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Amazon MLA-C01 Exam Syllabus Topics:

SectionWeightObjectives
ML Solution Monitoring, Maintenance, and Security24%- Monitor model and data quality
  • 1. Model drift detection: data drift, concept drift
  • 2. Performance monitoring and alerting
  • 3. Amazon SageMaker Model Monitor
- Secure ML solutions and resources
  • 1. Compliance, governance, and data privacy
  • 2. Data encryption: at rest and in transit
  • 3. Access control: IAM roles, policies, permissions
- Optimize and maintain workloads
  • 1. Logging, auditing, and troubleshooting
  • 2. Model retraining and update strategies
  • 3. Cost optimization and resource management
Deployment and Orchestration of ML Workflows22%- Configure deployment for scalability and availability
  • 1. A/B testing and canary deployment
  • 2. Infrastructure as code: AWS CloudFormation, Terraform
  • 3. Auto-scaling, load balancing, and high availability
- Automate and orchestrate ML pipelines
  • 1. CI/CD integration: AWS CodePipeline, AWS CodeBuild
  • 2. Workflow automation and event-driven processing
  • 3. ML pipelines: Amazon SageMaker Pipelines
- Choose deployment infrastructure and pattern
  • 1. Real-time inference, batch transform, serverless, edge deployment
  • 2. Model packaging and versioning: Amazon SageMaker Model Registry
  • 3. Infrastructure: Amazon SageMaker endpoints, AWS Lambda, Amazon ECS, Amazon EKS
ML Model Development26%- Evaluate and analyze model performance
  • 1. Model explainability: Amazon SageMaker Clarify
  • 2. Metrics: accuracy, precision, recall, F1, RMSE, MAE, confusion matrix
  • 3. Bias detection and mitigation
- Train, tune, and refine models
  • 1. Distributed training and managed services
  • 2. Training options: built-in algorithms, custom containers, frameworks
  • 3. Hyperparameter optimization: Amazon SageMaker Automatic Model Tuning
- Select appropriate modeling approach
  • 1. Algorithm selection: traditional ML, deep learning, pre-built models
  • 2. Problem type: classification, regression, clustering, forecasting, NLP, computer vision
  • 3. Use cases and service recommendations
Data Preparation for Machine Learning28%- Ensure data integrity and prepare for modeling
  • 1. Data splitting: train/validation/test sets
  • 2. Data validation, quality checks, and profiling
  • 3. Feature store usage: Amazon SageMaker Feature Store
- Transform data and perform feature engineering
  • 1. Data cleaning, normalization, and encoding
  • 2. Tools: Amazon SageMaker Processing, AWS Glue DataBrew, Pandas, PySpark
  • 3. Feature selection, transformation, and scaling
- Ingest and store data
  • 1. Data ingestion services: Amazon Kinesis, AWS Glue, Amazon S3, Amazon Athena
  • 2. Data storage options: object storage, data lakes, databases
  • 3. Data formats: Parquet, JSON, CSV, ORC, Avro, RecordIO

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Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 Prüfungsfragen mit Lösungen (Q233-Q238):

233. Frage
A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 ## in size and consists of CSV, JSON, Apache Parquet, and simple text files.
The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.
Which solution will meet these requirements?

Antwort: C

Begründung:
Amazon SageMaker Pipelines is designed for creating, automating, and managing end-to-end ML workflows, including complex data preprocessing tasks. It supports handling large datasets and can integrate with custom steps, such as NLP transformations. By combining SageMaker Pipelines with Amazon EventBridge, the entire workflow can be triggered and automated efficiently, meeting the requirements for scalability, automation, and processing complexity.


234. Frage
An ML engineer is training an ML model to identify people's health risk based on 20 features and
1 target. The target class has two values:
- Likely to have health risk (positive class)
- Unlikely to have health risk (negative class)
The age range of people in the dataset is 30 years old to 60 years old. Age is one of the features.
The ML engineer analyzes the features. For the positive class, the difference in proportions of labels (DPL) value is (+0.9) for the age range of 40 to 45 compared with all other age ranges.
What should the ML engineer do to correct this data imbalance?

Antwort: B

Begründung:
A DPL of +0.9 indicates that the positive class is heavily overrepresented in the 40-45 age range compared to other age ranges. To correct this imbalance, the solution is to undersample the positive class within the 40-45 range, reducing its dominance and improving fairness in the dataset.


235. Frage
A company has an ML model that needs to run one time each night to predict stock values. The model input is
3 MB of data that is collected during the current day. The model produces the predictions for the next day.
The prediction process takes less than 1 minute to finish running.
How should the company deploy the model on Amazon SageMaker to meet these requirements?

Antwort: D

Begründung:
A serverless inference endpoint in Amazon SageMaker is ideal for use cases where the model is invoked infrequently, such as running one time each night. It eliminates the cost of idle resources when the model is not in use. Setting the MaxConcurrency parameter to 1 ensures cost-efficiency while supporting the required single nightly invocation. This solution minimizes costs and matches the requirement to process a small amount of data quickly.


236. Frage
A company uses Amazon Athena to query a dataset in Amazon S3. The dataset has a target variable that the company wants to predict.
The company needs to use the dataset in a solution to determine if a model can predict the target variable.
Which solution will provide this information with the LEAST development effort?

Antwort: A

Begründung:
Amazon SageMaker Autopilot automates the process of building, training, and tuning machine learning models. It provides insights into whether the target variable can be effectively predicted by evaluating the model's performance metrics. This solution requires minimal development effort as SageMaker Autopilot handles data preprocessing, algorithm selection, and hyperparameter optimization automatically, making it the most efficient choice for this scenario.


237. Frage
A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon 53 to provide customers with a live conversational engine.
The model is using sensitive data. An ML engineer needs to implement a solution to identify and remove the sensitive data.
Which solution will meet these requirements with the LEAST operational overhead?

Antwort: D

Begründung:
Amazon Macie is a fully managed data security and privacy service that uses machine learning to discover and classify sensitive data in Amazon S3. It is purpose-built to identify sensitive data with minimal operational overhead. After identifying the sensitive data, you can use AWS Lambda functions to automate the process of removing or redacting the sensitive data, ensuring efficiency and integration with the hybrid cloud environment. This solution requires the least development effort and aligns with the requirement to handle sensitive data effectively.


238. Frage
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