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Which deployment orchestrator is the MOST SUITABLE for managing and automating your ML workflow?
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...
Which of the following approaches is MOST LIKELY to improve the effectiveness of the hyperparameter tuning process?
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...
Given these requirements, which AWS deployment service and configuration is the MOST SUITABLE for deploying the machine learning model?
You are a data scientist at a retail company responsible for deploying a machine learning model that predicts customer purchase behavior. The model needs to serve real-time predictions with low latency to support the company’s recommendation engine on its e-commerce platform. The deployment solution must also be scalable to handle...
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?
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...
Which of the following statements is the BEST recommendation for the given scenario?
You are working as a machine learning engineer for a startup that provides image recognition services. The service is currently in its beta phase, and the company expects varying levels of traffic, with some days having very few requests and other days experiencing sudden spikes. The company wants to minimize...
Which of the following would you suggest?
A company has recently migrated to AWS Cloud and it wants to optimize the hardware used for its AI workflows. Which of the following would you suggest?A . Leverage either AWS Trainium or AWS Inferentia for the deep learning (DL) and generative AI inference applicationsB . Leverage AWS Trainium for...
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?
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...
Given the nature of the data and the business objective, which Amazon SageMaker built-in algorithm is the MOST SUITABLE for this use case?
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...
Which of the following evaluation techniques and metrics should you prioritize when assessing the performance of your model, considering the dataset's imbalance and the need for a comprehensive understanding of both false positives and false negatives?
You are a Data Scientist working for an e-commerce company that is developing a machine learning model to predict whether a customer will make a purchase based on their browsing behavior. You need to evaluate the model's performance using different evaluation metrics to understand how well the model is predicting...
Which of the following strategies should you implement to effectively provision compute resources for both the production environment and the test environment using Amazon SageMaker, considering the different requirements for each environment?
You are an ML Engineer at a financial services company tasked with deploying a machine learning model for real-time fraud detection in production. The model requires low-latency inference to ensure that fraudulent transactions are flagged immediately. However, you also need to conduct extensive testing and experimentation in a separate environment...
