Brief Introduction
Prepare well for your AWS Certified Machine Learning Specialty certification by taking this practice exam!Description
Realistic practice exam based on the most recent AWS Certified Machine Learning Specialty exam.
Just like the actual exam this practice test has 65 questions and takes 170 minutes.
Questions are mapped based on the actual exam domains:
Data Engineering
Exploratory Data Analysis
Modeling
Machine Learning Implementation and Operations.
Suggested background knowledge
Data Engineering
AWS services:
Glue, EMR ( Apache Spark, Hive metastore), Athena
Kinesis family (Streams, data analytics, firehose, video streams)
S3, QuickSight
Data/File formats (Avro, Parquet, CSV, protobuf recordIO)
Exploratory Data Analysis
Handling missing values (Imputation: median, mean, most frequent, using ML model)
Feature scaling
Feature engineering
Handling outliers
One-hot encoding
Binning
Modeling
supervised machine learning ( Classification and Regression Algorithms)
unsupervised machine learning ( K-Means clustering, PCA)
Hyperparameter tuning ( supervised machine learning, deep learning)
Performance metrics ( accuracy, RMSE, F1 score, AUC, ROC, Precision, Recall)
Tuning deep learning networks ( how to prevent overfitting)
AWS services: SageMaker built-in algorithms, Lex, Polly, Transcribe, Translate, Comprehend
ML Implementation and Operations
Amazon SageMaker train and deploy a model
Inference pipeline, batch transform, inference endpoints , production variants, hosting services
Amazon SageMaker security (data encryption at rest and in transit)
Distributed training on Amazon SageMaker ( Using GPUs)
AWS SageMaker roles
Bring your own model container ( e.g. developed using scikit-learn)
Customize SageMaker built-in algorithm containers
How to develop and deploy deep learning models on frameworks such as Tensoflow, MXNet
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