Everything You Need to Pass: Amazon MLA-C01

via - https://aws.amazon.com/managed -workflows -for-apache -airflow/ Use AWS Step Functions to build a serverless workflow that integrates with SageMaker for model training and deployment, ensuring scalability and fault tolerance - AWS Step Functions is a serverless orchestration service that can integrate with SageMaker and other AWS services. However, it is more general -purpose and lacks some of the ML-specific features, such as model lineage tracking and hyperparameter tuning, that are built into SageMaker Pipelines. Use AWS Lambda functions to manually trigger each step of the ML workflow, enabling flexible execution without needing a predefined orchestration tool - AWS Lambda is useful for triggering specific tasks, but manually managing each step of a complex ML workflow without a comprehensive orchestration tool is not scalable or maintainable. It does not provide the task dependency management, monitoring, and versioning required for an end -to-end ML workflow. References: https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines.html https://aws.amazon.com/managed -workflows -for-apache -airflow/ You are tasked with building a predictive model for customer lifetime value (CLV) using Amazon SageMaker. Given the complexity of the model, it’s crucial to optimize hyperparameters to achieve the best possible performance. You decide to use SageMaker’s automatic model tuning (hyperparameter optimization) with Random Search strategy to fine -tune the model. You have a large dataset, and the tuning job involves several hyperparameters, including the learning rate, batch size, and dropout rate. During the tuning process, you observe that some of the trials are not converging effectively, and the results are not as expected. You suspect that the hyperparameter ranges or the strategy you are using may need adjustment. Which of the following approaches is MOST LIKELY to improve the effectiveness of the hyperparameter tuning process?

https://docs.aws.amazon.com/sagemaker/latest/dg/automatic -model -tuning -how-it-works.html https://aws.amazon.com/what -is/hyperparameter -tuning/ https://docs.aws.amazon.com/sagemaker/latest/dg/automatic -model -tuning.html A company stores its training datasets on Amazon S3 in the form of tabular data running into millions of rows. The company needs to prepare this data for Machine Learning jobs. The data preparation involves data selection, cleansing, exploration, and visualization using a single visual interface. Which Amazon SageMaker service is the best fit for this requirement?

via - https://aws.amazon.com/sagemaker/data -wrangler/ Incorrect options: SageMaker Model Dashboard - Amazon SageMaker Model Dashboard is a centralized portal, accessible from the SageMaker console, where you can view, search, and explore all of the models in your account. You can track which models are deployed for inference and if they are used in batch transform jobs or hosted on endpoints. Amazon SageMaker Clarify - SageMaker Clarify helps identify potential bias during data preparation without writing code. You specify input features, such as gender or age, and SageMaker Clarify runs an analysis job to detect potential bias in those features. Amazon SageMaker Feature Store - Amazon SageMaker Feature Store is a fully managed, purpose -built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. Reference: https://aws.amazon.com/sagemaker/data -wrangler/ Which of the following strategies best aligns with the defense -in-depth security approach for generative AI applications on AWS?

Applying multiple layers of security measures including input validation, access controls, and continuous monitoring to address vulnerabilities Architecting a defense -in-depth security approach involves implementing multiple layers of security to protect generative AI applications. This includes input validation to prevent malicious data inputs, strict access controls to limit who can interact with the AI models, and continuous monitoring to detect and respond to security incidents. These measures can help address common vulnerabilities and meet the best practices for securing generative AI applications on AWS. Incorrect options: Implementing a single -layer firewall to block unauthorized access to the AI models - While a firewall is an important security measure, relying on a single layer of defense is insufficient for comprehensive security. Defense -in-depth requires multiple, overlapping layers of protection. Relying solely on data encryption to protect the AI training data - Data encryption is crucial for protecting data at rest and in transit, but it does not address other vulnerabilities such as input validation or unauthorized access. A holistic security strategy is needed. Using a single authentication mechanism for all users and services accessing the AI models - Employing a single authentication mechanism is a weak security practice. Multiple authentication and authorization mechanisms should be used to ensure robust access control. Reference: https://aws.amazon.com/blogs/machine -learning/architect -defense -in-depth -security -for-generative -ai-ap plications -using -the-owasp -top-10-for-llms/ You are an ML engineer at an e -commerce company tasked with building an automated recommendation system that scales during peak shopping seasons. The solution requires provisioning multiple compute resources, including SageMaker for model training, EC2 instances for data preprocessing, and an RDS database for storing user interaction data. You need to automate the deployment and management of these resources, ensuring that the stacks can communicate effectively. The company prioritizes infrastructure as code (IaC) to maintain consistency and scalability across environments. Which approach is the MOST SUITABLE for automating the provisioning of compute resources and ensuring seamless communication between stacks?

unsuitable for managing complex ML solutions that require seamless communication between multiple resources. Use AWS Elastic Beanstalk to deploy the entire ML solution, relying on its built -in environment management to handle the provisioning and communication between resources automatically - AWS Elastic Beanstalk is a managed service for deploying applications, but it is not designed for orchestrating complex ML workflows with multiple resource types like SageMaker, EC2, and RDS. It also lacks fine -grained control over resource provisioning and inter -stack communication. Reference: https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/using -cfn-nested -stacks.html You are a data scientist at a healthcare startup tasked with developing a machine learning model to predict the likelihood of patients developing a specific chronic disease within the next five years. The dataset available includes patient demographics, medical history, lab results, and lifestyle factors, but it is relatively small, with only 1,000 records. Additionally, the dataset has missing values in some critical features, and the class distribution is highly imbalanced, with only 5% of patients labeled as having developed the disease. Given the data limitations and the complexity of the problem, which of the following approaches is the MOST LIKELY to determine the feasibility of an ML solution and guide your next steps?

➢ TOTAL QUESTIONS:125 You are a machine learning engineer at a fintech company tasked with developing and deploying an end-to-end machine learning workflow for fraud detection. The workflow involves multiple steps, including data extraction, preprocessing, feature engineering, model training, hyperparameter tuning, and deployment. The company requires the solution to be scalable, support complex dependencies between tasks, and provide robust monitoring and versioning capabilities. Additionally, the workflow needs to integrate seamlessly with existing AWS services. Which deployment orchestrator is the MOST SUITABLE for managing and automating your ML workflow?