{"id":22216,"date":"2021-12-29T11:00:00","date_gmt":"2021-12-29T05:30:00","guid":{"rendered":"https:\/\/www.testpreptraining.com\/blog\/?p=22216"},"modified":"2024-06-05T17:01:22","modified_gmt":"2024-06-05T11:31:22","slug":"how-hard-is-the-aws-machine-learning-specialty-exam","status":"publish","type":"post","link":"https:\/\/www.testpreptraining.ai\/blog\/how-hard-is-the-aws-machine-learning-specialty-exam\/","title":{"rendered":"How hard is the AWS Machine Learning Specialty Exam?"},"content":{"rendered":"\n<p>The <a href=\"https:\/\/www.testpreptraining.ai\/aws-certified-machine-learning-specialty-practice-exam\" target=\"_blank\" rel=\"noreferrer noopener\">AWS Machine Learning Specialty Exam<\/a> is a certification exam offered by Amazon Web Services (AWS) that validates an individual&#8217;s expertise in designing, implementing, deploying, and maintaining machine learning (ML) solutions on the AWS platform.<\/p>\n\n\n\n<p>The exam is intended for individuals who have a solid understanding of ML concepts, can use AWS services for ML workflows, and are proficient in building, training, and deploying ML models on AWS.<\/p>\n\n\n\n<p>To prepare for the exam, candidates need to have experience with AWS services such as Amazon SageMaker, Amazon Rekognition, Amazon Comprehend, Amazon Personalize, and Amazon Forecast. They should also be familiar with data science and ML concepts such as data wrangling, feature engineering, model selection, and evaluation metrics.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>AWS Machine Learning Specialty Exam Glossary<\/strong><\/h3>\n\n\n\n<p>Here is a glossary of some common terms and concepts related to the AWS Machine Learning Specialty Exam:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Artificial Intelligence (AI): The simulation of human intelligence processes by machines, especially computer systems.<\/li>\n\n\n\n<li>Machine Learning (ML): A subset of AI that enables machines to learn from data and improve over time without being explicitly programmed.<\/li>\n\n\n\n<li>Deep Learning (DL): A subset of machine learning that uses artificial neural networks with multiple layers to analyze and learn from data.<\/li>\n\n\n\n<li>Neural Network: A set of algorithms that are modeled after the structure of the human brain to recognize patterns in data.<\/li>\n\n\n\n<li>Data Science: The study of data and how it can be used to solve complex problems and make decisions.<\/li>\n\n\n\n<li>Feature Engineering: The process of selecting and extracting relevant features from data to improve the performance of machine learning models.<\/li>\n\n\n\n<li>Supervised Learning: A type of machine learning where the algorithm learns from labeled data, where the target variable is known.<\/li>\n\n\n\n<li>Reinforcement Learning: A type of machine learning where the algorithm learns by receiving feedback from the environment and adjusting its actions accordingly.<\/li>\n\n\n\n<li>Model Selection: The process of selecting the best machine learning algorithm and hyperparameters for a given problem.<\/li>\n\n\n\n<li>Evaluation Metrics: The metrics used to evaluate the performance of machine learning models, such as accuracy, precision, recall, and F1 score.<\/li>\n\n\n\n<li>Amazon SageMaker: A fully managed service that enables developers and data scientists to build, train, and deploy machine learning models at scale.<\/li>\n\n\n\n<li>Amazon Comprehend: A service that uses natural language processing (NLP) to extract insights and relationships from text.<\/li>\n\n\n\n<li>Learn Amazon Forecast: A service that provides time-series forecasting using machine learning algorithms.<\/li>\n\n\n\n<li>Amazon Augmented AI: A service that enables human review and feedback on machine learning predictions to improve accuracy and reduce bias.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>AWS Machine Learning Specialty Study Guide<\/strong><\/h3>\n\n\n\n<p>Here are some official AWS resources that can help you prepare for the AWS Machine Learning Specialty Exam:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>AWS Exam Readiness: AWS Certified Machine Learning &#8211; Specialty: This free, digital course is designed to help you prepare for the exam by covering key concepts and exam content. The course includes video lessons, demonstrations, and quizzes.<\/li>\n\n\n\n<li>AWS Machine Learning Blog: The AWS Machine Learning Blog is a great resource for learning about the latest updates and best practices in machine learning on AWS. The blog features articles, tutorials, case studies, and announcements related to AWS machine learning services.<\/li>\n\n\n\n<li>AWS Documentation: The AWS Documentation provides detailed documentation on AWS machines learning services such as Amazon SageMaker, Amazon Rekognition, Amazon Comprehend, Amazon Personalize, and Amazon Forecast. The documentation includes technical guides, API references, and examples.<\/li>\n\n\n\n<li>AWS Certified Machine Learning &#8211; Specialty Exam Guide: This official exam guide provides information about the exam format, content areas, and sample questions. It also includes study tips and recommended resources for exam preparation. <\/li>\n\n\n\n<li>AWS Certified Machine Learning &#8211; Specialty Exam Readiness Workshop: This one-day, instructor-led workshop is designed to help you prepare for the exam by covering key concepts and exam content. The workshop includes interactive discussions, demos, and hands-on labs.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>AWS Machine Learning Specialty Exam Tips and Tricks<\/strong><\/h3>\n\n\n\n<p>Here are some tips and tricks that can help you prepare for and pass the AWS Machine Learning Specialty Exam:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Understand the Exam Content: The exam covers a range of topics related to machine learning on AWS, including data engineering, data analysis, ML models, and deployment. It&#8217;s important to review the exam guide and ensure you understand each of the content areas.<\/li>\n\n\n\n<li>Review AWS Machine Learning Services: The exam includes questions related to AWS machine learning services such as Amazon SageMaker, Amazon Rekognition, Amazon Comprehend, Amazon Personalize, and Amazon Forecast. Review the documentation and understand the capabilities and use cases of each service.<\/li>\n\n\n\n<li>Practice with Sample Questions: Use the official sample exam questions and practice exams to get a sense of the exam format and difficulty level. Practice questions can also help you identify areas where you need more study.