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There are many books created to help candidates learn the exam topics. Still, only a few allow one to do this quickly and effectively. One of the most relevant study guides is “Machine Learning In The AWS Cloud” by Abhishek Mishra. You can easily buy it from Amazon and start your preparation as soon as possible. This is the perfect material for applicants to understand most common machine learning practices in the AWS cloud. It enhances one’s capabilities to perform well in the exam and in the workplace since it contains real-world practical examples and classification of business problems.
Candidates are expected to go through this book and clear their knowledge of the basics and specifics by mastering the two parts. The first one introduces the reader to the main concepts of machine learning and how they are utilized to solve the problems. The other part focuses on the cloud-based ML experience. It also introduces the use of Amazon Sagemaker and how it assists in overcoming complex issues professionals can face while performing day-to-day tasks.
Once you grasp all the information included in this book, you will also gain skills needed to solve computer vision problems with Amazon Rekognition. In addition, you will learn more about the core features of engineering, model building, and visualizing data. All this is explained in the easy-to-understand format due to sidebars, source code examples, and illustrations in each chapter. With such preparation, one boosts chances to ace the exam on the first attempt.
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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
|
What Exam Is Necessary for AWS Machine Learning – Specialty?
The only test necessary to take the AWS Machine Learning – Specialty certification has the code MLS-C This is a specialty exam and it is delivered in English, Japanese, Korean, and Simplified Chinese. Candidates can use two types of delivery methods:
- Online using a proctored exam platform.
- In testing centers;
The registration fee for the AWS MLS-C01 exam is $300. In case candidates want to enroll in doing a practice exam, they should pay another $40. MLS-C01 test includes two types of questions. Candidates will have to answer both multiple-choice and multiple-answer items. Besides, the passing score range goes from 100 to 1,000 points. A candidate will be successful only when he/she gets a minimum score of 750 points. After successfully completing this MLS-C01 exam, you will be awarded the AWS Certified Machine Learning Specialty certification. If you add this certificate to your resume and social network, your chances to get better salary offers are higher. Also, this certification is valid for three years. But once its validity expires, you will need to check the vendor's official site for recertification.
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