AWS Machine Learning Specialty (MLS-C01)

Machine learning has become the trend for IT enthusiasts. AWS Machine Learning specialty exam is designed to handle Amazon Web Services product that allows a developer to discover patterns in end-user data through algorithms, construct mathematical models based on these patterns and then create and implement predictive applications. Every organization wants its most important asset – workforce to be always updated in the domain of technology. And if you’re working with the IT company then keeping yourself updated on the technological side is necessary for saving your position and reputation. The certifications also show you dedication towards your work and your organization. Keeping yourself updated makes you feel more confident and also helps you to stand out in the crowd.

AWS Machine Learning Certification was accepted as most difficult certification among all other certifications offered by amazon. These kind of IT certifications have been challenging to crack and requires proper knowledge of the subject. Also, merely getting the certificate is not enough. You have to develop a complete understanding of the subject to know its application in reality. All this can be achieved if you have right set of resources and a proper schedule or your strategy. So, if you are preparing to ace this exam, you are at right destination as we provide you all the necessary details with our AWS Machine Learning Tutorials

What is Amazon machine learning specialty exam?

The AWS Certified Machine Learning – Specialty (MLS-C01) examination is intended for individuals who perform a development or data science role. This exam validates an examinee’s ability to build, train, tune, and deploy machine learning (ML) models using the AWS Cloud.

It evaluates an examinee’s ability to design, implement, deploy, and maintain ML solutions for given business problems. It will validate the candidate’s ability to:

• Select and justify the appropriate ML approach for a given business problem.
• Identify appropriate AWS services to implement ML solutions.
• Design and implement scalable, cost-optimized, reliable, and secure ML solutions.

The exam will test you on the following major domains and the weightage of each domain is given along.

• Domain 1: Data Engineering – 20%
• Domain 2: Exploratory Data Analysis – 24%
• Domain 3: Modeling – 36%
• Domain 4: Machine Learning Implementation and Operations – 20%

AWS Machine Learning Specialty Interview Questions

Practice with AWS Machine Learning Specialty Interview Questions and clear your interview successfully with Confidence.

Exam overview

The AWS Machine Learning Specialist Certification exam consists of 65 scenario-based questions in order to evaluate a candidate’s ability to solve different business problems. This is a specialty exam and duration for the exam is 170 minutes.  The AWS Machine Learning Certification Cost is $300 although the prices may vary from place to place. You can schedule the exam at Pearson VUE or PSI. The type of questions asked are multiple choice questions and multiple response questions. AWS machine learning specialty exam is measured on a scale of 1 – 1000 and passing score is 750 marks. AWS machine learning specialty exam is available in English, Japanese, Korean, and Simplified Chinese.

Exam Details

Name of the exam	AWS machine learning specialty
Exam code	MLS-C01
Exam type	Specialty
Exam duration	170 minutes
Exam cost	$300
Format	Multiple choice questions and multiple response questions
Passing score	750 marks
Languages available	English, Japanese, Korean, and Simplified Chinese

AWS Machine Learning Certification Prerequisites

Amazon recommends that a candidate appearing for the AWS machine learning specialty exam shall have following knowledge and experience:

• Firstly, 1-2 years of experience developing, architecting, or running ML/deep learning workloads on the AWS Cloud.
• Subsequently, the ability to express the intuition behind basic ML algorithms.
• Also, Experience performing basic hyperparameter optimization.
• Furthermore, Experience with ML and deep learning frameworks.
• Also, the ability to follow model-training best practices.
• Finally, the ability to follow deployment and operational best practices.

Result policy

Upon completing your exam, you will receive a pass or fail notification on the testing screen. Most of Amazon exams use a scale-scoring method. You will receive an email confirming your exam completion. Your detailed exam results will be available within five business days of completing your exam.

Exam Retake Policy

The candidates who do not pass an exam must wait 14 days before they are eligible to retake the exam. There is no limit on exam attempts until the candidate has passed. For each exam attempt, the full registration price must be paid i.e. $300 in the case of AWS machine learning specialty exam.