<\/li>\n\n\n\n<li>Hands-On Experience: It&#8217;s important to have hands-on experience with AWS machine learning services to prepare for the exam. Practice building, training, and deploying ML models using the services.<\/li>\n\n\n\n<li>Take AWS Training: AWS offers a range of training options for machine learning on AWS. Consider taking the official exam readiness course or attending an instructor-led workshop to get a deeper understanding of the exam content.<\/li>\n\n\n\n<li>Time Management: The exam includes 65 questions and you have 180 minutes to complete it. Manage your time carefully and ensure you have enough time to review your answers before submitting the exam.<\/li>\n<\/ul>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><a href=\"https:\/\/www.testpreptraining.ai\/tutorial\/aws-machine-learning-specialty-exam\/\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" width=\"961\" height=\"150\" src=\"https:\/\/www.testpreptraining.ai\/blog\/wp-content\/uploads\/2021\/12\/How-hard-is-the-AWS-Machine-Learning-Specialty-Exam-1.png\" alt=\"\" class=\"wp-image-22229\" srcset=\"https:\/\/www.testpreptraining.ai\/blog\/wp-content\/uploads\/2021\/12\/How-hard-is-the-AWS-Machine-Learning-Specialty-Exam-1.png 961w, https:\/\/www.testpreptraining.ai\/blog\/wp-content\/uploads\/2021\/12\/How-hard-is-the-AWS-Machine-Learning-Specialty-Exam-1-300x47.png 300w\" sizes=\"(max-width: 961px) 100vw, 961px\" \/><\/a><\/figure>\n<\/div>\n\n\n<h3 class=\"wp-block-heading\"><strong>Exam <strong><strong>Course Outline<\/strong><\/strong><\/strong> <\/h3>\n\n\n\n<p>There are 4 domains to focus on in this AWS Machine Learning Specialty Certificate,<\/p>\n\n\n\n<h5 class=\"wp-block-heading\"><strong>Domain 1: Data Engineering (20%)<\/strong><\/h5>\n\n\n\n<p>1.1 Create data repositories for ML.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Identify data sources (e.g., content and location, primary sources such as user data)&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/quicksight\/latest\/user\/supported-data-sources.html\" target=\"_blank\" rel=\"noreferrer noopener\">Supported data sources<\/a>)<\/li>\n\n\n\n<li>Determine storage mediums (for example, databases, Amazon S3, Amazon Elastic File System [Amazon EFS], Amazon Elastic Block Store [Amazon EBS]).&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/using-amazon-s3-with-amazon-ml.html\" target=\"_blank\" rel=\"noreferrer noopener\">Using Amazon S3 with Amazon ML<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/create-datasource-from-redshift-procedure.html\" target=\"_blank\" rel=\"noreferrer noopener\">Creating a Datasource with Amazon Redshift Data<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/using-amazon-rds-with-amazon-ml.html\" target=\"_blank\" rel=\"noreferrer noopener\">Using Data from an Amazon RDS Database<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/host-instance-storage.html\" target=\"_blank\" rel=\"noreferrer noopener\">Host instance storage volumes<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/apn\/amazon-machine-learning-and-amazon-elastic-file-system-amazon-efs-a-perspective-for-apn-partners\/\" target=\"_blank\" rel=\"noreferrer noopener\">Amazon Machine Learning and Amazon Elastic File System<\/a>)<\/li>\n<\/ul>\n\n\n\n<p>1.2 Identify and implement a data ingestion solution.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Identify data job styles and job types (for example, batch load, streaming).<\/li>\n\n\n\n<li>Orchestrate data ingestion pipelines (batch-based ML workloads and streaming-based ML workloads).\n<ul class=\"wp-block-list\">\n<li>Amazon Kinesis&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/aws.amazon.com\/kinesis\/data-streams\/getting-started\/\" target=\"_blank\" rel=\"noreferrer noopener\">Amazon Kinesis Data Streams<\/a>)<\/li>\n\n\n\n<li>Amazon Data Firehose<\/li>\n\n\n\n<li>Amazon EMR&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/datapipeline\/latest\/DeveloperGuide\/dp-launch-emr-jobflow.html\" target=\"_blank\" rel=\"noreferrer noopener\">Process Data Using Amazon EMR with Hadoop Streaming<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/big-data\/optimizing-downstream-data-processing-with-amazon-kinesis-data-firehose-and-amazon-emr-running-apache-spark\/\" target=\"_blank\" rel=\"noreferrer noopener\">Optimize downstream data processing<\/a>)<\/li>\n\n\n\n<li>Amazon Glue&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/big-data\/simplify-data-pipelines-with-aws-glue-automatic-code-generation-and-workflows\/\" target=\"_blank\" rel=\"noreferrer noopener\">Simplify data pipelines<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/glue\/?whats-new-cards.sort-by=item.additionalFields.postDateTime&amp;whats-new-cards.sort-order=desc\" target=\"_blank\" rel=\"noreferrer noopener\">AWS Glue<\/a>)<\/li>\n\n\n\n<li>Amazon Managed Service for Apache Flink<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Schedule Job&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/batch\/latest\/userguide\/job_scheduling.html\" target=\"_blank\" rel=\"noreferrer noopener\">Job scheduling<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/glue\/latest\/dg\/monitor-data-warehouse-schedule.html\" target=\"_blank\" rel=\"noreferrer noopener\">Time-based schedules for jobs and crawlers<\/a>)<\/li>\n<\/ul>\n\n\n\n<p>1.3 Identify and implement a data transformation solution.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Transforming data transit (ETL: Glue, Amazon EMR, AWS Batch)&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/database\/how-to-extract-transform-and-load-data-for-analytic-processing-using-aws-glue-part-2\/\" target=\"_blank\" rel=\"noreferrer noopener\">extract, transform, and load data for analytic processing using AWS Glue<\/a>)<\/li>\n\n\n\n<li>Handle ML-specific data by using MapReduce (for example, Apache Hadoop, Apache Spark, Apache Hive).