as exploratory data analysis, modeling and evaluation. A consultant may find this useful to learn the practical concerns of implementing advanced models.

See salaries and explore the career path for Technology Consultant

Database Administrator

A Database Administrator manages and maintains databases, ensuring they are secure and efficient. While this role is not directly related to data science, a database administrator may find it useful to understand the tools that are used by those who will work with the data they are managing. This practice exam covers the use of AWS services like Kinesis, S3 and Glue. A database administrator may find that this overview is useful in understanding machine learning systems, and in preparing for potential changes in the technology landscape.

See salaries and explore the career path for Database Administrator

AWS Certified Machine Learning Specialty Full Practice Exam

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