MLA-C01퍼펙트덤프샘플문제다운최신인증시험최신덤프자료

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Amazon MLA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Preparation for Machine Learning (ML)28%- 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)
      - Data preprocessing and transformation
      • 1. Data cleaning, normalization, and feature engineering
        • 2. Data validation and quality checks
          Topic 2: ML Solution Monitoring, Maintenance, and Security24%- Monitoring and observability
          • 1. Infrastructure and data monitoring
            • 2. Model drift detection and performance monitoring
              - Security and governance
              • 1. IAM policies and access control
                • 2. Compliance and secure ML system design
                  Topic 3: ML Model Development26%- Model tuning and evaluation
                  • 1. Performance evaluation and metrics interpretation
                    • 2. Hyperparameter tuning
                      - Model selection and training
                      • 1. Choosing appropriate ML algorithms
                        • 2. Training models using Amazon SageMaker
                          Topic 4: 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. Auto scaling and infrastructure configuration
                                • 2. Real-time and batch inference endpoints

                                  >> MLA-C01퍼펙트 덤프 샘플문제 다운 <<

                                  MLA-C01공부자료 & MLA-C01퍼펙트 덤프공부자료

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                                  최신 AWS Certified Associate MLA-C01 무료샘플문제 (Q110-Q115):

                                  질문 # 110
                                  A company runs an ML model on Amazon SageMaker. The company uses an automatic process that makes API calls to create training jobs for the model. The company has new compliance rules that prohibit the collection of aggregated metadata from training jobs. Which solution will prevent SageMaker from collecting metadata from the training jobs?

                                  정답：A

                                  설명：
                                  Amazon SageMaker automatically collects training job metadata, but you can opt out of metadata tracking when submitting a training job. This disables collection of aggregated metadata, ensuring compliance with rules that prohibit metadata collection.


                                  질문 # 111
                                  A company is gathering audio, video, and text data in various languages. The company needs to use a large language model (LLM) to summarize the gathered data that is in Spanish.
                                  Which solution will meet these requirements in the LEAST amount of time?

                                  정답：C


                                  질문 # 112
                                  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 an assigned 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?

                                  정답：B

                                  설명：
                                  Since the EC2 instances are in a private subnet and must not have public internet access, the correct solution is to use AWS PrivateLink with an interface VPC endpoint for Amazon Bedrock.
                                  This allows private connectivity from the VPC to the Bedrock service without exposing traffic to the public internet.


                                  질문 # 113
                                  A financial company receives a high volume of real-time market data streams from an external provider. The streams consist of thousands of JSON records per second.
                                  The company needs a scalable AWS solution to identify anomalous data points with the LEAST operational overhead.
                                  Which solution will meet these requirements?

                                  정답：B

                                  설명：
                                  For real-time anomaly detection on streaming data, AWS recommends using Amazon Managed Service for Apache Flink, which provides serverless, scalable stream processing with built-in ML functions.
                                  Apache Flink includes a native RANDOM_CUT_FOREST (RCF) function for unsupervised anomaly detection. This eliminates the need to deploy, manage, or scale custom ML endpoints. When combined with Amazon Kinesis Data Streams, the solution can process thousands of records per second with very low latency.
                                  Option B introduces multiple services and custom logic, increasing operational overhead. Option C requires managing EC2 infrastructure and Kafka clusters. Option D is batch-oriented and does not meet real-time requirements.
                                  AWS documentation explicitly highlights Kinesis + Managed Flink + RCF as the lowest-overhead solution for real-time anomaly detection.
                                  Therefore, Option A is the correct and AWS-aligned choice.


                                  질문 # 114
                                  A machine learning engineer is preparing a data frame for a supervised learning task with the Amazon SageMaker Linear Learner algorithm. The ML engineer notices the target label classes are highly imbalanced and multiple feature columns contain missing values. The proportion of missing values across the entire data frame is less than 5%.
                                  What should the ML engineer do to minimize bias due to missing values?

                                  정답：A

                                  설명：
                                  Use supervised learning to predict missing values based on the values of other features. Different supervised learning approaches might have different performances, but any properly implemented supervised learning approach should provide the same or better approximation than mean or median approximation, as proposed in responses A and C. Supervised learning applied to the imputation of missing values is an active field of research.


                                  질문 # 115
                                  ......

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