AWS Machine Learning Specialty Exam Study Guide

AWS Machine Learning specialty exam designed for use with Amazon Web Services products that let programmers build mathematical models based on patterns they find in user data, then develop and deploy predictive applications. The most challenging certification among all those given by Amazon is the AWS machine learning speciality. These kind of IT certifications have been challenging to crack and requires proper knowledge of the subject.

Moreover, only receiving the certificate is insufficient. To comprehend how the topic is used in practice, you must have a thorough comprehension of it. If you have the appropriate tools, a sound plan of action, and an appropriate timeline, you can do all of this.

AWS Machine Learning Specialty Exam

The AWS Machine Learning Specialty exam is for people who want to show they are really good at using machine learning (ML) on AWS. This test includes questions about different ML things like working with data, analyzing data, making models, using ML tricks, and putting it all into action.

Here are some key topics covered in the AWS Machine Learning Specialty exam:

1. Data preparation and feature engineering: This involves selecting, cleaning, and transforming data for use in ML models. It includes techniques such as normalization, dimensionality reduction, and feature scaling.
2. Exploratory data analysis: This involves using statistical techniques to analyze and visualize data, identify patterns, and gain insights into the data.
3. Modeling: This involves building ML models using techniques such as regression, classification, clustering, and dimensionality reduction. It also includes selecting appropriate models for specific use cases and evaluating model performance.
4. Machine learning algorithms: This means you need to learn and use various ML methods, like decision trees, random forests, support vector machines, and neural networks.
5. Deployment: This involves deploying ML models to production environments and integrating them with other systems. It includes understanding best practices for deploying models, such as containerization, serverless computing, and using AWS services such as SageMaker.

AWS Machine Learning Certification Learning Path

AWS has designed a 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.

Key Terms To Focus

Here are some key terms you should be familiar with if you’re preparing for the AWS Machine Learning Specialty exam:

• Supervised learning: In this kind of machine learning, the model learns from data that already has the right answers. The aim is to figure out a way for the computer to link the inputs to the correct outputs.
• Unsupervised learning: A type of machine learning where the model is trained on unlabeled data, meaning the data does not include the correct output. The goal is to find hidden patterns or structures in the data.
• Reinforcement learning: In this type of machine learning, the model learns by doing things in an environment and getting either rewards or punishments based on what it does. The aim is to figure out the best way to act over time to get the most rewards.
• Deep learning: Deep learning is a part of machine learning where we teach deep neural networks, which have lots of layers. It’s super useful because it’s helped us do really well in tasks like recognizing pictures, understanding language, and recognizing speech.
• Feature engineering: The process of selecting, extracting, and transforming features (variables) from raw data to improve the performance of machine learning models. Feature engineering can involve techniques such as normalization, dimensionality reduction, and feature selection.
• Bias-variance tradeoff: A fundamental tradeoff in machine learning between bias (underfitting) and variance (overfitting). A model with high bias is too simple and cannot capture the complexity of the data, while a model with high variance is too complex and can fit the noise in the data.
• Regularization: Regularization is a method in machine learning to stop models from getting too focused on the training data. We do this by adding a penalty to the math we use to train the model. There are different ways to do this, like L1 and L2 regularization, dropout, and stopping early.
• Hyperparameter tuning: The process of selecting the best hyperparameters (parameters that are set before training the model) for a machine learning algorithm. Hyperparameter tuning can involve techniques such as grid search, random search, and Bayesian optimization.
• Model selection: The process of selecting the best model architecture and hyperparameters for a particular task. Model selection can involve comparing the performance of different models on a validation set or using techniques such as cross-validation.
• Deployment: Getting a machine learning model ready to use in the real world is called deployment. It’s like making it work outside of the lab. We can do this in different ways, like putting it in a container, using serverless computing, or using AWS tools like SageMaker.

Study Guide for AWS Machine Learning Specialty Exam

To prepare for the AWS Certified Machine Learning – Specialty exam, you should have hands-on experience with AWS machine learning services, including Amazon SageMaker, Amazon Comprehend, Amazon Rekognition, and Amazon Forecast. You should also have a solid understanding of machine learning concepts and algorithms, including supervised and unsupervised learning, deep learning, and reinforcement learning.

