Practice Set 37

A machine learning (ML) specialist is building a credit score model for a financial institution. The ML specialist has collected data for the previous 3 years of transactions and third-party metadata that is related to the transactions.After the ML specialist builds the initial model, the ML specialist discovers that the model has low accuracy for both the training data and the test data. The ML specialist needs to improve the accuracy of the model.Which solutions will meet this requirement? (Choose two.) [{"voted_answers": "AC", "vote_count": 1, "is_most_voted": false}]

A data scientist uses Amazon SageMaker to perform hyperparameter tuning for a prototype machine leaming (ML) model. The data scientist's domain knowledge suggests that the hyperparameter is highly sensitive to changes.The optimal value, x, is in the 0.5 < x < 1.0 range. The data scientist's domain knowledge suggests that the optimal value is close to 1.0.The data scientist needs to find the optimal hyperparameter value with a minimum number of runs and with a high degree of consistent tuning conditions.Which hyperparameter scaling type should the data scientist use to meet these requirements? [{"voted_answers": "D", "vote_count": 3, "is_most_voted": false}]

A data scientist uses Amazon SageMaker Data Wrangler to analyze and visualize data. The data scientist wants to refine a training dataset by selecting predictor variables that are strongly predictive of the target variable. The target variable correlates with other predictor variables.The data scientist wants to understand the variance in the data along various directions in the feature space.Which solution will meet these requirements? [{"voted_answers": "C", "vote_count": 2, "is_most_voted": true}]

A business to business (B2B) ecommerce company wants to develop a fair and equitable risk mitigation strategy to reject potentially fraudulent transactions. The company wants to reject fraudulent transactions despite the possibility of losing some profitable transactions or customers.Which solution will meet these requirements with the LEAST operational effort? [{"voted_answers": "C", "vote_count": 5, "is_most_voted": true}, {"voted_answers": "D", "vote_count": 4, "is_most_voted": false}]

A data scientist needs to develop a model to detect fraud. The data scientist has less data for fraudulent transactions than for legitimate transactions.The data scientist needs to check for bias in the model before finalizing the model. The data scientist needs to develop the model quickly.Which solution will meet these requirements with the LEAST operational overhead? [{"voted_answers": "C", "vote_count": 2, "is_most_voted": false}]

A company has 2,000 retail stores. The company needs to develop a new model to predict demand based on holidays and weather conditions. The model must predict demand in each geographic area where the retail stores are located.Before deploying the newly developed model, the company wants to test the model for 2 to 3 days. The model needs to be robust enough to adapt to supply chain and retail store requirements.Which combination of steps should the company take to meet these requirements with the LEAST operational overhead? (Choose two.) [{"voted_answers": "BC", "vote_count": 4, "is_most_voted": true}, {"voted_answers": "BE", "vote_count": 3, "is_most_voted": false}, {"voted_answers": "AB", "vote_count": 1, "is_most_voted": false}]

A finance company has collected stock return data for 5,000 publicly traded companies. A financial analyst has a dataset that contains 2,000 attributes for each company. The financial analyst wants to use Amazon SageMaker to identify the top 15 attributes that are most valuable to predict future stock returns.Which solution will meet these requirements with the LEAST operational overhead? [{"voted_answers": "C", "vote_count": 5, "is_most_voted": true}, {"voted_answers": "D", "vote_count": 3, "is_most_voted": false}]

A company is using a machine learning (ML) model to recommend products to customers. An ML specialist wants to analyze the data for the most popular recommendations in four dimensions.The ML specialist will visualize the first two dimensions as coordinates. The third dimension will be visualized as color. The ML specialist will use size to represent the fourth dimension in the visualizationWhich solution will meet these requirements? [{"voted_answers": "D", "vote_count": 1, "is_most_voted": false}]

A clothing company is experimenting with different colors and materials for its products. The company stores the entire sales history of all its products in Amazon S3. The company is using custom-built exponential smoothing (ETS) models to forecast demand for its current products. The company needs to forecast the demand for a new product variation that the company will launch soon.Which solution will meet these requirements? [{"voted_answers": "B", "vote_count": 2, "is_most_voted": true}]