AWS Machine Learning Certification Exam | Complete Guide

Explore Amazon S3 storage tiers, from S3 standard and intelligent-tiering to S3 Glacier, and learn how lifecycle policies automate transitions and expirations to optimize cost and data retention.

Explore Amazon S3 security within the shared security model, covering data protection, durability, versioning, server-side and in-transit encryption, Macie, and access controls via resource-based and user-based policies.

Explore monitoring and logging for Amazon S3 with CloudWatch alarms, CloudTrail logs, S3 access logs, and Trusted Advisor recommendations, plus network security via VPC endpoints, tagging, and Macie guidance.

Explore the data engineering domain of the AWS machine learning certification exam (20% weight), covering data migration with AWS Glue, pipeline, step, and batch, plus data streaming with Kinesis.

Explore how AWS Glue crawlers scan S3 to build a data catalog, enabling Athena, Redshift, and Quicksight queries and visualizations with built-in and machine learning transformations.

Explore AWS Kinesis for data streaming, ingestion, and real-time analytics. The section covers Kinesis overview and its four services—video streams, data streams, firehose, and analytics—plus practical use cases.

Learn AWS Kinesis data streams basics: producers, data streams, shards, partition keys, immutable records, 24-hour retention (up to seven days), shard capacity, and CPL, KCL, or SDK API.

Amazon Kinesis Firehose enables near real-time, serverless loading of streaming data into S3, Redshift, Elasticsearch, and Splunk with optional Lambda transformations.

Explore AWS Kinesis Data Analytics for real-time streaming insights. Build end-to-end pipelines from Kinesis Data Streams or Firehose to analytics, using SQL templates or Java libraries, with serverless, pay-per-use scalability.

EMR, a scalable AWS big data platform, runs Apache Spark, Apache Hive, Apache Flink, and Presto on elastic clusters with S3 storage, Jupyter notebooks, autoscaling, and strong security.

Explore elastic mapreduce fundamentals, spot instances for cost savings, and EMR storage options like HDFS and EMRFS with S3, plus EMR notebooks and security.

Leverage Apache Hadoop on Amazon EMR to process data with MapReduce, Yarn, and HDFS, using EMRFS to store on S3. Enable analytics and ETL with Hive and Spark on AWS.

Explore feature engineering within exploratory data analysis for the AWS machine learning certification. Master techniques like one-hot encoding, scaling, imputation, and text features such as tf-idf and bag of words.

Explore imputation of missing values in real datasets like Titanic, using mean, median, and mode replacements, selective column drops, feature engineering, and deep learning methods including Deep AR for accuracy.

Explore activation functions used in neural networks, including sigmoid for probability outputs, relu to avoid vanishing gradients in hidden layers, and hyperbolic tangent as a zero centered alternative.

Learn how supervised neural network training uses labeled data, iterates over epochs to minimize error and achieve generalization, with training, validation, and testing splits.

Explore the difference between parameters and hyperparameters, focusing on learning rate and batch size; learn how gradient descent seeks the global minimum and how batch size influences training dynamics.

Learn the differences between underfitting and overfitting, how complexity and training affect performance, and how to build a generalized model for training and testing data.

Explore artificial neural networks, convolutional and recurrent networks, and LSTM to address vanishing gradients, then assess models with cross-validation, transfer learning, and ensemble methods for the AWS certification exam.

Explore architectures of artificial neural networks, including feedforward and convolutional nets, with recurrent variants for temporal data. Learn about backpropagation during training and LSTM/GRU approaches.

Explore how long short-term memory networks overcome the vanishing gradient with a memory cell and gates. Understand forget, input, and output gates and training via backpropagation through time.

Explore how the confusion matrix assesses model performance, identify true/false positives and negatives, type I/II errors, and metrics like accuracy, misclassification rate, precision, and recall.

Transfer learning reuses a pretrained network to tackle a new task and dataset, freezing base layers or retraining with a small learning rate, especially with limited data or resources.

Learn how k-fold cross validation assesses a model's generalization for AWS machine learning certification by training on k-1 folds and testing on the remaining fold across shuffled folds.

Introduce the modeling section of the AWS machine learning certification course, highlighting SageMaker, built-in algorithms, and deep learning, plus topics like object detection and image classification.

Master deep learning on AWS using EC2 with deep learning AMIs preinstalled with MXNet, TensorFlow, Keras, and more on GPU instances; learn EMR for big data processing with dynamic resizing.

Learn how SageMaker object detection draws bounding boxes, identifies objects with confidence scores, and uses vgg or resnet with ssd, transfer learning, and image augmentation for training and inference.

Explore image classification as a supervised, multi-class task without bounding boxes, powered by ResNet and transfer learning, with options for full training or fine-tuning on ImageNet-pretrained models.

Explore the linear learner, a supervised learning algorithm that fits a line for regression and supports classification. Learn pre-processing, SGD optimization, hyperparameters, and evaluation metrics used for training and inference.