MLA-C01 Übungsmaterialien & MLA-C01 Lernführung: AWS Certified Machine Learning Engineer - Associate & MLA-C01 Lernguide

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Amazon MLA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Deployment and Orchestration of ML Workflows22%- ML pipeline orchestration
  • 1. CI/CD pipelines for ML workflows
    • 2. Workflow automation using AWS services
      - Model deployment
      • 1. Real-time and batch inference endpoints
        • 2. Auto scaling and infrastructure configuration
          Topic 2: ML Model Development26%- Model tuning and evaluation
          • 1. Hyperparameter tuning
            • 2. Performance evaluation and metrics interpretation
              - Model selection and training
              • 1. Choosing appropriate ML algorithms
                • 2. Training models using Amazon SageMaker
                  Topic 3: ML Solution Monitoring, Maintenance, and Security24%- Monitoring and observability
                  • 1. Model drift detection and performance monitoring
                    • 2. Infrastructure and data monitoring
                      - Security and governance
                      • 1. IAM policies and access control
                        • 2. Compliance and secure ML system design
                          Topic 4: Data Preparation for Machine Learning (ML)28%- Data ingestion and collection
                          • 1. Batch and streaming ingestion using AWS services (e.g., S3, Kinesis, Glue)
                            • 2. Data acquisition from structured and unstructured sources
                              - Data preprocessing and transformation
                              • 1. Data cleaning, normalization, and feature engineering
                                • 2. Data validation and quality checks

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                                  Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 Prüfungsfragen mit Lösungen (Q13-Q18):

                                  13. Frage
                                  A company has trained an ML model in Amazon SageMaker. The company needs to host the model to provide inferences in a production environment.
                                  The model must be highly available and must respond with minimum latency. The size of each request will be between 1 KB and 3 MB. The model will receive unpredictable bursts of requests during the day. The inferences must adapt proportionally to the changes in demand.
                                  How should the company deploy the model into production to meet these requirements?

                                  Antwort: D


                                  14. Frage
                                  An ML engineer needs to ensure that a dataset complies with regulations for personally identifiable information (PII). The ML engineer will use the data to train an ML model on Amazon SageMaker instances. SageMaker must not use any of the PII.
                                  Which solution will meet these requirements in the MOST operationally efficient way?

                                  Antwort: C


                                  15. Frage
                                  A company is creating an application that will recommend products for customers to purchase.
                                  The application will make API calls to Amazon Q Business. The company must ensure that responses from Amazon Q Business do not include the name of the company's main competitor.
                                  Which solution will meet this requirement?

                                  Antwort: C


                                  16. Frage
                                  A company has an application that uses different APIs to generate embeddings for input text. The company needs to implement a solution to automatically rotate the API tokens every 3 months.
                                  Which solution will meet this requirement?

                                  Antwort: A

                                  Begründung:
                                  AWS Secrets Manager is designed for securely storing, managing, and automatically rotating secrets, including API tokens. By configuring a Lambda function for custom rotation logic, the solution can automatically rotate the API tokens every 3 months as required. Secrets Manager simplifies secret management and integrates seamlessly with other AWS services, making it the ideal choice for this use case.


                                  17. Frage
                                  A company is using Amazon SageMaker and millions of files to train an ML model. Each file is several megabytes in size. The files are stored in an Amazon S3 bucket. The company needs to improve training performance.
                                  Which solution will meet these requirements in the LEAST amount of time?

                                  Antwort: A

                                  Begründung:
                                  Amazon FSx for Lustre is designed for high-performance workloads like ML training. It provides fast, low- latency access to data by linking directly to the existing S3 bucket and caching frequently accessed files locally. This significantly improves training performance compared to directly accessing millions of files from S3. It requires minimal changes to the training job and avoids the overhead of transferring or restructuring data, making it the fastest and most efficient solution.


                                  18. Frage
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