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Which of the following approaches would you combine for detecting and managing drift in your ML model?
You are a data scientist working for a financial institution that uses a machine learning model to predict loan defaults. The model was trained on historical data from the past five years, but after being deployed for several months, its accuracy has gradually decreased. Upon investigation, you suspect that the...
Given this scenario, which of the following BEST describes how Conditional Demographic Disparity (CDD) can be used to assess and mitigate bias in your model?
You are a data scientist at an insurance company developing a machine learning model to predict the likelihood of claims being fraudulent. The company has a strong commitment to fairness and wants to ensure that the model does not disproportionately affect any specific demographic group. You decide to use Amazon...
Given these challenges, which combination of techniques is the MOST LIKELY to help prevent overfitting and improve the model’s performance on unseen data?
You are a data scientist working on a deep learning model to classify medical images for disease detection. The model initially shows high accuracy on the training data but performs poorly on the validation set, indicating signs of overfitting. The dataset is limited in size, and the model is complex,...
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...
