How to prepare for AWS machine learning specialty exam (MLS-C01) ?

The AWS Certified Machine Learning – Specialty (MLS-C01) exam is a test from Amazon Web Services (AWS) that checks if you can create, set up, put into action, and manage machine learning (ML) solutions on AWS. It’s for people who know a lot about ML and have experience using AWS services for ML projects.

The exam covers a broad range of topics related to ML on AWS, including data engineering, exploratory data analysis, modeling, machine learning algorithms, AWS services for ML, model deployment and monitoring, and business and ethical considerations.

To prepare for the exam, AWS recommends that you have at least one year of hands-on experience with machine learning and experience using AWS services for ML workloads. You can also take advantage of the many training and certification resources offered by AWS, including instructor-led courses, online training, and self-paced labs.

Getting the AWS Certified Machine Learning – Specialty certification shows that you’re really good at making, using, and taking care of ML solutions on AWS. This can open up new job chances and help you move ahead in the ML field.

AWS machine learning specialty (MLS-C01) Glossary

• Machine Learning (ML): A part of artificial intelligence (AI) where it teaches computer programs to make guesses or choices using information from data.
• Supervised Learning: A kind of machine learning method where a model learns from data that’s already labeled to make predictions on new, unlabeled data.
• Unsupervised Learning: A type of ML algorithm that involves training a model on unlabeled data to identify patterns and structures in the data.
• Reinforcement Learning: A type of ML algorithm that involves training a model to make decisions based on feedback received from its environment.
• Deep Learning: A subset of ML that involves training deep neural networks with many layers to make predictions or decisions.
• Data Engineering: The process of collecting, preparing, and transforming data for use in ML models.
• Exploratory Data Analysis (EDA): The process of visualizing and summarizing data to gain insights and identify patterns.
• Model Selection: The process of choosing the best model for a particular ML problem based on its performance on a validation dataset.
• Learning Model Deployment: The process of making an ML model available for use in a production environment.
• Model Monitoring: The process of monitoring the performance of an ML model over time and making updates as necessary.
• AWS Services for ML: A suite of AWS services that enable users to build, train, deploy, and monitor ML models, including Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend.

AWS machine learning specialty (MLS-C01) Study Guide

1. Exam Guide: The AWS Certified Machine Learning – Specialty Exam Guide provides a detailed overview of the topics covered on the exam, the format of the exam, and the passing score. You can access the guide here: https://d1.awsstatic.com/training-and-certification/docs-ml/AWS-Certified-Machine-Learning-Specialty_Exam-Guide_EN.pdf
2. Sample Questions: AWS provides a set of sample exam questions to help you prepare for the exam. The questions are intended to give you a better understanding of the types of questions you can expect to see on the exam. You can access the sample questions here: https://d1.awsstatic.com/training-and-certification/docs-ml/AWS-Certified-Machine-Learning-Specialty_Sample-Questions.pdf
3. Exam Readiness Course: The AWS Exam Readiness course for the AWS Certified Machine Learning – Specialty exam is designed to help you prepare for the exam. The course covers exam structure and question formats, tips for answering exam questions, and sample questions with explanations. You can access the course here: https://www.aws.training/Details/eLearning?id=42143
4. AWS Whitepapers: AWS provides a number of whitepapers that are relevant to the AWS Certified Machine Learning – Specialty exam. These include the AWS Well-Architected Framework, the AWS Machine Learning Lens, and the AWS Security Best Practices. You can access these whitepapers here: https://aws.amazon.com/whitepapers/
5. AWS Certified Machine Learning – Specialty Certification: The AWS Certified Machine Learning – Specialty certification page provides information about the exam, including prerequisites, recommended knowledge and experience, and certification benefits. You can access the certification page here: https://aws.amazon.com/certification/certified-machine-learning-specialty/

AWS machine learning specialty (MLS-C01) Exam tips and tricks

• Understand the Exam Format: The exam consists of 65 multiple-choice and multiple-response questions and you have 180 minutes to complete it. It’s important to understand the exam format so you can manage your time effectively and not get bogged down on any one question.
• Review AWS Services: The exam covers a wide range of AWS services related to machine learning, so it’s important to be familiar with them. Review services like Amazon SageMaker, Amazon Rekognition, Amazon Comprehend, and Amazon Elastic Inference.
• Practice with Sample Questions: AWS provides a set of sample exam questions that you can use to prepare for the exam. Practice answering these questions to get a better sense of the types of questions you’ll see on the exam.
• Study Key Concepts: Ensure you grasp important machine learning ideas like supervised learning, unsupervised learning, and reinforcement learning. Also, go over data engineering concepts such as getting data ready, changing data, and exploring data.
• Review AWS Best Practices: Review AWS best practices related to machine learning, such as the AWS Well-Architected Framework, the AWS Machine Learning Lens, and the AWS Security Best Practices. These can help you understand how to build and deploy machine learning models in a secure and reliable way.
• Use AWS Documentation: Use the official AWS documentation to deepen your understanding of the services and concepts covered on the exam. AWS provides detailed documentation on all of its services, which can be a valuable resource in your exam preparation.
• Join a Study Group: Consider joining a study group to connect with other professionals preparing for the same exam. You can discuss key concepts, review sample questions, and provide support to each other as you prepare for the exam.

Syllabus outline

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

Domain 1: Data Engineering

1.1 Create data repositories for machine learning.

• Identify data sources (e.g., content and location, primary sources such as user data) (AWS Documentation: Supported data sources)
• Determine storage mediums (e.g., DB, Data Lake, S3, EFS, 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.

