The AWS MLS-C01 exam preparation guide is designed to provide candidates with necessary information about the Machine Learning Specialty exam. It includes exam summary, sample questions, practice test, objectives and ways to interpret the exam objectives to enable candidates to assess the types of questions-answers that may be asked during the AWS Certified Machine Learning - Specialty exam.
It is recommended for all the candidates to refer the MLS-C01 objectives and sample questions provided in this preparation guide. The AWS Machine Learning Specialty certification is mainly targeted to the candidates who want to build their career in Specialty domain and demonstrate their expertise. We suggest you to use practice exam listed in this cert guide to get used to with exam environment and identify the knowledge areas where you need more work prior to taking the actual AWS Certified Machine Learning - Specialty exam.
AWS MLS-C01 Exam Summary:
| Exam Name | AWS Certified Machine Learning - Specialty (Machine Learning Specialty) |
| Exam Code | MLS-C01 |
| Exam Price | $300 USD |
| Duration | 180 minutes |
| Number of Questions | 65 |
| Passing Score | 750 / 1000 |
| Recommended Training / Books |
Practical Data Science with Amazon SageMaker The Machine Learning Pipeline on AWS Deep Learning on AWS |
| Schedule Exam | PEARSON VUE |
| Sample Questions | AWS MLS-C01 Sample Questions |
| Recommended Practice | AWS Certified Machine Learning - Specialty Practice Test |
AWS Machine Learning Specialty Syllabus:
| 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)
- Job scheduling |
| 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
|