provide the customization required by the BYOC approach. SageMaker pre-built containers may not support the specific custom libraries and dependencies your model requires. Deploy the model locally using Docker, then use the AWS Management Console to manually copy the environment and model files to a SageMaker instance for training - Manually deploying the model and environment locally and then copying files to SageMaker instances is not scalable or maintainable. SageMaker BYOC allows for a more robust, automated, and integrated solution. References: https://aws.amazon.com/blogs/machine -learning/bring -your-own-model -with-amazon -sagemaker -script - mode/ https://docs.aws.amazon.com/sagemaker/latest/dg/docker -containers.html You are a data scientist at a financial technology company developing a fraud detection system. The system needs to identify fraudulent transactions in real -time based on patterns in transaction data, including amounts, locations, times, and account histories. The dataset is large and highly imbalanced, with only a small percentage of transactions labeled as fraudulent. Your team has access to Amazon SageMaker and is considering various built -in algorithms to build the model. Given the need for both high accuracy and the ability to handle imbalanced data, which SageMaker built- in algorithm is the MOST SUITABLE for this use case?
Use the Linear Learner algorithm with weighted classification to address the class imbalance - The Linear Learner algorithm can handle classification tasks, and weighting classes can help with imbalance. However, it may not be as effective in capturing complex patterns in the data as more sophisticated algorithms like XGBoost. Select the Random Cut Forest (RCF) algorithm for its ability to detect anomalies in transaction data - Random Cut Forest (RCF) is designed for anomaly detection, which can be relevant for fraud detection. However, RCF is unsupervised and may not leverage the labeled data effectively, leading to suboptimal results in a supervised classification task like this. Implement the K-Nearest Neighbors (k-NN) algorithm to classify transactions based on similarity to known fraudulent cases - K-Nearest Neighbors (k -NN) can classify based on similarity, but it does not scale well with large datasets and may struggle with the high-dimensional, imbalanced nature of the data in this context. References: https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost_hyperparameters.html https://aws.amazon.com/blogs/gametech/fraud -detection -for-games -using -machine -learning/ https://d1.awsstatic.com/events/reinvent/2019/REPEAT_1_Build_a_fraud_detection_system_with_Amaz on_SageMaker_AIM359 -R1.pdf You are a Machine Learning Engineer working for a large retail company that has developed multiple machine learning models to improve various aspects of their business, including personalized recommendations, generative AI, and fraud detection. The models have different deployment requirements: 1. The recommendations models need to handle real-time inference with low latency. 2. The generative AI model requires high scalability to manage fluctuating loads. 3. The fraud detection model is a large model and needs to be integrated into serverless applications to minimize infrastructure management. Which of the following deployment targets should you choose for the different machine learning models, given their specific requirements? (Select two)
Explanation: Correct options: Deploy the real-time recommendation model using Amazon SageMaker endpoints to ensure low- latency, high -availability, and managed infrastructure for real -time inference Amazon EKS is designed for containerized applications that need high scalability and flexibility. It is suitable for the generative AI model, which may require complex orchestration and scaling in response to varying demand, while giving you full control over the deployment environment. via - https://aws.amazon.com/blogs/containers/deploy -generative -ai-models -on-amazon -eks/ Deploy the generative AI model using Amazon Elastic Kubernetes Service (Amazon EKS) to leverage containerized microservices for high scalability and control over the deployment environment Real-time inference is ideal for inference workloads where you have real -time, interactive, low latency requirements. You can deploy your model to SageMaker hosting services and get an endpoint that can be used for inference. These endpoints are fully managed and support autoscaling. This makes it an ideal choice for the recommendation model, which must provide fast responses to user interactions with minimal downtime. Incorrect options: Use AWS Lambda to deploy the fraud detection model, which requires rapid scaling and integration into an existing serverless architecture, minimizing infrastructure management - While AWS Lambda is excellent for serverless applications, it may not be the best choice for a fraud detection model if it requires continuous, low -latency processing or needs to handle very high throughput. Lambda is better suited for lightweight, event -driven tasks rather than long -running, complex inference jobs. Choose Amazon Elastic Container Service (Amazon ECS) for the recommendation model, as it provides container orchestration for large -scale, batch processing workloads with tight integration into other AWS services - Amazon ECS is a good choice for containerized workloads but is generally more appropriate for batch processing or large -scale, stateless applications. It might not provide the low -latency and real -time capabilities needed for the recommendation model. Deploy all models using Amazon SageMaker endpoints for consistency and ease of management, regardless of their individual requirements for scalability, latency, or integration - Deploying all models using Amazon SageMaker endpoints without considering their specific requirements for latency, scalability, and integration would be suboptimal. While SageMaker endpoints are highly versatile, they may not be the best fit for every use case, especially for models requiring serverless architecture or advanced container orchestration. References: https://docs.aws.amazon.com/sagemaker/latest/dg/realtime -endpoints.html https://aws.amazon.com/blogs/containers/deploy -generative -ai-models -on-amazon -eks/ Which AWS service is used to store, share and manage inputs to Machine Learning models used during training and inference?
time can achieve high accuracy without effectively capturing the minority class (e.g., customers who make a purchase). Prioritize Root mean squared error (RMSE) as the key metric, as it measures the average magnitude of the errors between predicted and actual values - RMSE is a regression metric, not suitable for classification problems. In this scenario, you are dealing with a classification task, so metrics like precision, recall, and F1 score are more appropriate. Utilize the AUC -ROC curve to evaluate the model’s ability to distinguish between classes across various thresholds, particularly in the presence of class imbalance - The AUC -ROC curve is a useful tool, especially in imbalanced datasets. However, understanding the confusion matrix and calculating precision and recall provide more direct insights into the types of errors the model is making, which is crucial for improving the model’s performance in your specific context. References: https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot -metrics -validation.html https://docs.aws.amazon.com/machine -learning/latest/dg/binary -classification.html You are a machine learning engineer working for a telecommunications company that needs to develop a predictive maintenance model. The goal is to predict when network equipment is likely to fail based on historical sensor data. The data includes features such as temperature, pressure, usage, and error rates recorded over time. The company wants to avoid unplanned downtime and optimize maintenance schedules by predicting failures just in time. Given the nature of the data and the business objective, which Amazon SageMaker built-in algorithm is the MOST SUITABLE for this use case?
Random Cut Forest (RCF) is specifically designed for detecting anomalies in data. This algorithm excels at identifying unexpected patterns in sensor data that could indicate the early stages of equipment failure. It’s particularly well-suited for scenarios where you need to react to unusual behaviors in near- real-time. Mapping use cases to built-in algorithms: via - https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html Incorrect options: DeepAR Algorithm to forecast future equipment failures based on historical data - DeepAR is designed for forecasting future time series data, which could be useful for predicting future equipment behavior. However, it is not primarily used for anomaly detection, which is critical for identifying unusual patterns that precede failures. Linear Learner Algorithm to classify equipment status as 'healthy' or 'at risk' based on sensor readings - Linear Learner could be used for classification tasks, but predicting maintenance needs often involves detecting subtle anomalies rather than simple classification. Additionally, a binary classification model might not capture the complex patterns associated with potential failures. Time Series K -Means Algorithm to cluster similar patterns in the sensor data and predict failures - Time Series K-Means can cluster similar time series patterns, but clustering alone does not provide the precision needed for real -time anomaly detection, which is crucial for predictive maintenance. References: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html https://docs.aws.amazon.com/sagemaker/latest/dg/randomcutforest.html Which benefits might persuade a developer to choose a transparent and explainable machine learning model? (Select two)
➢ 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?
