AWS Certified Machine Learning - Specialty (MLS-C01日本語版): MLS-C01 Exam
"AWS Certified Machine Learning - Specialty (MLS-C01日本語版)", also known as MLS-C01 exam, is a Amazon Certification. With the complete collection of questions and answers, Pass4sureCert has assembled to take you through 330 Q&As to your MLS-C01 Exam preparation. In the MLS-C01 exam resources, you will cover every field and category in AWS Certified Specialty Certification helping to ready you for your successful Amazon Certification.
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- Exam Code: MLS-C01
- Exam Name: AWS Certified Machine Learning - Specialty (MLS-C01日本語版)
- Certification Provider: Amazon
- Corresponding Certification: AWS Certified Specialty
- Updated: Jun 02, 2026
- No. of Questions: 330 Questions & Answers with Testing Engine
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The minimum passing score for this test is 750 marks. The result will be reported on a scale of 100-1000. Note that there might be some unscored items in the exam that are not identified but don't affect your score. Also, always keep in mind that you don't need to succeed in each section to get a pass status and obtain the certification — only a total amount of points matters. The report is only needed to show individuals their performance in each domain and help them identify what are their weak and strong areas of Machine Learning.
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The Amazon MLS-C01 exam is targeted at those individuals who are tasked with performing the data science or development role. It provides that the candidates can design, deploy, implement, and maintain ML or machine learning solutions for given business problems.
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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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