&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/big-data\/large-scale-machine-learning-with-spark-on-amazon-emr\/\" target=\"_blank\" rel=\"noreferrer noopener\">Large-Scale Machine Learning with Spark on Amazon EMR<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/emr\/features\/hive\/\" target=\"_blank\" rel=\"noreferrer noopener\">Apache Hive on Amazon EMR<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/emr\/features\/spark\/\" target=\"_blank\" rel=\"noreferrer noopener\">Apache Spark on Amazon EMR<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/apache-spark.html\" target=\"_blank\" rel=\"noreferrer noopener\">Use Apache Spark with Amazon SageMaker<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/perform-interactive-data-engineering-and-data-science-workflows-from-amazon-sagemaker-studio-notebooks\/\" target=\"_blank\" rel=\"noreferrer noopener\">Perform interactive data engineering and data science workflows<\/a>)<\/li>\n<\/ul>\n\n\n\n<h5 class=\"wp-block-heading\"><strong>Domain 2: Exploratory Data Analysis (24%)<\/strong><\/h5>\n\n\n\n<p>2.1 Sanitize and prepare data for modeling.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Identify and handle missing data, corrupt data, stop words, etc.&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/managing-missing-values-in-your-target-and-related-datasets-with-automated-imputation-support-in-amazon-forecast\/\" target=\"_blank\" rel=\"noreferrer noopener\">Managing missing values in your target and related datasets<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/amazon-sagemaker-deepar-now-supports-missing-values-categorical-and-time-series-features-and-generalized-frequencies\/\" target=\"_blank\" rel=\"noreferrer noopener\">Amazon SageMaker DeepAR now supports missing values,<\/a><a href=\"https:\/\/docs.aws.amazon.com\/cloudsearch\/latest\/developerguide\/configuring-analysis-schemes.html\" target=\"_blank\" rel=\"noreferrer noopener\">&nbsp;Configuring Text Analysis Schemes<\/a>)<\/li>\n\n\n\n<li>Formatting, normalizing, augmenting, and scaling data&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/understanding-the-data-format-for-amazon-ml.html\" target=\"_blank\" rel=\"noreferrer noopener\">Understanding the Data Format for Amazon ML<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/cdf-training.html\" target=\"_blank\" rel=\"noreferrer noopener\">Common Data Formats for Training<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/data-transformations-reference.html\" target=\"_blank\" rel=\"noreferrer noopener\">Data Transformations Reference<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/about-aws\/whats-new\/2020\/11\/introducing-aws-glue-databrew-visual-data-preparation-tool-to-clean-and-normalize-data-up-to-80-percent-faster\/\" target=\"_blank\" rel=\"noreferrer noopener\">AWS Glue DataBrew<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/easily-train-models-using-datasets-labeled-by-amazon-sagemaker-ground-truth\/\" target=\"_blank\" rel=\"noreferrer noopener\">Easily train models using datasets<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/making-machine-learning-predictions-in-amazon-quicksight-and-amazon-sagemaker\/\" target=\"_blank\" rel=\"noreferrer noopener\">Visualizing Amazon SageMaker machine learning predictions<\/a>)<\/li>\n\n\n\n<li>Determine whether there is sufficient labeled data.\u00a0<strong>(AWS Documentation:<\/strong><a href=\"https:\/\/aws.amazon.com\/sagemaker\/data-labeling\/what-is-data-labeling\/\" target=\"_blank\" rel=\"noreferrer noopener\">data labeling for machine learning<\/a>,\u00a0<a href=\"https:\/\/docs.aws.amazon.com\/AWSMechTurk\/latest\/AWSMechanicalTurkGettingStartedGuide\/SvcIntro.html\" target=\"_blank\" rel=\"noreferrer noopener\">Amazon Mechanical Turk<\/a>,\u00a0<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/use-amazon-mechanical-turk-with-amazon-sagemaker-for-supervised-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\">Use Amazon Mechanical Turk with Amazon SageMaker<\/a>)\n<ul class=\"wp-block-list\">\n<li>Identify mitigation strategies.<\/li>\n\n\n\n<li>Use data labelling tools (for example, Amazon Mechanical Turk).<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p>2.2 Perform feature engineering.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Identify and extract features from data sets, including from data sources such as text, speech, image, public datasets, etc.&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/feature-processing.html\" target=\"_blank\" rel=\"noreferrer noopener\">Feature Processing<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/wellarchitected\/latest\/machine-learning-lens\/feature-engineering.html\" target=\"_blank\" rel=\"noreferrer noopener\">Feature engineering<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/textract\/\" target=\"_blank\" rel=\"noreferrer noopener\">Amazon Textract<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/textract\/features\/\" target=\"_blank\" rel=\"noreferrer noopener\">Amazon Textract features<\/a>)<\/li>\n\n\n\n<li>Analyze\/evaluate feature engineering concepts (binning, tokenization, outliers, synthetic features, 1 hot encoding, reducing dimensionality of data)&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/data-transformations-reference.html\" target=\"_blank\" rel=\"noreferrer noopener\">Data Transformations Reference<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/compute\/building-a-serverless-tokenization-solution-to-mask-sensitive-data\/\" target=\"_blank\" rel=\"noreferrer noopener\">Building a serverless tokenization solution to mask sensitive data<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/quicksight\/latest\/user\/anomaly-detection-function.html\" target=\"_blank\" rel=\"noreferrer noopener\">ML-powered anomaly detection for outliers<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/databrew\/latest\/dg\/recipe-actions.ONE_HOT_ENCODING.html\" target=\"_blank\" rel=\"noreferrer noopener\">ONE_HOT_ENCODING<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/running-principal-component-analysis-in-amazon-sagemaker\/\" target=\"_blank\" rel=\"noreferrer noopener\">Running Principal Component Analysis<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/perform-a-large-scale-principal-component-analysis-faster-using-amazon-sagemaker\/\" target=\"_blank\" rel=\"noreferrer noopener\">Perform a large-scale principal component analysis<\/a>)<\/li>\n<\/ul>\n\n\n\n<p>2.3 Analyze and visualize data for ML.