MLS-C01 (Page 24)

A financial services company is building a robust serverless data lake on Amazon S3.The data lake should be flexible and meet the following requirements:✑ Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.✑ Support event-driven ETL pipelines✑ Provide a quick and easy way to understand metadataWhich approach meets these requirements?Read More →

An automotive company is using computer vision in its autonomous cars.The company has trained its models successfully by using transfer learning from a convolutional neural network (CNN).The models are trained with PyTorch through the use of the Amazon SageMaker SDK.The company wants to reduce the time that is required for performing inferences, given the low latency that is required for self-driving.Which solution should the company use to evaluate and improve the performance of the models?Read More →

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?Read More →

A medical imaging company wants to train a computer vision model to detect areas of concern on patients’ CT scans.The company has a large collection of unlabeled CT scans that are linked to each patient and stored in an Amazon S3 bucket.The scans must be accessible to authorized users only.A machine learning engineer needs to build a labeling pipeline.Which set of steps should the engineer take to build the labeling pipeline with the LEAST effort?Read More →

A machine learning (ML) specialist wants to secure calls to the Amazon SageMaker Service API.The specialist has configured Amazon VPC with a VPC interface endpoint for the Amazon SageMaker Service API and is attempting to secure traffic from specific sets of instances and IAM users.The VPC is configured with a single public subnet.Which combination of steps should the ML specialist take to secure the traffic? (Choose two.)Read More →

A company is using Amazon SageMaker to build a machine learning (ML) model to predict customer churn based on customer call transcripts.Audio files from customer calls are located in an on-premises VoIP system that has petabytes of recorded calls.The on-premises infrastructure has high-velocity networking and connects to the company’s AWS infrastructure through a VPN connection over a 100 Mbps connection.The company has an algorithm for transcribing customer calls that requires GPUs for inference.The company wants to store these transcriptions in an Amazon S3 bucket in the AWS Cloud for model development.Which solution should an ML specialist use to deliver the transcriptions to the S3 bucket as quickly as possible?Read More →

A data scientist has 20 TB of data in CSV format in an Amazon S3 bucket.The data scientist needs to convert the data to Apache Parquet format.How can the data scientist convert the file format with the LEAST amount of effort?Read More →

A logistics company needs a forecast model to predict next month’s inventory requirements for a single item in 10 warehouses.A machine learning specialist usesAmazon Forecast to develop a forecast model from 3 years of monthly data.There is no missing data.The specialist selects the DeepAR+ algorithm to train a predictor.The predictor means absolute percentage error (MAPE) is much larger than the MAPE produced by the current human forecasters.Which changes to the CreatePredictor API call could improve the MAPE? (Choose two.)Read More →

A financial company is trying to detect credit card fraud.The company observed that, on average, 2% of credit card transactions were fraudulent.A data scientist trained a classifier on a year’s worth of credit card transactions data.The model needs to identify the fraudulent transactions (positives) from the regular ones(negatives).The company’s goal is to accurately capture as many positives as possible.Which metrics should the data scientist use to optimize the model? (Choose two.)Read More →

A manufacturing company wants to use machine learning (ML) to automate quality control in its facilities.The facilities are in remote locations and have limited internet connectivity.The company has 20 ׀¢׀’ of training data that consists of labeled images of defective product parts.The training data is in the corporate on- premises data center.The company will use this data to train a model for real-time defect detection in new parts as the parts move on a conveyor belt in the facilities.The company needs a solution that minimizes costs for compute infrastructure and that maximizes the scalability of resources for training.The solution also must facilitate the company’s use of an ML model in the low-connectivity environments.Which solution will meet these requirements?Read More →