Practice Set 26

A data scientist wants to build a financial trading bot to automate investment decisions. The financial bot should recommend the quantity and price of an asset to buy or sell to maximize long-term profit. The data scientist will continuously stream financial transactions to the bot for training purposes. The data scientist must select the appropriate machine learning (ML) algorithm to develop the financial trading bot.Which type of ML algorithm will meet these requirements? [{"voted_answers": "D", "vote_count": 16, "is_most_voted": true}]

A manufacturing company wants to create a machine learning (ML) model to predict when equipment is likely to fail. A data science team already constructed a deep learning model by using TensorFlow and a custom Python script in a local environment. The company wants to use Amazon SageMaker to train the model.Which TensorFlow estimator configuration will train the model MOST cost-effectively? [{"voted_answers": "B", "vote_count": 12, "is_most_voted": true}]

An automotive company uses computer vision in its autonomous cars. The company trained its object detection models successfully by using transfer learning from a convolutional neural network (CNN). The company trained the models by using PyTorch through the Amazon SageMaker SDK.The vehicles have limited hardware and compute power. The company wants to optimize the model to reduce memory, battery, and hardware consumption without a significant sacrifice in accuracy.Which solution will improve the computational efficiency of the models? [{"voted_answers": "C", "vote_count": 10, "is_most_voted": true}]

A data scientist wants to improve the fit of a machine learning (ML) model that predicts house prices. The data scientist makes a first attempt to fit the model, but the fitted model has poor accuracy on both the training dataset and the test dataset.Which steps must the data scientist take to improve model accuracy? (Choose three.) [{"voted_answers": "BCE", "vote_count": 15, "is_most_voted": true}, {"voted_answers": "ACE", "vote_count": 3, "is_most_voted": false}, {"voted_answers": "CDE", "vote_count": 1, "is_most_voted": false}]

A car company is developing a machine learning solution to detect whether a car is present in an image. The image dataset consists of one million images. Each image in the dataset is 200 pixels in height by 200 pixels in width. Each image is labeled as either having a car or not having a car.Which architecture is MOST likely to produce a model that detects whether a car is present in an image with the highest accuracy? [{"voted_answers": "B", "vote_count": 9, "is_most_voted": true}, {"voted_answers": "A", "vote_count": 4, "is_most_voted": false}]

A company is creating an application to identify, count, and classify animal images that are uploaded to the company’s website. The company is using the Amazon SageMaker image classification algorithm with an ImageNetV2 convolutional neural network (CNN). The solution works well for most animal images but does not recognize many animal species that are less common.The company obtains 10,000 labeled images of less common animal species and stores the images in Amazon S3. A machine learning (ML) engineer needs to incorporate the images into the model by using Pipe mode in SageMaker.Which combination of steps should the ML engineer take to train the model? (Choose two.) [{"voted_answers": "DE", "vote_count": 15, "is_most_voted": true}, {"voted_answers": "CD", "vote_count": 15, "is_most_voted": false}]

A music streaming company is building a pipeline to extract features. The company wants to store the features for offline model training and online inference. The company wants to track feature history and to give the company’s data science teams access to the features.Which solution will meet these requirements with the MOST operational efficiency? [{"voted_answers": "A", "vote_count": 11, "is_most_voted": true}, {"voted_answers": "B", "vote_count": 1, "is_most_voted": false}]

A beauty supply store wants to understand some characteristics of visitors to the store. The store has security video recordings from the past several years. The store wants to generate a report of hourly visitors from the recordings. The report should group visitors by hair style and hair color.Which solution will meet these requirements with the LEAST amount of effort? [{"voted_answers": "A", "vote_count": 10, "is_most_voted": true}, {"voted_answers": "C", "vote_count": 9, "is_most_voted": false}, {"voted_answers": "D", "vote_count": 1, "is_most_voted": false}]

A financial services company wants to automate its loan approval process by building a machine learning (ML) model. Each loan data point contains credit history from a third-party data source and demographic information about the customer. Each loan approval prediction must come with a report that contains an explanation for why the customer was approved for a loan or was denied for a loan. The company will use Amazon SageMaker to build the model.Which solution will meet these requirements with the LEAST development effort? [{"voted_answers": "C", "vote_count": 5, "is_most_voted": true}]

A financial company sends special offers to customers through weekly email campaigns. A bulk email marketing system takes the list of email addresses as an input and sends the marketing campaign messages in batches. Few customers use the offers from the campaign messages. The company does not want to send irrelevant offers to customers.A machine learning (ML) team at the company is using Amazon SageMaker to build a model to recommend specific offers to each customer based on the customer's profile and the offers that the customer has accepted in the past.Which solution will meet these requirements with the MOST operational efficiency? [{"voted_answers": "D", "vote_count": 11, "is_most_voted": true}, {"voted_answers": "C", "vote_count": 6, "is_most_voted": false}]