AWS Certified Machine Learning Exam dumps & Questions 2024

Published: 08 February 2022
on channel: SkillCertPro e-Learning
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AWS Certified Machine Learning Exam dumps & Questions 2024 [ new practice tests]
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It is recommended to have below knowledge when attempting AWS Certified Machine Learning Specialty Exam Questions
Data Engineering (20%): S3 (and VPC Endpoint Gateway), Kinesis (Streams, FireHose, Data Analytics, Video), Glue (Data Catalog and Crawler), Athena, AWS Data Stores (Redshift, RDS/Aurora, DynamoDB, ElasticSearch, ElastiCache), AWS Data Pipelines, AWS Batch, AWS DMS, and AWS Step Functions.
Exploratory Data Analysis (24%): Data Types and Distribution, Time Series, Amazon Athena, Quicksight, Ground Truth, EMR, Spark, Data binning, Transforming, Encoding, Scaling and Shuffling, Dealing with Missing data, Imbalanced data, and Outliers.
Modeling (36%): CNN, RNN, Tuning neural networks, Regularization, Gradient descent method, L1 and L2 regularization, Confusion matrix (Precision, Recall, F1, AUC), Ensemble methods (Bagging and Boosting), Amazon Sagemaker, Amazon Algorithms (Linear Learner, XGBoost, Seq2Seq, BlazingText, DeepAR, Object2Vec, ObjectDetection, Image Classification, Semantic Segmentation, RCF, LDA, KNN, K-Means, PCA, Factorization Machine), and Amazon AI Services (Comprehend, Translate, Transcribe, Polly, Rekognition, Forecast, Lex etc).
ML Implementations and Operations (20%): SageMaker Production Variants, Neo, IoT Greengrass, Encryption at Rest and in Transit, VPC, IAM, Logging, Monitoring, Instance Types and Spot Instances, Elastic Inference, Auto-Scaling, Availability Zones, Inference Pipelines etc
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


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