Practice Set 14

A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company's data scientists run machine learning(ML) models on confidential financial data. The company is worried about data egress and wants an ML engineer to secure the environment.Which mechanisms can the ML engineer use to control data egress from SageMaker? (Choose three.) [{"voted_answers": "ADE", "vote_count": 20, "is_most_voted": true}, {"voted_answers": "ADF", "vote_count": 10, "is_most_voted": false}, {"voted_answers": "ABD", "vote_count": 2, "is_most_voted": false}, {"voted_answers": "DEF", "vote_count": 1, "is_most_voted": false}]

A company is converting a large number of unstructured paper receipts into images. The company wants to create a model based on natural language processing(NLP) to find relevant entities such as date, location, and notes, as well as some custom entities such as receipt numbers.The company is using optical character recognition (OCR) to extract text for data labeling. However, documents are in different structures and formats, and the company is facing challenges with setting up the manual workflows for each document type. Additionally, the company trained a named entity recognition (NER) model for custom entity detection using a small sample size. This model has a very low confidence score and will require retraining with a large dataset.Which solution for text extraction and entity detection will require the LEAST amount of effort? [{"voted_answers": "C", "vote_count": 7, "is_most_voted": true}]

A company is building a predictive maintenance model based on machine learning (ML). The data is stored in a fully private Amazon S3 bucket that is encrypted at rest with AWS Key Management Service (AWS KMS) CMKs. An ML specialist must run data preprocessing by using an Amazon SageMaker Processing job that is triggered from code in an Amazon SageMaker notebook. The job should read data from Amazon S3, process it, and upload it back to the same S3 bucket.The preprocessing code is stored in a container image in Amazon Elastic Container Registry (Amazon ECR). The ML specialist needs to grant permissions to ensure a smooth data preprocessing workflow.Which set of actions should the ML specialist take to meet these requirements? [{"voted_answers": "B", "vote_count": 31, "is_most_voted": true}, {"voted_answers": "A", "vote_count": 25, "is_most_voted": false}]

A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker.How can the data scientist meet this requirements? [{"voted_answers": "C", "vote_count": 19, "is_most_voted": true}]

A library is developing an automatic book-borrowing system that uses Amazon Rekognition. Images of library members' faces are stored in an Amazon S3 bucket.When members borrow books, the Amazon Rekognition CompareFaces API operation compares real faces against the stored faces in Amazon S3.The library needs to improve security by making sure that images are encrypted at rest. Also, when the images are used with Amazon Rekognition. they need to be encrypted in transit. The library also must ensure that the images are not used to improve Amazon Rekognition as a service.How should a machine learning specialist architect the solution to satisfy these requirements? [{"voted_answers": "A", "vote_count": 10, "is_most_voted": true}, {"voted_answers": "D", "vote_count": 2, "is_most_voted": false}, {"voted_answers": "B", "vote_count": 2, "is_most_voted": false}]

A company is building a line-counting application for use in a quick-service restaurant. The company wants to use video cameras pointed at the line of customers at a given register to measure how many people are in line and deliver notifications to managers if the line grows too long. The restaurant locations have limited bandwidth for connections to external services and cannot accommodate multiple video streams without impacting other operations.Which solution should a machine learning specialist implement to meet these requirements? [{"voted_answers": "D", "vote_count": 27, "is_most_voted": true}, {"voted_answers": "C", "vote_count": 19, "is_most_voted": false}, {"voted_answers": "B", "vote_count": 14, "is_most_voted": false}, {"voted_answers": "A", "vote_count": 4, "is_most_voted": false}]

A company has set up and deployed its machine learning (ML) model into production with an endpoint using Amazon SageMaker hosting services. The ML team has configured automatic scaling for its SageMaker instances to support workload changes. During testing, the team notices that additional instances are being launched before the new instances are ready. This behavior needs to change as soon as possible.How can the ML team solve this issue? [{"voted_answers": "D", "vote_count": 22, "is_most_voted": true}]

A telecommunications company is developing a mobile app for its customers. The company is using an Amazon SageMaker hosted endpoint for machine learning model inferences.Developers want to introduce a new version of the model for a limited number of users who subscribed to a preview feature of the app. After the new version of the model is tested as a preview, developers will evaluate its accuracy. If a new version of the model has better accuracy, developers need to be able to gradually release the new version for all users over a fixed period of time.How can the company implement the testing model with the LEAST amount of operational overhead? [{"voted_answers": "C", "vote_count": 35, "is_most_voted": true}, {"voted_answers": "A", "vote_count": 27, "is_most_voted": false}]

A company offers an online shopping service to its customers. The company wants to enhance the site's security by requesting additional information when customers access the site from locations that are different from their normal location. The company wants to update the process to call a machine learning (ML) model to determine when additional information should be requested.The company has several terabytes of data from its existing ecommerce web servers containing the source IP addresses for each request made to the web server. For authenticated requests, the records also contain the login name of the requesting user.Which approach should an ML specialist take to implement the new security feature in the web application? [{"voted_answers": "B", "vote_count": 13, "is_most_voted": true}]