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A company provisions Amazon SageMaker notebook instances for its data science teamand creates Amazon VPC interface endpoints to ensure communication between the VPCand the notebook instances. All connections to the Amazon SageMaker API are containedentirely and securely using the AWS network. However, the data science team realizes thatindividuals outside the VPC can still connect to the notebook instances across the internet.Which set of actions should the data science team take to fix the issue?
A. Modify the notebook instances' security group to allow traffic only from the CIDR rangesof the VPC. Apply this security group to all of the notebook instances' VPC interfaces.
B. Create an IAM policy that allows the sagemaker:CreatePresignedNotebooklnstanceUrland sagemaker:DescribeNotebooklnstance actions from only the VPC endpoints. Applythis policy to all IAM users, groups, and roles used to access the notebook instances.
C. Add a NAT gateway to the VPC. Convert all of the subnets where the AmazonSageMaker notebook instances are hosted to private subnets. Stop and start all of thenotebook instances to reassign only private IP addresses.
D. Change the network ACL of the subnet the notebook is hosted in to restrict access toanyone outside the VPC.
A data scientist is working on a public sector project for an urban traffic system. Whilestudying the traffic patterns, it is clear to the data scientist that the traffic behavior at eachlight is correlated, subject to a small stochastic error term. The data scientist must modelthe traffic behavior to analyze the traffic patterns and reduce congestionHow will the data scientist MOST effectively model the problem?
A. The data scientist should obtain a correlated equilibrium policy by formulating thisproblem as a multi-agent reinforcement learning problem.
B. The data scientist should obtain the optimal equilibrium policy by formulating thisproblem as a single-agent reinforcement learning problem.
C. Rather than finding an equilibrium policy, the data scientist should obtain accuratepredictors of traffic flow by using historical data through a supervised learning approach.
D. Rather than finding an equilibrium policy, the data scientist should obtain accuratepredictors of traffic flow by using unlabeled simulated data representing the new trafficpatterns in the city and applying an unsupervised learning approach.
A company is converting a large number of unstructured paper receipts into images. Thecompany wants to create a model based on natural language processing (NLP) to findrelevant entities such as date, location, and notes, as well as some custom entities such asreceipt 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 facingchallenges with setting up the manual workflows for each document type. Additionally, thecompany trained a named entity recognition (NER) model for custom entity detection usinga small sample size. This model has a very low confidence score and will require retrainingwith a large dataset.Which solution for text extraction and entity detection will require the LEAST amount ofeffort?
A. Extract text from receipt images by using Amazon Textract. Use the AmazonSageMaker BlazingText algorithm to train on the text for entities and custom entities.
B. Extract text from receipt images by using a deep learning OCR model from the AWSMarketplace. Use the NER deep learning model to extract entities.
C. Extract text from receipt images by using Amazon Textract. Use Amazon Comprehendfor entity detection, and use Amazon Comprehend custom entity recognition for customentity detection.
D. Extract text from receipt images by using a deep learning OCR model from the AWSMarketplace. Use Amazon Comprehend for entity detection, and use Amazon Comprehendcustom entity recognition for custom entity detection.
A company has set up and deployed its machine learning (ML) model into production withan endpoint using Amazon SageMaker hosting services. The ML team has configuredautomatic scaling for its SageMaker instances to support workload changes. During testing,the team notices that additional instances are being launched before the new instances areready. This behavior needs to change as soon as possible.How can the ML team solve this issue?
A. Decrease the cooldown period for the scale-in activity. Increase the configuredmaximum capacity of instances.
B. Replace the current endpoint with a multi-model endpoint using SageMaker.
C. Set up Amazon API Gateway and AWS Lambda to trigger the SageMaker inferenceendpoint.
D. Increase the cooldown period for the scale-out activity.
A power company wants to forecast future energy consumption for its customers inresidential properties and commercial business properties. Historical power consumptiondata for the last 10 years is available. A team of data scientists who performed the initialdata analysis and feature selection will include the historical power consumption data anddata such as weather, number of individuals on the property, and public holidays.The data scientists are using Amazon Forecast to generate the forecasts.Which algorithm in Forecast should the data scientists use to meet these requirements?