How to register for the AWS Machine Learning Specialty Exam?

To register for an exam, sign in to aws.training and click Certification in the top navigation. Next, click the AWS Certification Account button, followed by Schedule New Exam. Find the exam you wish to take and click either the Schedule at PSI or Schedule at Pearson VUE button. You will then be redirected to the test delivery provider’s scheduling page, where you will complete your exam registration.

Path for AWS Machine Learning Professionals

AWS has designed Machine Learning path so that Professionals can examine their skills and experience based on developing, tuning, training and deploying Machine learning models using services of AWS cloud.

For Specialty Level Machine Learning path in AWS have two paths,

Machine Learning Path for Data Scientist

This path is for individuals who are skilled in statistics, mathematics and analysis and want to become an expert in Machine learning in their organization. In this you will learn about the frameworks and analysis tools which are used for improving workplace.

AWS Machine Learning Path for Developer

Machine Learning Developer path is for software developers and builders. This will help you learn how Artificial Intelligence and Machine learning together can help you get better partner with Data Scientist to innovating with Machine learning technologies.

Other exam policies

Before you sit for the exam, make sure that you have all the information related to exam policies and terms and conditions of the exam by visiting official site. Do not miss out on anything important before sitting for the AWS Machine Learning Specialty Certificate exam.

To know more, visit: FAQs for AWS machine learning specialty exam

Syllabus outline

The Amazon AWS Machine Learning Certification exam will test you on the basis of following domains. The compositions of the domains are also fixed. Let us have a look at the AWS Machine Learning Certification Course Outline

Domain 1: Data Engineering (20%)

1.1 Create data repositories for ML.

• Identify data sources (e.g., content and location, primary sources such as user data) (AWS Documentation: Supported data sources)
• Determine storage mediums (for example, databases, Amazon S3, Amazon Elastic File System [Amazon EFS], Amazon Elastic Block Store [Amazon EBS]). (AWS Documentation: Using Amazon S3 with Amazon ML, Creating a Datasource with Amazon Redshift Data, Using Data from an Amazon RDS Database, Host instance storage volumes, Amazon Machine Learning and Amazon Elastic File System)

1.2 Identify and implement a data ingestion solution.

• Identify data job styles and job types (for example, batch load, streaming).
• Orchestrate data ingestion pipelines (batch-based ML workloads and streaming-based ML workloads).
  • Amazon Kinesis (AWS Documentation: Amazon Kinesis Data Streams)
  • Amazon Data Firehose
  • Amazon EMR (AWS Documentation: Process Data Using Amazon EMR with Hadoop Streaming, Optimize downstream data processing)
  • Amazon Glue (AWS Documentation: Simplify data pipelines, AWS Glue)
  • Amazon Managed Service for Apache Flink
• Schedule Job (AWS Documentation: Job scheduling, Time-based schedules for jobs and crawlers)

1.3 Identify and implement a data transformation solution.

• Transforming data transit (ETL: Glue, Amazon EMR, AWS Batch) (AWS Documentation: extract, transform, and load data for analytic processing using AWS Glue)
• Handle ML-specific data by using MapReduce (for example, Apache Hadoop, Apache Spark, Apache Hive). (AWS Documentation: Large-Scale Machine Learning with Spark on Amazon EMR, Apache Hive on Amazon EMR, Apache Spark on Amazon EMR, Use Apache Spark with Amazon SageMaker, Perform interactive data engineering and data science workflows)

Domain 2: Exploratory Data Analysis (24%)

2.1 Sanitize and prepare data for modeling.

• Identify and handle missing data, corrupt data, stop words, etc. (AWS Documentation: Managing missing values in your target and related datasets, Amazon SageMaker DeepAR now supports missing values, Configuring Text Analysis Schemes)
• Formatting, normalizing, augmenting, and scaling data (AWS Documentation: Understanding the Data Format for Amazon ML, Common Data Formats for Training, Data Transformations Reference, AWS Glue DataBrew, Easily train models using datasets, Visualizing Amazon SageMaker machine learning predictions)
• Determine whether there is sufficient labeled data. (AWS Documentation: data labeling for machine learning, Amazon Mechanical Turk, Use Amazon Mechanical Turk with Amazon SageMaker)
  • Identify mitigation strategies.
  • Use data labelling tools (for example, Amazon Mechanical Turk).

