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AWS Machine Learning Specialty Exam Syllabus Topics:
| Section | Objectives |
|---|---|
Data Engineering - 20% | |
| Create data repositories for machine learning. | - Identify data sources (e.g., content and location, primary sources such as user data) - Determine storage mediums (e.g., DB, Data Lake, S3, EFS, EBS) |
| Identify and implement a data ingestion solution. | - Data job styles/types (batch load, streaming)
- Data ingestion pipelines (Batch-based ML workloads and streaming-based ML workloads) |
| Identify and implement a data transformation solution. | - Transforming data transit (ETL: Glue, EMR, AWS Batch) - Handle ML-specific data using map reduce (Hadoop, Spark, Hive) |
Exploratory Data Analysis - 24% | |
| Sanitize and prepare data for modeling. | - Identify and handle missing data, corrupt data, stop words, etc. - Formatting, normalizing, augmenting, and scaling data - Labeled data (recognizing when you have enough labeled data and identifying mitigation strategies [Data labeling tools (Mechanical Turk, manual labor)]) |
| Perform feature engineering. | - Identify and extract features from data sets, including from data sources such as text, speech, image, public datasets, etc. - Analyze/evaluate feature engineering concepts (binning, tokenization, outliers, synthetic features, 1 hot encoding, reducing dimensionality of data) |
| Analyze and visualize data for machine learning. | - Graphing (scatter plot, time series, histogram, box plot) - Interpreting descriptive statistics (correlation, summary statistics, p value) - Clustering (hierarchical, diagnosing, elbow plot, cluster size) |
Modeling - 36% | |
| Frame business problems as machine learning problems. | - Determine when to use/when not to use ML - Know the difference between supervised and unsupervised learning - Selecting from among classification, regression, forecasting, clustering, recommendation, etc. |
| Select the appropriate model(s) for a given machine learning problem. | - Xgboost, logistic regression, K-means, linear regression, decision trees, random forests, RNN, CNN, Ensemble, Transfer learning - Express intuition behind models |
| Train machine learning models. | - Train validation test split, cross-validation - Optimizer, gradient descent, loss functions, local minima, convergence, batches, probability, etc. - Compute choice (GPU vs. CPU, distributed vs. non-distributed, platform [Spark vs. non-Spark]) - Model updates and retraining
|
| Perform hyperparameter optimization. | - Regularization
- Cross validation |
| Evaluate machine learning models. | - Avoid overfitting/underfitting (detect and handle bias and variance) - Metrics (AUC-ROC, accuracy, precision, recall, RMSE, F1 score) - Confusion matrix - Offline and online model evaluation, A/B testing - Compare models using metrics (time to train a model, quality of model, engineering costs) - Cross validation |
Machine Learning Implementation and Operations - 20% | |
| Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance. | - AWS environment logging and monitoring
- Multiple regions, Multiple AZs
- Load balancing |
| Recommend and implement the appropriate machine learning services and features for a given problem. | - ML on AWS (application services)
- AWS service limits
|
| Apply basic AWS security practices to machine learning solutions. | - IAM - S3 bucket policies - Security groups - VPC - Encryption/anonymization |
| Deploy and operationalize machine learning solutions. | - Exposing endpoints and interacting with them - ML model versioning - A/B testing - Retrain pipelines - ML debugging/troubleshooting
|
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To become an AWS Certified Machine Learning - Specialty, candidates must pass a two-hour, multiple-choice exam that consists of 65 questions. MLS-C01-JPN exam is designed to test the candidate's knowledge and skills in machine learning theory, as well as their practical experience in deploying machine learning models on AWS. Candidates must score at least 750 out of a possible 1000 points to pass the exam.
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Read the registration procedure of the AWS Certified Machine Learning Specialty Exam
In order to apply for the AWS Certified Machine Learning Specialty, You have to follow these steps
- Step 1: Sign in to AWS Training
- Step 2: Click Certification in the top navigation
- Step 3: Click AWS Certification Account Button
- Step 4: Followed by Schedule New Exam
- Step 5: Search the AWS Certified Machine Learning Specialty exam
- Step 6: Click either the Schedule at PSI or Schedule at Pearson VUE button
- Step 7: Select Date, time and Schedule your test
The AWS Certified Machine Learning - Specialty (MLS-C01日本語版) certification exam covers a variety of topics, including data engineering, data preprocessing, modeling, deep learning, and deployment. Candidates will be tested on their ability to understand and use various AWS services, such as Amazon SageMaker, AWS Lambda, AWS Glue, and AWS Kinesis, among others. They will also need to demonstrate their expertise in designing and implementing machine learning algorithms, as well as their ability to troubleshoot and optimize machine learning models.
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Amazon MLS-C01日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Modeling | 36% | - Deep learning concepts - Supervised and unsupervised learning - Algorithm selection and evaluation - Hyperparameter tuning |
| Topic 2: Data Engineering | 20% | - Feature engineering pipelines - Data preprocessing and transformation - Data storage and data ingestion |
| Topic 3: Exploratory Data Analysis (EDA) | 24% | - Data visualization and statistical analysis - Feature correlation and selection - Data quality assessment |
| Topic 4: Machine Learning Implementation and Operations | 20% | - ML pipeline deployment on AWS - CI/CD for ML workflows - Amazon SageMaker usage and operations - Model monitoring and maintenance |
