A machine learning specialist is developing a proof of concept for government users whose primary concern is security. The specialist is using Amazon
SageMaker to train a convolutional neural network (CNN) model for a photo classifier application. The specialist wants to protect the data so that it cannot be accessed and transferred to a remote host by malicious code accidentally installed on the training container.
Which action will provide the MOST secure protection?
D
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?
B
A company is using Amazon Textract to extract textual data from thousands of scanned text-heavy legal documents daily. The company uses this information to process loan applications automatically. Some of the documents fail business validation and are returned to human reviewers, who investigate the errors. This activity increases the time to process the loan applications.
What should the company do to reduce the processing time of loan applications?
C
A company ingests machine learning (ML) data from web advertising clicks into an Amazon S3 data lake. Click data is added to an Amazon Kinesis data stream by using the Kinesis Producer Library (KPL). The data is loaded into the S3 data lake from the data stream by using an Amazon Kinesis Data Firehose delivery stream. As the data volume increases, an ML specialist notices that the rate of data ingested into Amazon S3 is relatively constant. There also is an increasing backlog of data for Kinesis Data Streams and Kinesis Data Firehose to ingest.
Which next step is MOST likely to improve the data ingestion rate into Amazon S3?
C
A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist.
How should the data scientist split the dataset into a training and test set for this use case?
D
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.)
BDF
A company needs to quickly make sense of a large amount of data and gain insight from it. The data is in different formats, the schemas change frequently, and new data sources are added regularly. The company wants to use AWS services to explore multiple data sources, suggest schemas, and enrich and transform the data. The solution should require the least possible coding effort for the data flows and the least possible infrastructure management.
Which combination of AWS services will meet these requirements?
A.
✑ Amazon EMR for data discovery, enrichment, and transformation
✑ Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL
✑ Amazon QuickSight for reporting and getting insights
B.
✑ Amazon Kinesis Data Analytics for data ingestion
✑ Amazon EMR for data discovery, enrichment, and transformation
✑ Amazon Redshift for querying and analyzing the results in Amazon S3
C.
✑ AWS Glue for data discovery, enrichment, and transformation
✑ Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL
✑ Amazon QuickSight for reporting and getting insights
D.
✑ AWS Data Pipeline for data transfer
✑ AWS Step Functions for orchestrating AWS Lambda jobs for data discovery, enrichment, and transformation
✑ Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL
✑ Amazon QuickSight for reporting and getting insights
A
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?
C
Reference:
https://aws.amazon.com/blogs/machine-learning/building-an-nlp-powered-search-index-with-amazon-textract-and-amazon-comprehend/
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?
D
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?
C
Reference:
https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-software-updates.html