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Create Graphs (scatter plot, time series, histogram, box plot)&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/quicksight\/latest\/user\/scatter-plot.html\" target=\"_blank\" rel=\"noreferrer noopener\">Using scatter plots<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/AmazonCloudWatch\/latest\/logs\/CWL_AnalyzeLogData_VisualizationQuery.html\" target=\"_blank\" rel=\"noreferrer noopener\">Run a query that produces a time series visualization<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/quicksight\/latest\/user\/histogram-charts.html\" target=\"_blank\" rel=\"noreferrer noopener\">Using histograms<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/quicksight\/latest\/user\/box-plots.html\" target=\"_blank\" rel=\"noreferrer noopener\">Using box plots<\/a>)<\/li>\n\n\n\n<li>Interpreting descriptive statistics (correlation, summary statistics, p value)<\/li>\n\n\n\n<li>Perform cluster analysis (for example, hierarchical, diagnosis, elbow plot, cluster size).<\/li>\n<\/ul>\n\n\n\n<h5 class=\"wp-block-heading\"><strong>Domain 3: Modeling (36%)<\/strong><\/h5>\n\n\n\n<p>3.1 Frame business problems as ML problems.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Determine when to use and when not to use ML&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/when-to-use-machine-learning.html\" target=\"_blank\" rel=\"noreferrer noopener\">When to Use Machine Learning<\/a>)<\/li>\n\n\n\n<li>Know the difference between supervised and unsupervised learning<\/li>\n\n\n\n<li>Select from among classification, regression, forecasting, clustering, recommendation, and foundation models.&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/k-means-clustering-with-amazon-sagemaker\/\" target=\"_blank\" rel=\"noreferrer noopener\">K-means clustering with Amazon SageMaker<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/building-a-customized-recommender-system-in-amazon-sagemaker\/\" target=\"_blank\" rel=\"noreferrer noopener\">Building a customized recommender system in Amazon SageMaker<\/a>)<\/li>\n<\/ul>\n\n\n\n<p>3.2 Select the appropriate model(s) for a given ML problem.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Xgboost, logistic regression, K-means, linear regression, decision trees, random forests, RNN, CNN, Ensemble, Transfer learning&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/xgboost.html\" target=\"_blank\" rel=\"noreferrer noopener\">XGBoost Algorithm<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/k-means-clustering-with-amazon-sagemaker\/\" target=\"_blank\" rel=\"noreferrer noopener\">K-means clustering with Amazon SageMaker<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/forecasting-time-series-with-dynamic-deep-learning-on-aws\/\" target=\"_blank\" rel=\"noreferrer noopener\">Forecasting financial time series<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/amazon-forecast-can-now-use-convolutional-neural-networks-cnns-to-train-forecasting-models-up-to-2x-faster-with-up-to-30-higher-accuracy\/\" target=\"_blank\" rel=\"noreferrer noopener\">Amazon Forecast can now use Convolutional Neural Networks<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/detecting-hidden-but-non-trivial-problems-in-transfer-learning-models-using-amazon-sagemaker-debugger\/\" target=\"_blank\" rel=\"noreferrer noopener\">Detecting hidden but non-trivial problems in transfer learning models<\/a>)<\/li>\n\n\n\n<li>Express intuition behind models<\/li>\n<\/ul>\n\n\n\n<p>3.3 Train ML models.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Split data between training and validation (for example, cross validation).&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/ex1-train-model.html\" target=\"_blank\" rel=\"noreferrer noopener\">Train a Model<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/incremental-training.html\" target=\"_blank\" rel=\"noreferrer noopener\">Incremental Training<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/model-managed-spot-training.html\" target=\"_blank\" rel=\"noreferrer noopener\">Managed Spot Training<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/how-it-works-model-validation.html\" target=\"_blank\" rel=\"noreferrer noopener\">Validate a Machine Learning Model<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/cross-validation.html\" target=\"_blank\" rel=\"noreferrer noopener\">Cross-Validation<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/autopilot-model-support-validation.html\" target=\"_blank\" rel=\"noreferrer noopener\">Model support, metrics, and validation<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/splitting-types.html\" target=\"_blank\" rel=\"noreferrer noopener\">Splitting Your Data<\/a>)<\/li>\n\n\n\n<li>Understand optimization techniques for ML training (for example, gradient descent, loss functions, convergence).<\/li>\n\n\n\n<li>Choose appropriate compute resources (for example GPU or CPU, distributed or non-distributed).\n<ul class=\"wp-block-list\">\n<li>Choose appropriate compute platforms (Spark or non-Spark).<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Update and retraining Models\u00a0<strong>(AWS Documentation:<\/strong><a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/retraining-models-on-new-data.html\" target=\"_blank\" rel=\"noreferrer noopener\">Retraining Models on New Data<\/a>,\u00a0<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/automating-model-retraining-and-deployment-using-the-aws-step-functions-data-science-sdk-for-amazon-sagemaker\/\" target=\"_blank\" rel=\"noreferrer noopener\">Automating model retraining and deployment<\/a>)\n<ul class=\"wp-block-list\">\n<li>Batch vs. real-time\/online<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p>3.4 Perform hyperparameter optimization.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Perform Regularization\u00a0<strong>(AWS Documentation:<\/strong><a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/training-parameters1.html\" target=\"_blank\" rel=\"noreferrer noopener\">Training Parameters<\/a>)\n<ul class=\"wp-block-list\">\n<li>Drop out<\/li>\n\n\n\n<li>L1\/L2<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Perform Cross validation&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/cross-validation.html\" target=\"_blank\" rel=\"noreferrer noopener\">Cross-Validation<\/a>)<\/li>\n\n\n\n<li>Model initialization<\/li>\n\n\n\n<li>Neural network architecture (layers\/nodes), learning rate, activation functions<\/li>\n\n\n\n<li>Understand tree-based models (number of trees, number of levels).<\/li>\n\n\n\n<li>Understand linear models (learning rate).<\/li>\n<\/ul>\n\n\n\n<p>3.5 Evaluate ML models.