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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...
Given the scenario, which of the following approaches is the MOST LIKELY to improve the model’s performance?
You are working as a data scientist at a financial services company tasked with developing a credit risk prediction model. After experimenting with several models, including logistic regression, decision trees, and support vector machines, you find that none of the models individually achieves the desired level of accuracy and robustness....
What is a key difference in feature engineering tasks for structured data compared to unstructured data in the context of machine learning?
What is a key difference in feature engineering tasks for structured data compared to unstructured data in the context of machine learning?A . Feature engineering for structured data is not necessary as the data is already in a usable format, whereas for unstructured data, extensive preprocessing is always requiredB ....
Which of the following interpretations of the ROC and AUC metrics is MOST ACCURATE for assessing the model’s performance?
You are a data scientist working on a binary classification model to predict whether customers will default on their loans. The dataset is highly imbalanced, with only 10% of the customers having defaulted in the past. After training the model, you need to evaluate its performance to ensure it effectively...
Which AWS service is used to store, share and manage inputs to Machine Learning models used during training and inference?
Which AWS service is used to store, share and manage inputs to Machine Learning models used during training and inference?A . Amazon SageMaker Ground TruthB . Amazon SageMaker Feature StoreC . Amazon SageMaker ClarifyD . Amazon SageMaker Data WranglerView AnswerAnswer: B Explanation: Correct option: Amazon SageMaker Feature Store Amazon SageMaker...
Which strategy is the MOST EFFECTIVE for your ML training job while minimizing cost and ensuring the job completes successfully?
You are an ML engineer at a data analytics company tasked with training a deep learning model on a large, computationally intensive dataset. The training job can tolerate interruptions and is expected to run for several hours or even days, depending on the available compute resources. The company has a...
Which of the following strategies is the MOST LIKELY to achieve an optimal balance between model performance, training time, and cost?
You are a machine learning engineer at a financial services company tasked with building a real-time fraud detection system. The model needs to be highly accurate to minimize false positives and false negatives. However, the company has a limited budget for cloud resources, and the model needs to be retrained...
Which of the following strategies should you implement to ensure a smooth and reliable deployment of the new model version using Amazon SageMaker, considering best practices for versioning and rollback strategies?
You are an ML Engineer working for a healthcare company that uses a machine learning model to recommend personalized treatment plans to patients. The model is deployed on Amazon SageMaker and is critical to the company's operations, as any incorrect predictions could have significant consequences. A new version of the...
Given these requirements, which of the following options is the MOST SUITABLE for orchestrating your ML workflow?
You are a machine learning engineer at a healthcare company responsible for developing and deploying an end-to-end ML workflow for predicting patient readmission rates. The workflow involves data preprocessing, model training, hyperparameter tuning, and deployment. Additionally, the solution must support regular retraining of the model as new data becomes available,...
Given the size and nature of the dataset, which SageMaker input mode and AWS Cloud Storage configuration is the MOST SUITABLE for this use case?
You are a data scientist at a healthcare company developing a machine learning model to analyze medical imaging data, such as X-rays and MRIs, for disease detection. The dataset consists of 10 million high-resolution images stored in Amazon S3, amounting to several terabytes of data. The training process requires processing...