A. Autoregressive Integrated Moving Average (AIRMA)
B. Exponential Smoothing (ETS)
C. Convolutional Neural Network - Quantile Regression (CNN-QR)
A company ingests machine learning (ML) data from web advertising clicks into an AmazonS3 data lake. Click data is added to an Amazon Kinesis data stream by using the KinesisProducer Library (KPL). The data is loaded into the S3 data lake from the data stream byusing an Amazon Kinesis Data Firehose delivery stream. As the data volume increases, anML 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 DataFirehose to ingest.Which next step is MOST likely to improve the data ingestion rate into Amazon S3?
A. Increase the number of S3 prefixes for the delivery stream to write to.
B. Decrease the retention period for the data stream.
C. Increase the number of shards for the data stream.
D. Add more consumers using the Kinesis Client Library (KCL).
A machine learning specialist is running an Amazon SageMaker endpoint using the built-inobject detection algorithm on a P3 instance for real-time predictions in a company'sproduction application. When evaluating the model's resource utilization, the specialistnotices that the model is using only a fraction of the GPU.Which architecture changes would ensure that provisioned resources are being utilizedeffectively?
A. Redeploy the model as a batch transform job on an M5 instance.
B. Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to theinstance.
C. Redeploy the model on a P3dn instance.
D. Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) clusterusing a P3 instance.
A company wants to predict the sale prices of houses based on available historical salesdata. The targetvariable in the company’s dataset is the sale price. The features include parameters suchas the lot size, livingarea measurements, non-living area measurements, number of bedrooms, number ofbathrooms, year built,and postal code. The company wants to use multi-variable linear regression to predicthouse sale prices.Which step should a machine learning specialist take to remove features that are irrelevantfor the analysisand reduce the model’s complexity?
A. Plot a histogram of the features and compute their standard deviation. Remove featureswith high variance.
B. Plot a histogram of the features and compute their standard deviation. Remove featureswith low variance.
C. Build a heatmap showing the correlation of the dataset against itself. Remove featureswith low mutual correlation scores.
D. Run a correlation check of all features against the target variable. Remove features withlow target variable correlation scores.
A data scientist is developing a pipeline to ingest streaming web traffic data. The datascientist needs toimplement a process to identify unusual web traffic patterns as part of the pipeline. Thepatterns will be useddownstream for alerting and incident response. The data scientist has access to unlabeledhistoric data to use,if needed.The solution needs to do the following:Calculate an anomaly score for each web traffic entry.Adapt unusual event identification to changing web patterns over time.Which approach should the data scientist implement to meet these requirements?
A. Use historic web traffic data to train an anomaly detection model using the AmazonSageMaker Random Cut Forest (RCF) built-in model. Use an Amazon Kinesis Data Stream to process theincoming web traffic data. Attach a preprocessing AWS Lambda function to perform data enrichment by callingthe RCF model to calculate the anomaly score for each record.
B. Use historic web traffic data to train an anomaly detection model using the AmazonSageMaker built-in XGBoost model. Use an Amazon Kinesis Data Stream to process the incoming web trafficdata. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the XGBoostmodel to calculate the anomaly score for each record.
C. Collect the streaming data using Amazon Kinesis Data Firehose. Map the deliverystream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time againstthe streaming data with the k-Nearest Neighbors (kNN) SQL extension to calculate anomaly scores for eachrecord using a tumbling window.
D. Collect the streaming data using Amazon Kinesis Data Firehose. Map the deliverystream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time againstthe streaming data with the Amazon Random Cut Forest (RCF) SQL extension to calculate anomaly scores foreach record using a sliding window.
