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Amazon MLA-C01 Exam Syllabus Topics:

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

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                                  Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q47-Q52):

                                  NEW QUESTION # 47
                                  An ML engineer needs to use an Amazon EMR cluster to process large volumes of data in batches. Any data loss is unacceptable.
                                  Which instance purchasing option will meet these requirements MOST cost-effectively?

                                  Answer: B


                                  NEW QUESTION # 48
                                  Case Study
                                  A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
                                  The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
                                  The company needs to use the central model registry to manage different versions of models in the application.
                                  Which action will meet this requirement with the LEAST operational overhead?

                                  Answer: B

                                  Explanation:
                                  Amazon SageMaker Model Registry is a feature designed to manage machine learning (ML) models throughout their lifecycle. It allows users to catalog, version, and deploy models systematically, ensuring efficient model governance and management.
                                  Key Features of SageMaker Model Registry:
                                  Centralized Cataloging: Organizes models into Model Groups, each containing multiple versions.
                                  Version Control: Maintains a history of model iterations, making it easier to track changes.
                                  Metadata Association: Attach metadata such as training metrics and performance evaluations to models.
                                  Approval Status Management: Allows setting statuses like PendingManualApproval or Approved to ensure only vetted models are deployed.
                                  Seamless Deployment: Direct integration with SageMaker deployment capabilities for real-time inference or batch processing.
                                  Implementation Steps:
                                  Create a Model Group: Organize related models into groups to simplify management and versioning.
                                  Register Model Versions: Each model iteration is registered as a version within a specific Model Group.
                                  Set Approval Status: Assign approval statuses to models before deploying them to ensure quality control.
                                  Deploy the Model: Use SageMaker endpoints for deployment once the model is approved.
                                  Benefits:
                                  Centralized Management: Provides a unified platform to manage models efficiently.
                                  Streamlined Deployment: Facilitates smooth transitions from development to production.
                                  Governance and Compliance: Supports metadata association and approval processes.
                                  By leveraging the SageMaker Model Registry, the company can ensure organized management of models, version control, and efficient deployment workflows with minimal operational overhead.
                                  AWS Documentation: SageMaker Model Registry
                                  AWS Blog: Model Registry Features and Usage


                                  NEW QUESTION # 49
                                  A company is using an Amazon Redshift database as its single data source. Some of the data is sensitive.
                                  A data scientist needs to use some of the sensitive data from the database. An ML engineer must give the data scientist access to the data without transforming the source data and without storing anonymized data in the database.
                                  Which solution will meet these requirements with the LEAST implementation effort?

                                  Answer: B

                                  Explanation:
                                  Dynamic data maskingallows you to control how sensitive data is presented to users at query time, without modifying or storing transformed versions of the source data. Amazon Redshift supports dynamic data masking, which can be implemented with minimal effort. This solution ensures that the data scientistcan access the required information while sensitive data remains protected, meeting the requirements efficiently and with the least implementation effort.


                                  NEW QUESTION # 50
                                  An ML engineer needs to deploy a trained model that is based on a genetic algorithm. The algorithm solves a complex problem and can take several minutes to generate predictions.
                                  When the model is deployed, the model needs to access large amounts of data to process requests. The requests can involve as much as 100 MB of data.
                                  Which deployment solution will meet these requirements with the LEAST operational overhead?

                                  Answer: C

                                  Explanation:
                                  SageMaker Asynchronous Inference is designed for models with long processing times and large payloads. It can handle input data up to 1 GB and avoids holding open connections during long inference runs, reducing operational overhead compared to managing EC2 or ECS infrastructure.
                                  This makes it the best fit for the genetic algorithm model that takes minutes and processes large requests.


                                  NEW QUESTION # 51
                                  A company is developing an ML model to predict customer satisfaction. The company needs to use survey feedback and the past satisfaction level of customers to predict the future satisfaction level of customers.
                                  The dataset includes a column named Feedback that contains long text responses. The dataset also includes a column named Satisfaction Level that contains three distinct values for past customer satisfaction: High, Medium, and Low. The company must apply encoding methods to transform the data in each column.
                                  Which solution will meet these requirements?

                                  Answer: D


                                  NEW QUESTION # 52
                                  ......

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