AWS offers various resources to help you prepare for the certification exam, including AWS training courses, whitepapers, and the AWS Certified Machine Learning – Specialty exam guide. Additionally, you can take practice exams and use online resources such as study groups and online forums to increase your knowledge and prepare for the certification.

AWS Machine Learning Specialty Preparations are quite challenging and require a lot of dedication and hard work combined with the 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 at a handful of resources that will help you in passing the exam with flying colors.

Step 1- Gather all exam detail

The first step is to collect all the information about exam policies and courses. You must familiarise with the exam course before beginning your preparations. The course outline acts as the blueprint for the exam. It covers all about the important exam details and concepts covered in the exam. Therefore, you must refer the Exam Guide in order to clear the exam. This AWS Machine Learning Certification Course covers the following domains-

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)

Step 2 – Know about the Learning Resources

There are plenty of learning resources available in the market place. We recommend you to refer the following so as the ace the exam.

Resource 1: The Official Learning Path by Amazon

The official site of amazon recommends hands-on experience along with 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. Further, the official site provides knowledge about the technical aspects of the exam and about the latest updates on the exam. Also, 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:

Resource 2: Online Training Programs

There are many AWS Machine Learning Certification Training programs that are made available by educational sites. You can find the training programs that are best suitable to you according to the syllabus and availability of time. Moreover, there are online classes as well as instructor-led classes which offer an interactive way of learning. Further, you can clear your doubts without any hesitation and take the test series along with the courses from the same site. For more training options, you an visit Training Library by Amazon for machine learning.

Resource 3: Reference Books

Books are the most valued resources of all time. You can refer to many books for the AWS machine learning specialty Certification 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:

1. Mastering Machine Learning on AWS: Advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow
2. Effective Amazon Machine Learning
3. Learning Amazon Web Services (AWS): A Hands-On Guide to the Fundamentals of AWS Cloud | First Edition | By Pearson

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 groups with 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.

Step 3 – Attempt Practice Tests

The AWS Machine Learning Certification Practice Exam is your key to getting a high score on the test. The more you practice, the better you’ll understand the material. It’s important to do practice questions and take practice tests as much as possible. This will help you discover where you need improvement and where you’re already strong in terms of the exam topics. It’s a crucial part of your preparation. Many trustworthy educational websites provide sample papers and promise a 100% success rate. Try a free practice test now!

Exam Tips:

Here are some tips to help you prepare for and pass the AWS Certified Machine Learning Specialty exam:

• Gain hands-on experience: The AWS Certified Machine Learning – Specialty certification checks if you can create, build, and put machine learning models into action using AWS tools. It’s essential to get real practice using AWS machine learning services like Amazon SageMaker, Amazon Comprehend, Amazon Rekognition, and Amazon Forecast.
• Study the exam domains: The AWS Certified Machine Learning – Specialty exam covers five domains: Data Engineering, Exploratory Data Analysis, Modeling, Deployment, and Operations and Maintenance. Make sure you understand the topics covered in each domain and can apply that knowledge to real-world scenarios.
• Use official AWS resources: AWS provides a range of resources to help you prepare for the AWS Certified Machine Learning – Specialty exam, including training courses, whitepapers, and the AWS Certified Machine Learning – Specialty exam guide. Make sure you use these resources to increase your knowledge and prepare for the certification.
• Join a study group: Joining a study group can be a great way to increase your knowledge and connect with others who are preparing for the AWS Certified Machine Learning – Specialty exam. You can find study groups online or in person, and you can use these groups to ask questions, share your knowledge, and support each other as you prepare for the certification.
• Stay current: AWS keeps making its machine learning tools better, so it’s vital to keep up with the latest changes in this area. You can do this by reading AWS documents and staying informed about what’s happening in the industry. This way, you’ll always have the most recent knowledge and abilities.

Final Words

With the increasing popularity of machine learning, more and more people are claiming to have expertise in the field. By earning the AWS Machine Learning Specialty certification, you can differentiate yourself from the competition and prove that you have the skills and knowledge to back up your claims. Machine learning is a rapidly evolving field, and staying up to date with the latest tools and techniques is essential for success. When you pass the AWS Machine Learning exam, you show that you’re really good at using the latest and advanced machine learning stuff.

The process of preparing for the exam involves gaining hands-on experience with AWS machine learning services, such as Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend. This can be a valuable learning experience in itself, regardless of whether you ultimately pass the exam.

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