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Avoid overfitting and underfitting\n<ul class=\"wp-block-list\">\n<li>Detect and handle bias and variance&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/model-fit-underfitting-vs-overfitting.html\" target=\"_blank\" rel=\"noreferrer noopener\">Underfitting vs. Overfitting<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/aws\/new-amazon-sagemaker-clarify-detects-bias-and-increases-the-transparency-of-machine-learning-models\/\" target=\"_blank\" rel=\"noreferrer noopener\">Amazon SageMaker Clarify Detects Bias and Increases the Transparency<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/sagemaker\/clarify\/?sagemaker-data-wrangler-whats-new.sort-by=item.additionalFields.postDateTime&amp;sagemaker-data-wrangler-whats-new.sort-order=desc\" target=\"_blank\" rel=\"noreferrer noopener\">Amazon SageMaker Clarify<\/a>)<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Evaluate metrics (for example, area under curve [AUC]-receiver operating characteristics [ROC], accuracy, precision, recall, Root Mean Square Error [RMSE], F1 score).<\/li>\n\n\n\n<li>Interpret confusion matrix&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/comprehend\/latest\/dg\/cer-doc-class.html\" target=\"_blank\" rel=\"noreferrer noopener\">Custom classifier metrics<\/a>)<\/li>\n\n\n\n<li>Offline and online model evaluation (A\/B testing)&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/how-it-works-model-validation.html\" target=\"_blank\" rel=\"noreferrer noopener\">Validate a Machine Learning Model<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/wellarchitected\/latest\/machine-learning-lens\/machine-learning-lens.html\" target=\"_blank\" rel=\"noreferrer noopener\">Machine Learning Lens<\/a>)<\/li>\n\n\n\n<li>Compare models using metrics (time to train a model, quality of model, engineering costs)&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/easily-monitor-and-visualize-metrics-while-training-models-on-amazon-sagemaker\/\" target=\"_blank\" rel=\"noreferrer noopener\">Easily monitor and visualize metrics while training models<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/model-monitor-model-quality-metrics.html\" target=\"_blank\" rel=\"noreferrer noopener\">Model Quality Metrics<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/model-monitor-model-quality.html\" target=\"_blank\" rel=\"noreferrer noopener\">Monitor model quality<\/a>)<\/li>\n\n\n\n<li>Cross validation&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/cross-validation.html\" target=\"_blank\" rel=\"noreferrer noopener\">Cross-Validation<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/autopilot-model-support-validation.html\" target=\"_blank\" rel=\"noreferrer noopener\">Model support, metrics, and validation<\/a>)<\/li>\n<\/ul>\n\n\n\n<h5 class=\"wp-block-heading\"><strong>Domain 4: Machine Learning Implementation and Operations (20%)<\/strong><\/h5>\n\n\n\n<p>4.1 Build ML solutions for performance, availability, scalability, resiliency, and fault tolerance.&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/step-4-review-model-and-set-cutoff.html\" target=\"_blank\" rel=\"noreferrer noopener\">Review the ML Model\u2019s Predictive Performance<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/best-practices.html\" target=\"_blank\" rel=\"noreferrer noopener\">Best practices<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/disaster-recovery-resiliency.html\" target=\"_blank\" rel=\"noreferrer noopener\">Resilience in Amazon SageMaker<\/a>)<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Log and monitor AWS environments\u00a0<strong>(AWS Documentation:<\/strong><a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/sagemaker-incident-response.html\" target=\"_blank\" rel=\"noreferrer noopener\">Logging and Monitoring<\/a>)\n<ul class=\"wp-block-list\">\n<li>AWS CloudTrail and AWS CloudWatch&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/logging-using-cloudtrail.html\" target=\"_blank\" rel=\"noreferrer noopener\">Logging Amazon ML API Calls with AWS CloudTrail<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/logging-using-cloudtrail.html\" target=\"_blank\" rel=\"noreferrer noopener\">Log Amazon SageMaker API Calls<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/cw-doc.html\" target=\"_blank\" rel=\"noreferrer noopener\">Monitoring Amazon ML<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/monitoring-cloudwatch.html\" target=\"_blank\" rel=\"noreferrer noopener\">Monitor Amazon SageMaker<\/a>)<\/li>\n\n\n\n<li>Build error monitoring solutions&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/whitepapers\/latest\/build-secure-enterprise-ml-platform\/ml-platform-monitoring.html\" target=\"_blank\" rel=\"noreferrer noopener\">ML Platform Monitoring<\/a>)<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Deploy to multiple AWS Regions and multiple Availability Zones.&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/regions-and-endpoints.html\" target=\"_blank\" rel=\"noreferrer noopener\">Regions and Endpoints<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/best-practices.html\" target=\"_blank\" rel=\"noreferrer noopener\">Best practices<\/a>)<\/li>\n\n\n\n<li>AMI and golden image&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/dlami\/latest\/devguide\/what-is-dlami.html\" target=\"_blank\" rel=\"noreferrer noopener\">AWS Deep Learning AMI<\/a>)<\/li>\n\n\n\n<li>Docker containers&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/opensource\/why-use-docker-containers-for-machine-learning-development\/\" target=\"_blank\" rel=\"noreferrer noopener\">Why use Docker containers for machine learning development<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/docker-containers.html\" target=\"_blank\" rel=\"noreferrer noopener\">Using Docker containers with SageMaker<\/a>)<\/li>\n\n\n\n<li>Deploy Auto Scaling groups&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/endpoint-auto-scaling.html\" target=\"_blank\" rel=\"noreferrer noopener\">Automatically Scale Amazon SageMaker Models<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/configuring-autoscaling-inference-endpoints-in-amazon-sagemaker\/\" target=\"_blank\" rel=\"noreferrer noopener\">Configuring autoscaling inference endpoints<\/a>)<\/li>\n\n\n\n<li>Rightsizing resources, for example:\n<ul class=\"wp-block-list\">\n<li>Instances&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/ensure-efficient-compute-resources-on-amazon-sagemaker\/\" target=\"_blank\" rel=\"noreferrer noopener\">Ensure efficient compute resources on Amazon SageMaker<\/a>)<\/li>\n\n\n\n<li>Provisioned IOPS&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/optimizing-i-o-for-gpu-performance-tuning-of-deep-learning-training-in-amazon-sagemaker\/\" target=\"_blank\" rel=\"noreferrer