2.2 Perform feature engineering.

• Identify and extract features from data sets, including from data sources such as text, speech, image, public datasets, etc. (AWS Documentation: Feature Processing, Feature engineering, Amazon Textract, Amazon Textract features)
• Analyze/evaluate feature engineering concepts (binning, tokenization, outliers, synthetic features, 1 hot encoding, reducing dimensionality of data) (AWS Documentation: Data Transformations Reference, Building a serverless tokenization solution to mask sensitive data, ML-powered anomaly detection for outliers, ONE_HOT_ENCODING, Running Principal Component Analysis, Perform a large-scale principal component analysis)

2.3 Analyze and visualize data for ML.

• Create Graphs (scatter plot, time series, histogram, box plot) (AWS Documentation: Using scatter plots, Run a query that produces a time series visualization, Using histograms, Using box plots)
• Interpreting descriptive statistics (correlation, summary statistics, p value)
• Perform cluster analysis (for example, hierarchical, diagnosis, elbow plot, cluster size).

Domain 3: Modeling (36%)

3.1 Frame business problems as ML problems.

• Determine when to use and when not to use ML (AWS Documentation: When to Use Machine Learning)
• Know the difference between supervised and unsupervised learning
• Select from among classification, regression, forecasting, clustering, recommendation, and foundation models. (AWS Documentation: K-means clustering with Amazon SageMaker, Building a customized recommender system in Amazon SageMaker)

3.2 Select the appropriate model(s) for a given ML problem.

• Xgboost, logistic regression, K-means, linear regression, decision trees, random forests, RNN, CNN, Ensemble, Transfer learning (AWS Documentation: XGBoost Algorithm, K-means clustering with Amazon SageMaker, Forecasting financial time series, Amazon Forecast can now use Convolutional Neural Networks, Detecting hidden but non-trivial problems in transfer learning models)
• Express intuition behind models

3.3 Train ML models.

• Split data between training and validation (for example, cross validation). (AWS Documentation: Train a Model, Incremental Training, Managed Spot Training, Validate a Machine Learning Model, Cross-Validation, Model support, metrics, and validation, Splitting Your Data)
• Understand optimization techniques for ML training (for example, gradient descent, loss functions, convergence).
• Choose appropriate compute resources (for example GPU or CPU, distributed or non-distributed).
  • Choose appropriate compute platforms (Spark or non-Spark).
• Update and retraining Models (AWS Documentation:Retraining Models on New Data, Automating model retraining and deployment)
  • Batch vs. real-time/online

3.4 Perform hyperparameter optimization.

• Perform Regularization (AWS Documentation:Training Parameters)
  • Drop out
  • L1/L2
• Perform Cross validation (AWS Documentation: Cross-Validation)
• Model initialization
• Neural network architecture (layers/nodes), learning rate, activation functions
• Understand tree-based models (number of trees, number of levels).
• Understand linear models (learning rate).

3.5 Evaluate ML models.

• Avoid overfitting and underfitting
  • Detect and handle bias and variance (AWS Documentation: Underfitting vs. Overfitting, Amazon SageMaker Clarify Detects Bias and Increases the Transparency, Amazon SageMaker Clarify)
• Evaluate metrics (for example, area under curve [AUC]-receiver operating characteristics [ROC], accuracy, precision, recall, Root Mean Square Error [RMSE], F1 score).
• Interpret confusion matrix (AWS Documentation: Custom classifier metrics)
• Offline and online model evaluation (A/B testing) (AWS Documentation: Validate a Machine Learning Model, Machine Learning Lens)
• Compare models using metrics (time to train a model, quality of model, engineering costs) (AWS Documentation: Easily monitor and visualize metrics while training models, Model Quality Metrics, Monitor model quality)
• Cross validation (AWS Documentation: Cross-Validation, Model support, metrics, and validation)

