Machine Learning Specialty (Page 8)

A company wants to create an artificial intelligence (AШ) yoga instructor that can lead large classes of students.The company needs to create a feature that can accurately count the number of students who are in a class.The company also needs a feature that can differentiate students who are performing a yoga stretch correctly from students who are performing a stretch incorrectly.Determine whether students are performing a stretch correctly, the solution needs to measure the location and angle of each student’s arms and legs.A data scientist must use Amazon SageMaker to access video footage of a yoga class by extracting image frames and applying computer vision models.Which combination of models will meet these requirements with the LEAST effort? (Choose two.)Read More →

A social media company wants to develop a machine learning (ML) model to detect inappropriate or offensive content in images.The company has collected a large dataset of labeled images and plans to use the built-in Amazon SageMaker image classification algorithm to train the model.The company also intends to use SageMaker pipe mode to speed up the training.The company splits the dataset into training, validation, and testing datasets.The company stores the training and validation images in folders that are named Training and Validation, respectively.The folders contain subfolders that correspond to the names of the dataset classes.The company resizes the images to the same size and generates two input manifest files named training.lst and validation.lst, for the training dataset and the validation dataset, respectively.Finally, the company creates two separate Amazon S3 buckets for uploads of the training dataset and the validation dataset.Which additional data preparation steps should the company take before uploading the files to Amazon S3?Read More →

A medical device company is building a machine learning (ML) model to predict the likelihood of device recall based on customer data that the company collects from a plain text survey.One of the survey questions asks which medications the customer is taking.The data for this field contains the names of medications that customers enter manually.Customers misspell some of the medication names.The column that contains the medication name data gives a categorical feature with high cardinality but redundancy.What is the MOST effective way to encode this categorical feature into a numeric feature?Read More →

A data science team is working with a tabular dataset that the team stores in Amazon S3.The team wants to experiment with different feature transformations such as categorical feature encoding.Then the team wants to visualize the resulting distribution of the dataset.After the team finds an appropriate set of feature transformations, the team wants to automate the workflow for feature transformations.Which solution will meet these requirements with the MOST operational efficiency?Read More →

A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models.As DataScientists may create an arbitrary number of new datasets every day, the solution has to scale automatically and be cost-effective.Also, it must be possible to explore the data using SQL.Which storage scheme is MOST adapted to this scenario?Read More →

A Machine Learning Specialist deployed a model that provides product recommendations on a company’s website.Initially, the model was performing very well and resulted in customers buying more products on average.However, within the past few months, the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less.The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago.Which method should the Specialist try to improve model performance?Read More →

A data engineer is preparing a dataset that a retail company will use to predict the number of visitors to stores.The data engineer created an Amazon S3 bucket.The engineer subscribed the S3 bucket to an AWS Data Exchange data product for general economic indicators.The data engineer wants to join the economic indicator data to an existing table in Amazon Athena to merge with the business data.All these transformations must finish running in 30-60 minutes.Which solution will meet these requirements MOST cost-effectively?Read More →

A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server-side encryption using AWS KMS.How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3?Read More →

A Machine Learning Specialist is working for an online retailer that wants to run analytics on every customer visit, processed through a machine learning pipeline.The data needs to be ingested by Amazon Kinesis Data Streams at up to 100 transactions per second, and the JSON data blob is 100 KB in size.What is the MINIMUM number of shards in Kinesis Data Streams the Specialist should use to successfully ingest this data?Read More →

A machine learning specialist is developing a proof of concept for government users whose primary concern is security.The specialist is using AmazonSageMaker to train a convolutional neural network (CNN) model for a photo classifier application.The specialist wants to protect the data so that it cannot be accessed and transferred to a remote host by malicious code accidentally installed on the training container.Which action will provide the MOST secure protection?Read More →