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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?
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,...
Which of the following approaches are the MOST LIKELY to lead to a significant improvement in model performance?
You are working on a machine learning project for a financial services company, developing a model to predict credit risk. After deploying the initial version of the model using Amazon SageMaker, you find that its performance, measured by the AUC (Area Under the Curve), is not meeting the company’s accuracy...
Which scaling policy is the MOST SUITABLE for this scenario, and why?
You are an ML engineer at a retail company that uses a SageMaker model to generate product recommendations for customers in real-time. During peak shopping periods, the traffic to the recommendation engine increases dramatically. The company needs to ensure that the model endpoint can handle these spikes in demand without...
Which combination of practices and AWS services is MOST LIKELY to result in a maintainable, scalable, and cost-effective ML infrastructure?
You are a lead machine learning engineer at a growing tech startup that is developing a recommendation system for a mobile app. The recommendation engine must be able to scale quickly as the user base grows, remain cost-effective to align with the startup’s budget constraints, and be easy to maintain...
Which of the following deployment targets should you choose for the different machine learning models, given their specific requirements?
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)A . Choose Amazon Elastic Container Service (Amazon ECS) for...
Which of the following strategies is the MOST LIKELY to ensure model versioning, repeatability, and auditability?
You are a data scientist at a pharmaceutical company that builds predictive models to analyze clinical trial data. Due to regulatory requirements, the company must maintain strict version control of all models used in decision-making processes. This includes tracking which data, hyperparameters, and code were used to train each model,...
Given these varying requirements, which deployment target is the MOST SUITABLE for each model?
You are a machine learning engineer at a fintech company that has developed several models for various use cases, including fraud detection, credit scoring, and personalized marketing. Each model has different performance and deployment requirements. The fraud detection model requires real-time predictions with low latency and needs to scale quickly...
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,...