Domain 4: Machine Learning Implementation and Operations (20%)

4.1 Build ML solutions for performance, availability, scalability, resiliency, and fault tolerance. (AWS Documentation: Review the ML Model’s Predictive Performance, Best practices, Resilience in Amazon SageMaker)

• Log and monitor AWS environments (AWS Documentation:Logging and Monitoring)
  • AWS CloudTrail and AWS CloudWatch (AWS Documentation: Logging Amazon ML API Calls with AWS CloudTrail, Log Amazon SageMaker API Calls, Monitoring Amazon ML, Monitor Amazon SageMaker)
  • Build error monitoring solutions (AWS Documentation: ML Platform Monitoring)
• Deploy to multiple AWS Regions and multiple Availability Zones. (AWS Documentation: Regions and Endpoints, Best practices)
• AMI and golden image (AWS Documentation: AWS Deep Learning AMI)
• Docker containers (AWS Documentation: Why use Docker containers for machine learning development, Using Docker containers with SageMaker)
• Deploy Auto Scaling groups (AWS Documentation: Automatically Scale Amazon SageMaker Models, Configuring autoscaling inference endpoints)
• Rightsizing resources, for example:
  • Instances (AWS Documentation: Ensure efficient compute resources on Amazon SageMaker)
  • Provisioned IOPS (AWS Documentation: Optimizing I/O for GPU performance tuning of deep learning)
  • Volumes (AWS Documentation: Customize your notebook volume size, up to 16 TB)
• Load balancing (AWS Documentation: Managing your machine learning lifecycle)
• AWS best practices (AWS Documentation: Machine learning best practices in financial services)

4.2 Recommend and implement the appropriate ML services and features for a given problem.

• ML on AWS (application services)
  • Amazon Poly (AWS Documentation: Amazon Polly, Build a unique Brand Voice with Amazon Polly)
  • Amazon Lex (AWS Documentation: Amazon Lex, Build more effective conversations on Amazon Lex)
  • Amazon Transcribe (AWS Documentation: Amazon Transcribe, Transcribe speech to text in real time)
  • Amazon Q
• Understand AWS service quotas (AWS Documentation: Amazon SageMaker endpoints and quotas, Amazon Machine Learning endpoints and quotas, System Limits)
• Determine when to build custom models and when to use Amazon SageMaker built-in algorithms.
• Understand AWS infrastructure (for example, instance types) and cost considerations.
  • Using spot instances to train deep learning models using AWS Batch (AWS Documentation: Train Deep Learning Models on GPUs)

4.3 Apply basic AWS security practices to ML solutions.

• AWS Identity and Access Management (IAM) (AWS Documentation: Controlling Access to Amazon ML Resources, Identity and Access Management in AWS Deep Learning Containers)
• S3 bucket policies (AWS Documentation: Using Amazon S3 with Amazon ML, Granting Amazon ML Permissions to Read Your Data from Amazon S3)
• Security groups (AWS Documentation: Secure multi-account model deployment with Amazon SageMaker, Use an AWS Deep Learning AMI)
• VPC (AWS Documentation: Securing Amazon SageMaker Studio connectivity, Direct access to Amazon SageMaker notebooks, Building secure machine learning environments)
• Encryption and anonymization (AWS Documentation: Protect Data at Rest Using Encryption, Protecting Data in Transit with Encryption, Anonymize and manage data in your data lake)