noopener\">Optimizing I\/O for GPU performance tuning of deep learning<\/a>)<\/li>\n\n\n\n<li>Volumes&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/customize-your-notebook-volume-size-up-to-16-tb-with-amazon-sagemaker\/\" target=\"_blank\" rel=\"noreferrer noopener\">Customize your notebook volume size, up to 16 TB<\/a>)<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Load balancing&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/managing-your-machine-learning-lifecycle-with-mlflow-and-amazon-sagemaker\/\" target=\"_blank\" rel=\"noreferrer noopener\">Managing your machine learning lifecycle<\/a>)<\/li>\n\n\n\n<li>AWS best practices&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/machine-learning-best-practices-in-financial-services\/\" target=\"_blank\" rel=\"noreferrer noopener\">Machine learning best practices in financial services<\/a>)<\/li>\n<\/ul>\n\n\n\n<p>4.2 Recommend and implement the appropriate ML services and features for a given problem.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>ML on AWS (application services)\n<ul class=\"wp-block-list\">\n<li>Amazon Poly&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/aws.amazon.com\/polly\/?nc=sn&amp;loc=1\" target=\"_blank\" rel=\"noreferrer noopener\">Amazon Polly<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/build-a-unique-brand-voice-with-amazon-polly\/\" target=\"_blank\" rel=\"noreferrer noopener\">Build a unique Brand Voice with Amazon Polly<\/a>)<\/li>\n\n\n\n<li>Amazon Lex&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/lex\/latest\/dg\/what-is.html\" target=\"_blank\" rel=\"noreferrer noopener\">Amazon Lex<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/build-more-effective-conversations-on-amazon-lex-with-confidence-scores-and-increased-accuracy\/\" target=\"_blank\" rel=\"noreferrer noopener\">Build more effective conversations on Amazon Lex<\/a>)<\/li>\n\n\n\n<li>Amazon Transcribe&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/aws.amazon.com\/transcribe\/?nc=sn&amp;loc=1\" target=\"_blank\" rel=\"noreferrer noopener\">Amazon Transcribe<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/transcribe-speech-to-text-in-real-time-using-amazon-transcribe-with-websocket\/\" target=\"_blank\" rel=\"noreferrer noopener\">Transcribe speech to text in real time<\/a>)<\/li>\n\n\n\n<li>Amazon Q<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Understand AWS service quotas&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/general\/latest\/gr\/sagemaker.html\" target=\"_blank\" rel=\"noreferrer noopener\">Amazon SageMaker endpoints and quotas<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/general\/latest\/gr\/machinelearning.html\" target=\"_blank\" rel=\"noreferrer noopener\">Amazon Machine Learning endpoints and quotas<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/system-limits.html\" target=\"_blank\" rel=\"noreferrer noopener\">System Limits<\/a>)<\/li>\n\n\n\n<li>Determine when to build custom models and when to use Amazon SageMaker built-in algorithms.<\/li>\n\n\n\n<li>Understand AWS infrastructure (for example, instance types) and cost considerations.\n<ul class=\"wp-block-list\">\n<li>Using spot instances to train deep learning models using AWS Batch&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/train-deep-learning-models-on-gpus-using-amazon-ec2-spot-instances\/\" target=\"_blank\" rel=\"noreferrer noopener\">Train Deep Learning Models on GPUs<\/a>)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p>4.3 Apply basic AWS security practices to ML solutions.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>AWS Identity and Access Management (IAM)&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/controlling-access-to-amazon-ml-resources-by-using-iam.html\" target=\"_blank\" rel=\"noreferrer noopener\">Controlling Access to Amazon ML Resources<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/deep-learning-containers\/latest\/devguide\/security-iam.html\" target=\"_blank\" rel=\"noreferrer noopener\">Identity and Access Management in AWS Deep Learning Containers<\/a>)<\/li>\n\n\n\n<li>S3 bucket policies&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/using-amazon-s3-with-amazon-ml.html\" target=\"_blank\" rel=\"noreferrer noopener\">Using Amazon S3 with Amazon ML<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/granting-amazon-ml-permissions-to-read-your-data-from-amazon-s3.html\" target=\"_blank\" rel=\"noreferrer noopener\">Granting Amazon ML Permissions to Read Your Data from Amazon S3<\/a>)<\/li>\n\n\n\n<li>Security groups&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/part-1-secure-multi-account-model-deployment-with-amazon-sagemaker\/\" target=\"_blank\" rel=\"noreferrer noopener\">Secure multi-account model deployment with Amazon SageMaker<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/AWSEC2\/latest\/UserGuide\/efa-start-nccl-dlami.html\" target=\"_blank\" rel=\"noreferrer noopener\">Use an AWS Deep Learning AMI<\/a>)<\/li>\n\n\n\n<li>VPC&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/securing-amazon-sagemaker-studio-connectivity-using-a-private-vpc\/\" target=\"_blank\" rel=\"noreferrer noopener\">Securing Amazon SageMaker Studio connectivity<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/direct-access-to-amazon-sagemaker-notebooks-from-amazon-vpc-by-using-an-aws-privatelink-endpoint\/\" target=\"_blank\" rel=\"noreferrer noopener\">Direct access to&nbsp;Amazon SageMaker notebooks<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/building-secure-machine-learning-environments-with-amazon-sagemaker\/\" target=\"_blank\" rel=\"noreferrer noopener\">Building secure machine learning environments<\/a>)<\/li>\n\n\n\n<li>Encryption and anonymization&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/encryption-at-rest.html\" target=\"_blank\" rel=\"noreferrer noopener\">Protect Data at Rest Using Encryption<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/encryption-in-transit.html\" target=\"_blank\" rel=\"noreferrer noopener\">Protecting Data in Transit with Encryption<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/big-data\/anonymize-and-manage-data-in-your-data-lake-with-amazon-athena-and-aws-lake-formation\/\" target=\"_blank\" rel=\"noreferrer noopener\">Anonymize and manage data in your data lake<\/a>)<\/li>\n<\/ul>\n\n\n\n<p>4.4 Deploy and operationalize ML solutions.