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Amazon MLA-C01 Exam Syllabus Topics:

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

                                  >> MLA-C01 Exam Prep <<

                                  Quiz 2026 Amazon MLA-C01: Unparalleled AWS Certified Machine Learning Engineer - Associate Exam Prep

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                                  Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q198-Q203):

                                  NEW QUESTION # 198
                                  A company's dataset for prediction analytics contains duplicate records, missing data, and unusually extreme high or low values. The company needs a solution to resolve the data quality issues quickly. The solution must maintain data integrity and have the LEAST operational overhead.
                                  Which solution will meet these requirements?

                                  Answer: A

                                  Explanation:
                                  AWS Glue DataBrew is designed specifically for no-code and low-code data preparation, making it the fastest and lowest-overhead solution for resolving common data quality issues. DataBrew provides built-in transformations for deduplication, missing value imputation, and outlier handling while preserving data integrity.
                                  Option A uses standard statistical techniques such as median imputation and value normalization, which are widely accepted and maintain the distribution of the data. DataBrew jobs are fully managed and do not require infrastructure setup or maintenance.
                                  Option B deletes records, which can lead to data loss and does not preserve integrity. Option C introduces unnecessary infrastructure complexity and uses poor data imputation practices. Option D provides advanced capabilities but requires more configuration and ML expertise, increasing operational overhead.
                                  AWS documentation clearly positions DataBrew as the preferred solution for quick, reliable data cleaning with minimal effort.
                                  Therefore, Option A is the correct answer.


                                  NEW QUESTION # 199
                                  A company is exploring generative AI and wants to add a new product feature. An ML engineer is making API calls from existing Amazon EC2 instances to Amazon Bedrock.
                                  The EC2 instances are in a private subnet and must remain private during the implementation. The EC2 instances have a security group that allows access to all IP addresses in the private subnet.
                                  What should the ML engineer do to establish a connection between the EC2 instances and Amazon Bedrock?

                                  Answer: A

                                  Explanation:
                                  Amazon Bedrock is a fully managed AWS service and does not reside inside customer VPCs. To allow private connectivity from EC2 instances in a private subnet without using the public internet, AWS documentation recommends AWS PrivateLink.
                                  By creating an interface VPC endpoint for Amazon Bedrock, traffic between the EC2 instances and Bedrock remains entirely within the AWS network. This approach preserves the private subnet configuration and meets strict security requirements.
                                  Security groups alone cannot establish connectivity to a managed service endpoint. Amazon Bedrock cannot be deployed into a customer subnet, making Option C invalid. AWS Direct Connect is designed for on- premises connectivity, not for accessing AWS managed services from within a VPC.
                                  AWS explicitly documents that PrivateLink is the correct mechanism for private access to Amazon Bedrock.
                                  Therefore, Option B is the correct and AWS-verified answer.


                                  NEW QUESTION # 200
                                  A company runs its ML workflows on an on-premises Kubernetes cluster. The ML workflows include ML services that perform training and inferences for ML models. Each ML service runs from its own standalone Docker image.
                                  The company needs to perform a lift and shift from the on-premises Kubernetes cluster to an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.
                                  Which solution will meet this requirement with the LEAST operational overhead?

                                  Answer: D

                                  Explanation:
                                  A lift-and-shift migration requires minimal architectural changes. The company already uses Kubernetes and standalone Docker images, which are fully compatible with Amazon Elastic Kubernetes Service.
                                  The lowest-overhead approach is to push the existing Docker images to Amazon Elastic Container Registry and deploy them directly to the EKS cluster using standard Kubernetes manifests or Helm charts. This approach preserves the existing ML workflows, training logic, and inference services without refactoring.
                                  Option A requires redesigning services with Kubeflow, significantly increasing complexity. Option C changes both data storage and training methodology. Option D requires retraining models and changing operational workflows.
                                  Therefore, uploading Docker images to Amazon ECR and deploying them to EKS is the correct lift-and-shift solution.


                                  NEW QUESTION # 201
                                  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?

                                  Answer: C

                                  Explanation:
                                  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.


                                  NEW QUESTION # 202
                                  A company wants to improve the sustainability of its ML operations.
                                  Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

                                  Answer: A,C

                                  Explanation:
                                  SageMaker Debuggercan identify when a training job is not converging or is stuck in a non-productive state.
                                  By stopping these jobs early, unnecessary energy and computational resources are conserved, improving sustainability.
                                  AWS Trainiuminstances are purpose-built for ML training and are optimized for energy efficiency and cost- effectiveness. They use less energy per training task compared to general-purpose instances, making them a sustainable choice.


                                  NEW QUESTION # 203
                                  ......

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