4.4 Deploy and operationalize ML solutions.

• Exposing endpoints and interacting with them (AWS Documentation: Creating a machine learning-powered REST API, Call an Amazon SageMaker model endpoint)
• Understand ML models.
• A/B testing (AWS Documentation: A/B Testing ML models in production, Dynamic A/B testing for machine learning models)
• Retrain pipelines (AWS Documentation: Automating model retraining and deployment, Machine Learning Lens)
• Debug and troubleshoot ML models (AWS Documentation:Debug Your Machine Learning Models, Analyzing open-source ML pipeline models in real time, Troubleshoot Amazon SageMaker model deployments)
  • Detect and mitigate drop in performance (AWS Documentation: Identify bottlenecks, improve resource utilization, and reduce ML training costs, Optimizing I/O for GPU performance tuning of deep learning training)
  • Monitor performance of the model (AWS Documentation: Monitor models for data and model quality, bias, and explainability, Monitoring in-production ML models at large scale)

Preparatory Guide for AWS Machine Learning Specialty Exam

AWS Machine Learning Specialty Preparations are quite challenging one and requires a lot of dedication and hard work combined with right set of resources to ace the exam. There are numerous resources but we need to figure out the ones which are beneficial for us. The resources through which we can gain more in less time. This will help in increasing the time that will be available for practice and revisions. Let us look some handful resources that will help you in passing the exam with flying colors.

Resource 1: The Official learning path by Amazon

The official site of amazon recommends the hands-on experience along with the online training and sample papers in order to ace the exam. Always make sure to visit the official site to gather details about every detail of the exam. The official site provides knowledge about technical aspects about the exam and about the latest updates of the exam. There are many official resources that are made available the amazon for the exam. Amazon is also providing free webinars to help spread knowledge about the exam. in addition, amazon provides various classroom sessions and expert-led courses as listed below:

Recommended Progression

• Machine Learning Exam Basics
• Process Model: CRISP-DM on the AWS Stack
• The Elements of Data Science
• Storage Deep Dive Learning Path
• Machine Learning Security
• Developing Machine Learning Applications
• Types of Machine Learning Solutions

Branching content areas

• Communicating with Chat Bots
• Speaking of: Machine Translation and NLP
• Seeing Clearly: Computer Vision Theory

Optional training

• Big Data on AWS

For more training options, you an visit Training Library by Amazon for machine learning.

Resource 2: Online training programs

There are many AWS Machine Learning Certification Training programs which are made available by the educational sites. You can find the training programs that are best suitable to you according to the syllabus and availability of time. There are online classes as well as instructor-led classes which offers interactive way of learning. You can clear your doubts without any hesitation and take the test series along with the courses from the same site.

Resource 3: Books

Books are the most valued resources for all time. You can refer to many books for AWS machine learning specialty exam.  You can choose any book that covers the aspects of the syllabus and has the language according to your ease. There are many books available as:

• Mastering Machine Learning on AWS: Advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow
• Machine Learning with Aws
• Effective Amazon Machine Learning
• Learning Amazon Web Services (AWS): A Hands-On Guide to the Fundamentals of AWS Cloud | First Edition | By Pearson
• Pragmatic AI an Introduction to Cloud Based Machine Learning

Also, you can enhance your learning with AWS Machine Learning Documentation and AWS Machine Learning White Papers.

Resource 4: Join study groups and discussions

You can join many study groups for improving your preparations and pooling different resources. Discussions help you test your knowledge. Try to form the groups with the people who are more interactive as this will help you in getting answers quickly. This will help to instill a competitive spirit in you and increase your performance.

Resource 5: Practice papers and test series

AWS Machine Learning Practice Exam is the only way out to pass the exam with a good score. The more you practice, the more your concepts will be clear. Always practice sample papers and take test series as much as you can. This will help to find your loopholes and will help to identify your weak areas. You will find the parts that you need to work more on and the parts that are fully prepared from the exam point of view. This is the most important part of preparation. Many reliable educational sites offer you sample papers and guarantee 100% success. Try a free practice test now!

Upgrade your skills and get ready to qualify AWS Machine Learning Specialty Exam with latest practice tests and expert learning resources. Start Preparing Now!