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Exposing endpoints and interacting with them&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/creating-a-machine-learning-powered-rest-api-with-amazon-api-gateway-mapping-templates-and-amazon-sagemaker\/\" target=\"_blank\" rel=\"noreferrer noopener\">Creating a machine learning-powered REST API<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/call-an-amazon-sagemaker-model-endpoint-using-amazon-api-gateway-and-aws-lambda\/\" target=\"_blank\" rel=\"noreferrer noopener\">Call an Amazon SageMaker model endpoint<\/a>)<\/li>\n\n\n\n<li>Understand ML models.<\/li>\n\n\n\n<li>A\/B testing&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/a-b-testing-ml-models-in-production-using-amazon-sagemaker\/\" target=\"_blank\" rel=\"noreferrer noopener\">A\/B Testing ML models in production<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/dynamic-a-b-testing-for-machine-learning-models-with-amazon-sagemaker-mlops-projects\/\" target=\"_blank\" rel=\"noreferrer noopener\">Dynamic A\/B testing for machine learning models<\/a>)<\/li>\n\n\n\n<li>Retrain pipelines&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/automating-model-retraining-and-deployment-using-the-aws-step-functions-data-science-sdk-for-amazon-sagemaker\/\" target=\"_blank\" rel=\"noreferrer noopener\">Automating model retraining and deployment<\/a>,&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/wellarchitected\/latest\/machine-learning-lens\/machine-learning-lens.html\" target=\"_blank\" rel=\"noreferrer noopener\">Machine Learning Lens<\/a>)<\/li>\n\n\n\n<li>Debug and troubleshoot ML models\u00a0<strong>(AWS Documentation:<\/strong><a href=\"https:\/\/aws.amazon.com\/blogs\/aws\/amazon-sagemaker-debugger-debug-your-machine-learning-models\/\" target=\"_blank\" rel=\"noreferrer noopener\">Debug Your Machine Learning Models<\/a>,\u00a0<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/analyzing-open-source-ml-pipeline-models-in-real-time-using-amazon-sagemaker-debugger\/\" target=\"_blank\" rel=\"noreferrer noopener\">Analyzing open-source ML pipeline models in real time<\/a>,\u00a0<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/deploy-model-troubleshoot.html\" target=\"_blank\" rel=\"noreferrer noopener\">Troubleshoot Amazon SageMaker model deployments<\/a>)\n<ul class=\"wp-block-list\">\n<li>Detect and mitigate drop in performance&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/identify-bottlenecks-improve-resource-utilization-and-reduce-ml-training-costs-with-the-new-profiling-feature-in-amazon-sagemaker-debugger\/\" target=\"_blank\" rel=\"noreferrer noopener\">Identify bottlenecks, improve resource utilization, and reduce ML training costs<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/optimizing-i-o-for-gpu-performance-tuning-of-deep-learning-training-in-amazon-sagemaker\/\" target=\"_blank\" rel=\"noreferrer noopener\">Optimizing I\/O for GPU performance tuning of deep learning training<\/a>)<\/li>\n\n\n\n<li>Monitor performance of the model&nbsp;<strong>(AWS Documentation:<\/strong>&nbsp;<a href=\"https:\/\/docs.aws.amazon.com\/sagemaker\/latest\/dg\/model-monitor.html\" target=\"_blank\" rel=\"noreferrer noopener\">Monitor models for data and model quality, bias, and explainability<\/a>,&nbsp;<a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/monitoring-in-production-ml-models-at-large-scale-using-amazon-sagemaker-model-monitor\/\" target=\"_blank\" rel=\"noreferrer noopener\">Monitoring in-production ML models at large scale<\/a>)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p><strong><em>To know more details about the Exam, visit AWS <a href=\"https:\/\/www.testpreptraining.ai\/tutorial\/aws-machine-learning-specialty-exam\/\" target=\"_blank\" rel=\"noreferrer noopener\">Machine Learning Specialty Exam Tutorials. <\/a><\/em><\/strong><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>How difficult is the AWS Machine Learning Specialty Exam?<\/strong><\/h3>\n\n\n\n<p>Even if you don&#8217;t have the minimum years of experience, you can still get the ML &#8211; Specialty certification. The test, however, is not a standard AWS certification that simply asks questions on AWS-related services; it also asks a lot of questions about DS. Preparing for the AWS Machine Learning Expertise test is a demanding endeavor that requires a lot of devotion and hard effort, as well as the necessary tools. There are several study and mock test materials accessible online, some of which contain the SAME questions that will be asked on your exam! The vast majority of them actually cover a significant portion of the test material. So keep practicing until you&#8217;re quite certain you can answer the questions on the mock examinations.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><a href=\"https:\/\/www.testpreptraining.ai\/tutorial\/aws-machine-learning-specialty-exam\/\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" width=\"961\" height=\"150\" src=\"https:\/\/www.testpreptraining.ai\/blog\/wp-content\/uploads\/2021\/12\/How-hard-is-the-AWS-Machine-Learning-Specialty-Exam-1-1.png\" alt=\"tutorials\" class=\"wp-image-22230\" srcset=\"https:\/\/www.testpreptraining.ai\/blog\/wp-content\/uploads\/2021\/12\/How-hard-is-the-AWS-Machine-Learning-Specialty-Exam-1-1.png 961w, https:\/\/www.testpreptraining.ai\/blog\/wp-content\/uploads\/2021\/12\/How-hard-is-the-AWS-Machine-Learning-Specialty-Exam-1-1-300x47.png 300w\" sizes=\"(max-width: 961px) 100vw, 961px\" \/><\/a><\/figure>\n<\/div>\n\n\n<p><em>Let us now jump to the resources that you can use for the preparation for this exam. <\/em><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Learning resources for the exam<\/strong><\/h3>\n\n\n\n<p>There are a lot of resources out there, but we need to figure out which ones will be useful to us. We can obtain more in less time thanks to the resources. This will allow you to have more time for practise and corrections. Let&#8217;s have a look at some resources that will help you pass the exam with flying colors:<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" width=\"750\" height=\"400\" src=\"https:\/\/www.testpreptraining.ai\/blog\/wp-content\/uploads\/2021\/12\/How-hard-is-the-AWS-Machine-Learning-Specialty-Exam-3.png\" alt=\"\" class=\"wp-image-22231\" srcset=\"https:\/\/www.testpreptraining.ai\/blog\/wp-content\/uploads\/2021\/12\/How-hard-is-the-AWS-Machine-Learning-Specialty-Exam-3.png 750w, https:\/\/www.testpreptraining.ai\/blog\/wp-content\/uploads\/2021\/12\/How-hard-is-the-AWS-Machine-Learning-Specialty-Exam-3-300x160.png 