Pattern of the Exam
MLS-C01 test contains questions in the form of multiple choice (with only one correct option) and multiple answer (more than 1 correct response). The candidates will get 180 minutes to finish the exam. Also, they need to pay $300 for registration. They can also choose preferred language from the options such as English, Simplified Chinese, Korean, and Japanese. Finally, one has the opportunity of taking the exam online or in a testing center. Topics covered include ML operations as well as implementation, exploratory data analysis, data engineering, and modeling.
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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Preparation Process
Many useful resources are available for the Amazon MLS-C01 exam. Let’s take a closer look at them.
- AWS Machine Learning Documentation
The official AWS Machine Learning Documentation is one of the best preparation guides for the Amazon AWS Certified Machine Learning – Specialty exam. It is a great material to learn all the topics included in the exam content. You can find documentation, such as splitting data, data transformation, as well as types of data.
- Discussion Forums
Besides the official training options offered by the vendor, many websites provide the candidates with the topic details and useful information related to the exam as well as the certification. Some of these sites include Quora and ExamLabs.com.
- AWS Training Courses
The vendor offers the official Amazon AWS Machine Learning Certification Training, which you can access by visiting the Amazon website. It is good to know that these training courses are usually available as free.
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