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 areadded regularly. The company wants to use AWS services to explore multiple datasources, suggest schemas, and enrich and transform the data. The solution should requirethe least possible coding effort for the data flows and the least possible infrastructuremanagement.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 Machine Learning Specialist is deciding between building a naive Bayesian model or afull Bayesian networkfor a classification problem. The Specialist computes the Pearson correlation coefficientsbetween eachfeature and finds that their absolute values range between 0.1 to 0.95.Which model describes the underlying data in this situation?
A. A naive Bayesian model, since the features are all conditionally independent.
B. A full Bayesian network, since the features are all conditionally independent.
C. A naive Bayesian model, since some of the features are statistically dependent.
D. A full Bayesian network, since some of the features are statistically dependent.
A retail company wants to combine its customer orders with the product description datafrom its product catalog. The structure and format of the records in each dataset isdifferent. A data analyst tried to use a spreadsheet to combine the datasets, but the effortresulted in duplicate records and records that were not properly combined. The companyneeds a solution that it can use to combine similar records from the two datasets andremove any duplicates.Which solution will meet these requirements?
A. Use an AWS Lambda function to process the data. Use two arrays to compare equalstrings in the fields from the two datasets and remove any duplicates.
B. Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Callthe AWS Glue SearchTables API operation to perform a fuzzy-matching search on the twodatasets, and cleanse the data accordingly.
C. Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Usethe FindMatches transform to cleanse the data.
D. Create an AWS Lake Formation custom transform. Run a transformation for matchingproducts from the Lake Formation console to cleanse the data automatically.
A logistics company needs a forecast model to predict next month's inventory requirementsfor a single item in 10 warehouses. A machine learning specialist uses Amazon Forecast todevelop a forecast model from 3 years of monthly data. There is no missing data. Thespecialist selects the DeepAR+ algorithm to train a predictor. The predictor means absolutepercentage error (MAPE) is much larger than the MAPE produced by the current humanforecasters.Which changes to the CreatePredictor API call could improve the MAPE? (Choose two.)
A. Set PerformAutoML to true.
B. Set ForecastHorizon to 4.
C. Set ForecastFrequency to W for weekly.
D. Set PerformHPO to true.
E. Set FeaturizationMethodName to filling.
A library is developing an automatic book-borrowing system that uses AmazonRekognition. Images of library members’ faces are stored in an Amazon S3 bucket. Whenmembers borrow books, the Amazon Rekognition CompareFaces API operation comparesreal 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 intransit. The library also must ensure that the images are not used to improve AmazonRekognition as a service.How should a machine learning specialist architect the solution to satisfy theserequirements?
A. Enable server-side encryption on the S3 bucket. Submit an AWS Support ticket to optout of allowing images to be used for improving the service, and follow the processprovided by AWS Support.
B. Switch to using an Amazon Rekognition collection to store the images. Use theIndexFaces and SearchFacesByImage API operations instead of the CompareFaces APIoperation.
C. Switch to using the AWS GovCloud (US) Region for Amazon S3 to store images and forAmazon Rekognition to compare faces. Set up a VPN connection and only call the AmazonRekognition API operations through the VPN.
D. Enable client-side encryption on the S3 bucket. Set up a VPN connection and only callthe Amazon Rekognition API operations through the VPN.
A Data Scientist is building a model to predict customer churn using a dataset of 100continuous numericalfeatures. The Marketing team has not provided any insight about which features arerelevant for churnprediction. The Marketing team wants to interpret the model and see the direct impact ofrelevant features onthe model outcome. While training a logistic regression model, the Data Scientist observesthat there is a widegap between the training and validation set accuracy.Which methods can the Data Scientist use to improve the model performance and satisfythe Marketing team’sneeds? (Choose two.)
A. Add L1 regularization to the classifier
B. Add features to the dataset
C. Perform recursive feature elimination
D. Perform t-distributed stochastic neighbor embedding (t-SNE)
E. Perform linear discriminant analysis
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