Practice Set 10

A manufacturer of car engines collects data from cars as they are being driven. The data collected includes timestamp, engine temperature, rotations per minute(RPM), and other sensor readings. The company wants to predict when an engine is going to have a problem, so it can notify drivers in advance to get engine maintenance. The engine data is loaded into a data lake for training.Which is the MOST suitable predictive model that can be deployed into production? [{"voted_answers": "A", "vote_count": 11, "is_most_voted": true}]

A company wants to predict the sale prices of houses based on available historical sales data. The target variable in the company's dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to use multi-variable linear regression to predict house sale prices.Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model's complexity? [{"voted_answers": "D", "vote_count": 14, "is_most_voted": true}, {"voted_answers": "B", "vote_count": 2, "is_most_voted": false}]

A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a machine learning specialist will build a binary classifier based on two features: age of account, denoted by x, and transaction month, denoted by y. The class distributions are illustrated in the provided figure. The positive class is portrayed in red, while the negative class is portrayed in black.Which model would have the HIGHEST accuracy? [{"voted_answers": "C", "vote_count": 16, "is_most_voted": true}, {"voted_answers": "B", "vote_count": 15, "is_most_voted": false}]

A health care company is planning to use neural networks to classify their X-ray images into normal and abnormal classes. The labeled data is divided into a training set of 1,000 images and a test set of 200 images. The initial training of a neural network model with 50 hidden layers yielded 99% accuracy on the training set, but only 55% accuracy on the test set.What changes should the Specialist consider to solve this issue? (Choose three.) [{"voted_answers": "BDF", "vote_count": 8, "is_most_voted": true}, {"voted_answers": "BCE", "vote_count": 1, "is_most_voted": false}]

This graph shows the training and validation loss against the epochs for a neural network.The network being trained is as follows:✑ Two dense layers, one output neuron✑ 100 neurons in each layer✑ 100 epochsRandom initialization of weightsWhich technique can be used to improve model performance in terms of accuracy in the validation set? [{"voted_answers": "A", "vote_count": 5, "is_most_voted": true}]

A Machine Learning Specialist is attempting to build a linear regression model.Given the displayed residual plot only, what is the MOST likely problem with the model? [{"voted_answers": "A", "vote_count": 6, "is_most_voted": true}, {"voted_answers": "D", "vote_count": 2, "is_most_voted": false}]

A large company has developed a BI application that generates reports and dashboards using data collected from various operational metrics. The company wants to provide executives with an enhanced experience so they can use natural language to get data from the reports. The company wants the executives to be able ask questions using written and spoken interfaces.Which combination of services can be used to build this conversational interface? (Choose three.) [{"voted_answers": "CDF", "vote_count": 28, "is_most_voted": true}, {"voted_answers": "CEF", "vote_count": 20, "is_most_voted": false}, {"voted_answers": "CDE", "vote_count": 9, "is_most_voted": false}, {"voted_answers": "DEF", "vote_count": 2, "is_most_voted": false}, {"voted_answers": "ACD", "vote_count": 1, "is_most_voted": false}]

A machine learning specialist works for a fruit processing company and needs to build a system that categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied transfer learning on a neural network that was pretrained on ImageNet with this dataset.The company requires at least 85% accuracy to make use of the model.After an exhaustive grid search, the optimal hyperparameters produced the following:✑ 68% accuracy on the training set✑ 67% accuracy on the validation setWhat can the machine learning specialist do to improve the system's accuracy? [{"voted_answers": "B", "vote_count": 17, "is_most_voted": true}, {"voted_answers": "C", "vote_count": 1, "is_most_voted": false}, {"voted_answers": "A", "vote_count": 1, "is_most_voted": false}, {"voted_answers": "D", "vote_count": 1, "is_most_voted": false}]

A company uses camera images of the tops of items displayed on store shelves to determine which items were removed and which ones still remain. After several hours of data labeling, the company has a total of 1,000 hand-labeled images covering 10 distinct items. The training results were poor.Which machine learning approach fulfills the company's long-term needs? [{"voted_answers": "D", "vote_count": 21, "is_most_voted": true}]

A Data Scientist is developing a binary classifier to predict whether a patient has a particular disease on a series of test results. The Data Scientist has data on400 patients randomly selected from the population. The disease is seen in 3% of the population.Which cross-validation strategy should the Data Scientist adopt? [{"voted_answers": "B", "vote_count": 3, "is_most_voted": true}]