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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...
You are an ML engineer at a startup that is developing a recommendation engine for an e-commerce platform. The workload involves training models on large datasets and deploying them to serve real-time recommendations to customers. The training jobs are sporadic but require significant computational power, while the inference workloads must handle varying traffic throughout the day. The company is cost-conscious and aims to balance cost efficiency with the need for scalability and performance. Given these requirements, which approach to resource allocation is the MOST SUITABLE for training and inference, and why?
You are an ML engineer at a startup that is developing a recommendation engine for an e-commerce platform. The workload involves training models on large datasets and deploying them to serve real-time recommendations to customers. The training jobs are sporadic but require significant computational power, while the inference workloads must...
Which feature engineering process should your team apply to select a subset of features that are the most relevant towards minimizing the error rate of the trained model?
Your data science team is working on developing a machine learning model to predict customer churn. The dataset that you are using contains hundreds of features, but you suspect that not all of these features are equally important for the model's accuracy. To improve the model's performance and reduce its...
Given the problem context and the need to effectively handle class imbalance, which boosting technique is MOST SUITABLE for this scenario?
You are a data scientist at a financial institution tasked with building a model to detect fraudulent transactions. The dataset is highly imbalanced, with only a small percentage of transactions being fraudulent. After experimenting with several models, you decide to implement a boosting technique to improve the model’s accuracy, particularly...
Which of the following statements BEST describes the difference between data drift and model drift, and how you would address them using Amazon SageMaker?
You are a machine learning engineer at an e-commerce company that uses a recommendation model to suggest products to customers. The model was trained on data from the past year, but after being in production for several months, you notice that the model's recommendations are becoming less relevant. You suspect...
Which approach is the MOST APPROPRIATE for fine-tuning the pre-trained model with your custom dataset?
You are a data scientist at a marketing agency tasked with creating a sentiment analysis model to analyze customer reviews for a new product. The company wants to quickly deploy a solution with minimal training time and development effort. You decide to leverage a pre-trained natural language processing (NLP) model...
Given the requirements, which combination of IaC option and container service is MOST SUITABLE for this scenario, and why?
You are a DevOps engineer at a tech company that is building a scalable microservices-based application. The application is composed of several containerized services, each responsible for different parts of the application, such as user authentication, data processing, and recommendation systems. The company wants to standardize and automate the deployment...
How would you differentiate between K-Means and K-Nearest Neighbors (KNN) algorithms in machine learning?
How would you differentiate between K-Means and K-Nearest Neighbors (KNN) algorithms in machine learning?A . K-Means requires labeled data to form clusters, whereas KNN does not use labeled data for making predictionsB . K-Means is primarily used for regression tasks, while KNN is used for reducing the dimensionality of dataC...
Which approach is the MOST SUITABLE for automating the provisioning of compute resources and ensuring seamless communication between stacks?
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...
Which Amazon SageMaker service is the best fit for this requirement?
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...