• Data job styles/types (batch load, streaming)
• Data ingestion pipelines (Batch-based ML workloads and streaming-based ML workloads)
  • Kinesis (AWS Documentation: Amazon Kinesis Data Streams)
  • Further, Kinesis Analytics (AWS Documentation: Amazon Kinesis Data Analytics)
  • Kinesis Firehose (AWS Documentation: Build seamless data streaming pipelines)
  • EMR (AWS Documentation: Process Data Using Amazon EMR with Hadoop Streaming, Optimize downstream data processing)
  • Glue (AWS Documentation: Simplify data pipelines, AWS Glue)
• Job scheduling (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, EMR, AWS Batch) (AWS Documentation: extract, transform, and load data for analytic processing using AWS Glue)
• Handle ML-specific data using map reduce (Hadoop, Spark, 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

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)
• Labeled data (recognizing when you have enough labeled data and identifying mitigation strategies [Data labeling tools (Mechanical Turk, manual labor)]) (AWS Documentation: data labeling for machine learning, Amazon Mechanical Turk, Use Amazon Mechanical Turk with Amazon SageMaker)

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 machine learning.

• Graphing (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)
• Clustering (hierarchical, diagnosing, elbow plot, cluster size)

Domain 3: Modeling

3.1 Frame business problems as machine learning problems.

• Determine when to use/when not to use ML (AWS Documentation: When to Use Machine Learning)
• Know the difference between supervised and unsupervised learning
• Selecting from among classification, regression, forecasting, clustering, recommendation, etc. (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 machine learning 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 machine learning models.

• Train validation test split, 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)
• Optimizer, gradient descent, loss functions, local minima, convergence, batches, probability, etc.
• Compute choice (GPU vs. CPU, distributed vs. non-distributed, platform [Spark vs. non-Spark]) (AWS Documentation: Introduction to Apache Spark)
• Model updates and retraining (AWS Documentation:Retraining Models on New Data, Automating model retraining and deployment)
  • Batch vs. real-time/online

3.4 Perform hyperparameter optimization.

• Regularization (AWS Documentation:Training Parameters)
  • Drop out
  • L1/L2
• Cross validation (AWS Documentation: Cross-Validation)
• Model initialization
• Neural network architecture (layers/nodes), learning rate, activation functions
• Tree-based models (# of trees, # of levels)
• Linear models (learning rate)

3.5 Evaluate machine learning models.

• Avoid overfitting/underfitting (detect and handle bias and variance) (AWS Documentation: Underfitting vs. Overfitting, Amazon SageMaker Clarify Detects Bias and Increases the Transparency, Amazon SageMaker Clarify)
• Metrics (AUC-ROC, accuracy, precision, recall, RMSE, F1 score)
• 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

4.1 Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.

(AWS Documentation: Review the ML Model’s Predictive Performance, Best practices, Resilience in Amazon SageMaker)

• AWS environment logging and monitoring (AWS Documentation:Logging and Monitoring)
  • CloudTrail and 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 (AWS Documentation: ML Platform Monitoring)
• Multiple regions, Multiple AZs (AWS Documentation: Regions and Endpoints, Best practices)
• AMI/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)
• Auto Scaling groups (AWS Documentation: Automatically Scale Amazon SageMaker Models, Configuring autoscaling inference endpoints)
• Rightsizing
  • 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 machine learning services and features for a given problem.

• ML on AWS (application services)
  • Poly (AWS Documentation: Amazon Polly, Build a unique Brand Voice with Amazon Polly)
  • Lex (AWS Documentation: Amazon Lex, Build more effective conversations on Amazon Lex)
  • Transcribe (AWS Documentation: Amazon Transcribe, Transcribe speech to text in real time)
• AWS service limits (AWS Documentation: Amazon SageMaker endpoints and quotas, Amazon Machine Learning endpoints and quotas, System Limits)
• Build your own model vs. SageMaker built-in algorithms (AWS Documentation: Use Amazon SageMaker Built-in Algorithms or Pre-trained Models)
• Infrastructure: (spot, instance types), cost considerations (AWS Documentation:Instance Types for Built-in Algorithms)
  • 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 machine learning solutions.

• 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/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 machine learning solutions.

• Exposing endpoints and interacting with them (AWS Documentation: Creating a machine learning-powered REST API, Call an Amazon SageMaker model endpoint)
• ML model versioning (AWS Documentation: Model versioning, Register a Model Version)
• 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)
• ML debugging/troubleshooting (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)

Now that we have every detail about the AWS machine learning specialty exam let us move to the preparatory resources for the exam.

Preparatory resources 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 give you more time for practicing and reviewing. Now, let’s explore some helpful resources that will assist you in acing the exam.

Resource 1: the official site

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.

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

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

The AWS Machine Learning Practice Exam is the best method to succeed in the test with a high score. The more you practice, the better you’ll understand the concepts. Regularly practice sample questions and take practice tests as much as possible. This will help you discover where you’re struggling and where you need improvement. It will also show you which areas you’re well-prepared for in terms of the exam. This is a crucial part of getting ready. Numerous trusted educational websites provide sample questions and ensure a 100% success rate. Try a free practice test now!

This was the list of some of the resources that you can use for preparation. Now let us move to the conclusion part along with some of the tips.

Conclusion

AWS Machine Learning Certification Difficulty is really high as compared to other AWS certifications. You need to be completely focused in order to pass the exam. Make sure to revise the important concepts on the exam day and follow your schedule strictly. Make sure to make notes so that you do not miss out on anything important. And practice as much as you can.

You will surely get the certification and make yourself proud. Just a pinch of hard work, a pinch of dedication mixed with right set of resources is required to clear the exam and showcase your abilities.

All the best!