Machine Learning Specialty (Page 18)

A company that promotes healthy sleep patterns by providing cloud-connected devices currently hosts a sleep tracking application on AWS.The application collects device usage information from device users.The company’s Data Science team is building a machine learning model to predict if and when a user will stop utilizing the company’s devices.Predictions from this model are used by a downstream application that determines the best approach for contacting users.The Data Science team is building multiple versions of the machine learning model to evaluate each version against the company’s business goals.To measure long-term effectiveness, the team wants to run multiple versions of the model in parallel for long periods of time, with the ability to control the portion of inferences served by the models.Which solution satisfies these requirements with MINIMAL effort?Read More →

A machine learning (ML) specialist is administering a production Amazon SageMaker endpoint with model monitoring configured.Amazon SageMaker ModelMonitor detects violations on the SageMaker endpoint, so the ML specialist retrains the model with the latest dataset.This dataset is statistically representative of the current production traffic.The ML specialist notices that even after deploying the new SageMaker model and running the first monitoring job, the SageMaker endpoint still has violations.What should the ML specialist do to resolve the violations?Read More →

A company wants to use automatic speech recognition (ASR) to transcribe messages that are less than 60 seconds long from a voicemail-style application.The company requires the correct identification of 200 unique product names, some of which have unique spellings or pronunciations.The company has 4,000 words of Amazon SageMaker Ground Truth voicemail transcripts it can use to customize the chosen ASR model.The company needs to ensure that everyone can update their customizations multiple times each hour.Which approach will maximize transcription accuracy during the development phase?Read More →

An ecommerce company sends a weekly email newsletter to all of its customers.Management has hired a team of writers to create additional targeted content.A data scientist needs to identify five customer segments based on age, income, and location.The customers’ current segmentation is unknown.The data scientist previously built an XGBoost model to predict the likelihood of a customer responding to an email based on age, income, and location.Why does the XGBoost model NOT meet the current requirements, and how can this be fixed?Read More →

A machine learning (ML) specialist wants to bring a custom training algorithm to Amazon SageMaker.The ML specialist implements the algorithm in a Docker container that is supported by SageMaker.How should the ML specialist package the Docker container so that SageMaker can launch the training correctly?Read More →

A company is building a demand forecasting model based on machine learning (ML).In the development stage, an ML specialist uses an Amazon SageMaker notebook to perform feature engineering during work hours that consumes low amounts of CPU and memory resources.A data engineer uses the same notebook to perform data preprocessing once a day on average that requires very high memory and completes in only 2 hours.The data preprocessing is not configured to use GPU.All the processes are running well on an ml.m5.4xlarge notebook instance.The company receives an AWS Budgets alert that the billing for this month exceeds the allocated budget.Which solution will result in the MOST cost savings?Read More →

A company is building a new version of a recommendation engine.Machine learning (ML) specialists need to keep adding new data from users to improve personalized recommendations.The ML specialists gather data from the users’ interactions on the platform and from sources such as external websites and social media.The pipeline cleans, transforms, enriches, and compresses terabytes of data daily, and this data is stored in Amazon S3.A set of Python scripts was coded to do the job and is stored in a large Amazon EC2 instance.The whole process takes more than 20 hours to finish, with each script taking at least an hour.The company wants to move the scripts out of Amazon EC2 into a more managed solution that will eliminate the need to maintain servers.Which approach will address all of these requirements with the LEAST development effort?Read More →

A machine learning (ML) engineer at a bank is building a data ingestion solution to provide transaction features to financial ML models.Raw transactional data is available in an Amazon Kinesis data stream.The solution must compute rolling averages of the ingested data from the data stream and must store the results in Amazon SageMaker Feature Store.The solution also must serve the results to the models in near real time.Which solution will meet these requirements?Read More →

A company is building custom deep learning models in Amazon SageMaker by using training and inference containers that run on Amazon EC2 instances.The company wants to reduce training costs but does not want to change the current architecture.The SageMaker training job can finish after interruptions.The company can wait days for the results.Which combination of resources should the company use to meet these requirements MOST cost-effectively? (Choose two.)Read More →

A company operates large cranes at a busy port The company plans to use machine learning (ML) for predictive maintenance of the cranes to avoid unexpected breakdowns and to improve productivity.The company already uses sensor data from each crane to monitor the health of the cranes in real time. The sensor data includes rotation speed, tension, energy consumption, vibration, pressure, and temperature for each crane. The company contracts AWS ML experts to implement an ML solution.Which potential findings would indicate that an ML-based solution is suitable for this scenario? (Choose two.)Read More →