AWS Certified Machine Learning Engineer - Associate
MLA-C01
Money Back Guarantee
98% Success Rate
Real Questions
1
Data Preparation for Machine Learning (ML) 28%
1.1
Ingest and store data
2 subtopics1.1.1Data ingestion from various sources
1.1.2Data storage strategies
Learning Objectives
- Identify appropriate data ingestion mechanisms for various data sources
- Select optimal storage solutions based on ML workload requirements
- Implement data partitioning strategies for efficient access
- Configure streaming data pipelines for real-time ML applications
1.2
Transform data and perform feature engineering
3 subtopics1.2.1Data cleaning and transformation
1.2.2Feature engineering techniques
1.2.3Data splitting strategies
Learning Objectives
- Apply appropriate data cleaning techniques for ML datasets
- Implement feature engineering methods to improve model performance
- Use SageMaker Data Wrangler and Glue for data transformation
- Design feature engineering pipelines for production ML systems
1.3
Ensure data integrity and prepare data for modeling
3 subtopics1.3.1Bias detection and mitigation
1.3.2Class imbalance handling
1.3.3Data security and compliance
Learning Objectives
- Detect and address bias in ML training data
- Implement strategies to handle class imbalance
- Ensure data security and compliance with regulations
- Apply data validation techniques before model training
Hands-on Skills
- Configure SageMaker Clarify for bias detection
- Implement SMOTE for minority class oversampling
- Set up encryption for S3 data with KMS
- Use Macie to detect PII in datasets
Common Mistakes to Avoid
- Not considering data leakage when engineering features
- Ignoring class imbalance in training data
- Failing to encrypt sensitive data
- Not validating data quality before training
2
ML Model Development 26%
3
Deployment and Orchestration of ML Workflows 22%
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