MLS-C01 (Page 13)

A machine learning (ML) specialist uploads a dataset to an Amazon S3 bucket that is protected by server-side encryption with AWS KMS keys (SSE-KMS).The ML specialist needs to ensure that an Amazon SageMaker notebook instance can read the dataset that is in Amazon S3.Which solution will meet these requirements?Read More →

A Data Scientist is building a model to predict customer churn using a dataset of 100 continuous numerical features.The Marketing team has not provided any insight about which features are relevant for churn prediction.The Marketing team wants to interpret the model and see the direct impact of relevant features on the model outcome.While training a logistic regression model, the Data Scientist observes that there is a wide gap between the training and validation set accuracy.Which methods can the Data Scientist use to improve the model performance and satisfy the Marketing team’s needs? (Choose two.)Read More →

An analytics company has an Amazon SageMaker hosted endpoint for an image classification model.The model is a custom-built convolutional neural network (CNN) and uses the PyTorch deep learning framework.The company wants to increase throughput and decrease latency for customers that use the model.Which solution will meet these requirements MOST cost-effectively?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 →

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 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 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 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 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 →

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 →