Machine Learning Specialty (Page 26)

A data scientist is working on a model to predict a company’s required inventory stock levels.All historical data is stored in .csv files in the company’s data lake on Amazon S3.The dataset consists of approximately 500 GB of data The data scientist wants to use SQL to explore the data before training the model.The company wants to minimize costs.Which option meets these requirements with the LEAST operational overhead?Read More →

A company is building a machine learning (ML) model to classify images of plants.An ML specialist has trained the model using the Amazon SageMaker built-in Image Classification algorithm.The model is hosted using a SageMaker endpoint on an ml.m5.xlarge instance for real-time inference.When used by researchers in the field, the inference has greater latency than is acceptable.The latency gets worse when multiple researchers perform inference at the same time on their devices.Using Amazon CloudWatch metrics, the ML specialist notices that the ModelLatency metric shows a high value and is responsible for most of the response latency.The ML specialist needs to fix the performance issue so that researchers can experience less latency when performing inference from their devices.Which action should the ML specialist take to meet this requirement?Read More →

A company’s machine learning (ML) specialist is designing a scalable data storage solution for Amazon SageMaker.The company has an existing TensorFlow-based model that uses a train.py script.The model relies on static training data that is currently stored in TFRecord format.What should the ML specialist do to provide the training data to SageMaker with the LEAST development overhead?Read More →

An ecommerce company wants to train a large image classification model with 10,000 classes.The company runs multiple model training iterations and needs to minimize operational overhead and cost.The company also needs to avoid loss of work and model retraining.Which solution will meet these requirements?Read More →

A retail company uses a machine learning (ML) model for daily sales forecasting.The model has provided inaccurate results for the past 3 weeks.At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model.The AWS Glue job stores the data in Amazon S3.The company’s ML team determines that the inaccuracies are occurring because of a change in the value distributions of the model features.The ML team must implement a solution that will detect when this type of change occurs in the future.Which solution will meet these requirements with the LEAST amount of operational overhead?Read More →

A finance company needs to forecast the price of a commodity.The company has compiled a dataset of historical daily prices.A data scientist must train various forecasting models on 80% of the dataset and must validate the efficacy of those models on the remaining 20% of the dataset.How should the data scientist split the dataset into a training dataset and a validation dataset to compare model performance?Read More →

A retail company wants to build a recommendation system for the company’s website.The system needs to provide recommendations for existing users and needs to base those recommendations on each user’s past browsing history.The system also must filter out any items that the user previously purchased.Which solution will meet these requirements with the LEAST development effort?Read More →

A bank wants to use a machine learning (ML) model to predict if users will default on credit card payments.The training data consists of 30,000 labeled records and is evenly balanced between two categories.For the model, an ML specialist selects the Amazon SageMaker built-in XGBoost algorithm and configures a SageMaker automatic hyperparameter optimization job with the Bayesian method.The ML specialist uses the validation accuracy as the objective metric.When the bank implements the solution with this model, the prediction accuracy is 75%.The bank has given the ML specialist 1 day to improve the model in production.Which approach is the FASTEST way to improve the model’s accuracy?Read More →

A company is building a pipeline that periodically retrains its machine learning (ML) models by using new streaming data from devices.The company’s data engineering team wants to build a data ingestion system that has high throughput, durable storage, and scalability.The company can tolerate up to 5 minutes of latency for data ingestion.The company needs a solution that can apply basic data transformation during the ingestion process.Which solution will meet these requirements with the MOST operational efficiency?Read More →

A manufacturing company wants to monitor its devices for anomalous behavior.A data scientist has trained an Amazon SageMaker scikit-learn model that classifies a device as normal or anomalous based on its 4-day telemetry.The 4-day telemetry of each device is collected in a separate file and is placed in an Amazon S3 bucket once every hour.The total time to run the model across the telemetry for all devices is 5 minutes.What is the MOST cost-effective solution for the company to use to run the model across the telemetry for all the devices?Read More →