AWS Certified Machine Learning Specialty || 150+ Unique Ques

Format
Multiple choice, multiple answer

Type
Specialty

Delivery Method
Testing center or online proctored exam

Time
180 minutes to complete the exam

Cost
300 USD (Practice exam: 40 USD)

Language
Available in English, Japanese, Korean, and Simplified Chinese

The AWS Certified Machine Learning - Specialty certification is intended for individuals who perform a development or data science role. It validates a candidate's ability to design, implement, deploy, and maintain machine learning (ML) solutions for given business problems.

This course is structured into the four domains tested by this exam: data engineering, exploratory data analysis, modeling, and machine learning implementation and operations. Just some of the topics we'll cover include:

• S3 data lakes

• AWS Glue and Glue ETL

• Kinesis data streams, firehose, and video streams

• DynamoDB

• Data Pipelines, AWS Batch, and Step Functions

• Using scikit_learn

• Data science basics

• Athena and Quicksight

• Elastic MapReduce (EMR)

• Apache Spark and MLLib

• Feature engineering (imputation, outliers, binning, transforms, encoding, and normalization)

• Ground Truth

• Deep Learning basics

• Tuning neural networks and avoiding overfitting

• Amazon SageMaker, in depth

• Regularization techniques

• Evaluating machine learning models (precision, recall, F1, confusion matrix, etc.)

• High-level ML services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition, and more

• Security best practices with machine learning on AWS

  
  

Abilities Validated by the Certification

• Select and justify the appropriate ML approach for a given business problem

• Identify appropriate AWS services to implement ML solutions

• Design and implement scalable, cost-optimized, reliable, and secure ML solutions

Recommended Knowledge and Experience

• 1-2 years of experience developing, architecting, or running ML/deep learning workloads on the AWS Cloud

• The ability to express the intuition behind basic ML algorithms

• Experience performing basic hyperparameter optimization

• Experience with ML and deep learning frameworks

• The ability to follow model-training best practices

• The ability to follow deployment and operational best practices