AWS Certified Machine Learning Specialty Sample Exam Questions
- We are offering 334 latest real AWS Certified Machine Learning Specialty Exam Questions for practice, which will help you to score higher in your exam.
- Aim for above 85% or above in our mock exams before giving the main exam.
- Do review wrong & right answers and thoroughly go through explanations provided to each question which will help you understand the question.
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The AWS Certified Machine Learning Specialty Exam Questions 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.
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
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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