300w\" sizes=\"(max-width: 750px) 100vw, 750px\" \/><\/figure>\n<\/div>\n\n\n<h4 class=\"wp-block-heading\"><strong>1. AWS Machine Learning Documentation<\/strong> <\/h4>\n\n\n\n<p>For studying for the AWS Certified Machine Learning Specialty test, the official documentation from AWS is a useful resource. The AWS official material is a good resource for understanding the numerous sub-topics necessary for the machine learning speciality certification test. Data splitting, machine learning model types, and data transformations are examples of Amazon machine learning concepts that have documentation. Reading Materials for the AWS Machine Learning Specialty Exam &#8211;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/amazon-machine-learning-key-concepts.html\" target=\"_blank\" rel=\"noreferrer noopener\">Concepts of Amazon Machine Learning<\/a><\/li>\n\n\n\n<li>Also, <a href=\"https:\/\/aws.amazon.com\/machine-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\">Functionality of Machine Learning on AWS<\/a><\/li>\n\n\n\n<li>Furthermore, <a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/types-of-ml-models.html\" target=\"_blank\" rel=\"noreferrer noopener\">Machine Learning Models<\/a><\/li>\n\n\n\n<li>Additionally, <a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/splitting-types.html\" target=\"_blank\" rel=\"noreferrer noopener\">Splitting of Data<\/a><\/li>\n\n\n\n<li>Also, <a href=\"https:\/\/docs.aws.amazon.com\/machine-learning\/latest\/dg\/splitting-types.html\" target=\"_blank\" rel=\"noreferrer noopener\">Concept of Data Transformations<\/a><\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>2. AWS Machine Learning Specialty References<\/strong> <\/h4>\n\n\n\n<p>There are numerous references for the AWS Machine Learning Specialty exam available both online and offline. However, many websites offer online exam preparation with full course support, such as Simplilearn, Testprep training, Pluralsight, and Udemy.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>3. Discussion Forums<\/strong> <\/h4>\n\n\n\n<p>Numerous websites provide useful information and also topic specifics about the certification. Additionally, This can be useful if you have any questions or want to learn more about the exam. Answers.com, Quora, and Stackoverflow are a few examples.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>4.Training at AWS<\/strong> <\/h4>\n\n\n\n<p>The AWS Machine Learning Certification Training exam is available at <a href=\"https:\/\/aws.amazon.com\/training\/\">https:\/\/aws.amazon.com\/training\/<\/a>. Furthermore, these training require registration and are free of charge. Also, To learn more about AWS services, you can access a variety of Learning libraries.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>5. Practice Exams<\/strong><\/h4>\n\n\n\n<p>The AWS Machine Learning Certification Practice Exam is everything you&#8217;ll need to double-check your preparations. To increase speed and preparedness, utilize practice sets of questions. Some websites provide practice exams and also assess you depending on your AWS cloud skills and expertise. Practice Sets are also available on Amazon, albeit not all topics will be covered. A huge number of practice sets of questions for the <a href=\"https:\/\/www.testpreptraining.ai\/aws-certified-machine-learning-specialty-free-practice-test\" target=\"_blank\" rel=\"noreferrer noopener\">AWS Machine Learning Specialty exam<\/a> are also available from Testprep Training.<\/p>\n\n\n\n<p>All you need to check your preparations is the AWS Machine Learning Certification Practice Exam. Practice sets of questions can be used to improve speed and preparation. Some websites offer practice tests and also validate you based on your skills and knowledge of the AWS cloud. You can also look for practice sets on Amazon, though not all topics will be covered. Moreover, Testprep Training provides a large number of practice sets of questions for the<a href=\"https:\/\/www.testpreptraining.ai\/aws-certified-machine-learning-specialty-free-practice-test\" target=\"_blank\" rel=\"noreferrer noopener\"> <\/a><\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" width=\"961\" height=\"150\" src=\"https:\/\/www.testpreptraining.ai\/blog\/wp-content\/uploads\/2021\/12\/How-hard-is-the-AWS-Machine-Learning-Specialty-Exam-2.png\" alt=\"exam practice tests\" class=\"wp-image-22232\" srcset=\"https:\/\/www.testpreptraining.ai\/blog\/wp-content\/uploads\/2021\/12\/How-hard-is-the-AWS-Machine-Learning-Specialty-Exam-2.png 961w, https:\/\/www.testpreptraining.ai\/blog\/wp-content\/uploads\/2021\/12\/How-hard-is-the-AWS-Machine-Learning-Specialty-Exam-2-300x47.png 300w\" sizes=\"(max-width: 961px) 100vw, 961px\" \/><\/figure>\n<\/div>\n\n\n<h3 class=\"wp-block-heading\"><strong>Expert Advice<\/strong><\/h3>\n\n\n\n<p>Professionals may use the AWS Machine Learning Specialty test to further their careers and gain access to new opportunities. However, it is essential that you concentrate on grasping test subjects and properly preparing for the exam in order to achieve this. It&#8217;s important to practise and get into the right mindset for this. So, best of luck in passing the exam.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The AWS Machine Learning Specialty Exam is a certification exam offered by Amazon Web Services (AWS) that validates an individual&#8217;s expertise in designing, implementing, deploying, and maintaining machine learning (ML) solutions on the AWS platform. The exam is intended for individuals who have a solid understanding of ML concepts, can use AWS services for ML&#8230;<\/p>\n","protected":false},"author":7,"featured_media":22228,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[31],"tags":[7,9,199,825,1051,1052,3819],"class_list":["post-22216","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-aws","tag-aws","tag-aws-certification","tag-aws-machine-learning-specialty","tag-aws-machine-learning-specialty-exam","tag-aws-machine-learning-specialty-exam-exam-guide","tag-aws-machine-learning-specialty-exam-exam-resources","tag-how-hard-is-the-aws-machine-learning-specialty-exam"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How hard is the AWS Machine Learning Specialty Exam? - Blog<\/title>\n<meta name=\"description\" content=\"Learn about How hard is the AWS Machine Learning Specialty Exam! 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