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Amazon MLA-C01 Exam Syllabus Topics:

SectionWeightObjectives
ML Model Development26%- Model selection and training
  • 1. Choosing appropriate ML algorithms
    • 2. Training models using Amazon SageMaker
      - Model tuning and evaluation
      • 1. Hyperparameter tuning
        • 2. Performance evaluation and metrics interpretation
          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. 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
                          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. Batch and streaming ingestion using AWS services (e.g., S3, Kinesis, Glue)
                                • 2. Data acquisition from structured and unstructured sources

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                                  Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q210-Q215):

                                  NEW QUESTION # 210
                                  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?

                                  Answer: C

                                  Explanation:
                                  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.


                                  NEW QUESTION # 211
                                  A company needs an AWS solution that will automatically create versions of ML models as the models are created.
                                  Which solution will meet this requirement?

                                  Answer: C


                                  NEW QUESTION # 212
                                  An ML engineer is building a generative AI application on Amazon Bedrock by using large language models (LLMs).
                                  Select the correct generative AI term from the following list for each description. Each term should be selected one time or not at all. (Select three.)
                                  * Embedding
                                  * Retrieval Augmented Generation (RAG)
                                  * Temperature
                                  * Token

                                  Answer:

                                  Explanation:

                                  Explanation:

                                  Text representation of basic units of data processed by LLMs: Token
                                  High-dimensional vectors that contain the semantic meaning of text: Embedding Enrichment of information from additional data sources to improve a generated response: Retrieval Augmented Generation (RAG) Comprehensive Detailed Explanation Token:
                                  Description: A token represents the smallest unit of text (e.g., a word or part of a word) that an LLM processes. For example, " running " might be split into two tokens: " run " and " ing. " Why? Tokens are the fundamental building blocks for LLM input and output processing, ensuring that the model can understand and generate text efficiently.
                                  Embedding:
                                  Description: High-dimensional vectors that encode the semantic meaning of text. These vectors are representations of words, sentences, or even paragraphs in a way that reflects their relationships and meaning.
                                  Why? Embeddings are essential for enabling similarity search, clustering, or any task requiring semantic understanding. They allow the model to " understand " text contextually.
                                  Retrieval Augmented Generation (RAG):
                                  Description: A technique where information is enriched or retrieved from external data sources (e.g., knowledge bases or document stores) to improve the accuracy and relevance of a model ' s generated responses.
                                  Why? RAG enhances the generative capabilities of LLMs by grounding their responses in factual and up-to- date information, reducing hallucinations in generated text.
                                  By matching these terms to their respective descriptions, the ML engineer can effectively leverage these concepts to build robust and contextually aware generative AI applications on Amazon Bedrock.


                                  NEW QUESTION # 213
                                  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: C


                                  NEW QUESTION # 214
                                  A company runs an Amazon SageMaker domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker domain.
                                  Recently, the company discovered suspicious traffic to the domain from a specific IP address. The company needs to block traffic from the specific IP address.
                                  Which update to the network configuration will meet this requirement?

                                  Answer: A

                                  Explanation:
                                  Network ACLs (Access Control Lists) operate at the subnet level and allow for rules to explicitly deny traffic from specific IP addresses. By creating an inbound rule in the network ACL to deny traffic from the suspicious IP address, the company can block traffic to the Amazon SageMaker domain from that IP. This approach works because network ACLs are evaluated before traffic reaches the security groups, making them effective for blocking traffic at the subnet level.


                                  NEW QUESTION # 215
                                  ......

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