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The $10 Million Painting

Once an art gallery announced, any painter who will create the best painting depicting peace will be rewarded $10 million.

This news spread in the whole world, and thousands of painters sent their painting to participate in the contest.

From those thousands of paintings, the judges selected hundred best paintings and kept an exhibition in which not only the painters of those paintings but the media and crowd of thousands was also there, because prize amount was so huge.

Everyone was interested to know, who will win such a huge prize?

Finally the day came, all the paintings were very beautiful and each of them was best.

There were some paintings which had the largest crowd:

One of which, was showcasing, a very clean river, with mountain covered with snow, with beautiful sunrise.
Another painting depicting peace, showcasing a full moon night, completely still lake, so still that you can see your reflection in it.
There was one in which white clouds and beautiful green grass.
All the paintings were so beautiful that it became very difficult for the judges, to Short list one of them.

But finally, the moment came when the judges made their minds, and selected one painting and kept it behind a curtain.

Everyone was very excited, and sat down in front of the painting, to have a clear look. Everyone was very curious, as the prize amount was very big.

Finally the curtains were removed. When everyone saw the painting, they were shocked because the painting wasn't depicting peace at all. Everyone thought that art gallery placed it by mistake.

All painters went to the owner of art gallery, and asked whether it is mistake or what?

The Owner smiled and replied, no.Judges selected this painting only.

All the painters got angry and started a revolt.

Seeing this all media members hurried to the owner and placed their mikes in front of him.

Again the owner smiled and said, before asking me anything, just go close to the painting and take a clear look. Maybe then you can notice what you haven't.
All of you saw the storm, the thounder storm and that everything is getting destroyed.

But...

You didn't noticed that there is a house, and a man is inside it seeing all this from a small window, but there is no tension on his face. His face depicts how calm he is, even in this kind of storm.

Moral:

True peace doesn't mean that everything outside is peaceful and so you are, because that's temporary. True peace means you are calm from inside no matter what's going on outside.
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[October-2021]New Braindump2go DAS-C01 PDF and VCE Dumps[Q122-Q132]
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The Amazon Redshift cluster contains a product table and a transactions table, and both tables have a product_sku column. The tables are over 100 GB in size. The majority of queries run on both tables. Which distribution style should the company use for the two tables to achieve optimal query performance? A.An EVEN distribution style for both tables B.A KEY distribution style for both tables C.An ALL distribution style for the product table and an EVEN distribution style for the transactions table D.An EVEN distribution style for the product table and an KEY distribution style for the transactions table Answer: B QUESTION 124 A company receives data from its vendor in JSON format with a timestamp in the file name. The vendor uploads the data to an Amazon S3 bucket, and the data is registered into the company's data lake for analysis and reporting. The company has configured an S3 Lifecycle policy to archive all files to S3 Glacier after 5 days. 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Extract the schema for RDS and Amazon Redshift sources and build the Data Catalog. Use AWS crawlers for data stored in Amazon S3 to infer the schema and automatically update the Data Catalog. Answer: D QUESTION 128 An ecommerce company is migrating its business intelligence environment from on premises to the AWS Cloud. The company will use Amazon Redshift in a public subnet and Amazon QuickSight. The tables already are loaded into Amazon Redshift and can be accessed by a SQL tool. The company starts QuickSight for the first time. During the creation of the data source, a data analytics specialist enters all the information and tries to validate the connection. An error with the following message occurs: "Creating a connection to your data source timed out." How should the data analytics specialist resolve this error? A.Grant the SELECT permission on Amazon Redshift tables. B.Add the QuickSight IP address range into the Amazon Redshift security group. 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Answer: C QUESTION 130 A company needs to collect streaming data from several sources and store the data in the AWS Cloud. The dataset is heavily structured, but analysts need to perform several complex SQL queries and need consistent performance. Some of the data is queried more frequently than the rest. The company wants a solution that meets its performance requirements in a cost-effective manner. Which solution meets these requirements? A.Use Amazon Managed Streaming for Apache Kafka to ingest the data to save it to Amazon S3. Use Amazon Athena to perform SQL queries over the ingested data. B.Use Amazon Managed Streaming for Apache Kafka to ingest the data to save it to Amazon Redshift. Enable Amazon Redshift workload management (WLM) to prioritize workloads. C.Use Amazon Kinesis Data Firehose to ingest the data to save it to Amazon Redshift. Enable Amazon Redshift workload management (WLM) to prioritize workloads. D.Use Amazon Kinesis Data Firehose to ingest the data to save it to Amazon S3. Load frequently queried data to Amazon Redshift using the COPY command. Use Amazon Redshift Spectrum for less frequently queried data. Answer: B QUESTION 131 A manufacturing company uses Amazon Connect to manage its contact center and Salesforce to manage its customer relationship management (CRM) data. The data engineering team must build a pipeline to ingest data from the contact center and CRM system into a data lake that is built on Amazon S3. What is the MOST efficient way to collect data in the data lake with the LEAST operational overhead? A.Use Amazon Kinesis Data Streams to ingest Amazon Connect data and Amazon AppFlow to ingest Salesforce data. B.Use Amazon Kinesis Data Firehose to ingest Amazon Connect data and Amazon Kinesis Data Streams to ingest Salesforce data. C.Use Amazon Kinesis Data Firehose to ingest Amazon Connect data and Amazon AppFlow to ingest Salesforce data. 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[October-2021]New Braindump2go AZ-400 PDF and VCE Dumps[Q214-Q223]
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The subscription contains an Azure virtual machine scale set named VMSS1 that is configured for autoscaling. You have a project in Azure DevOps named Project1. Project1 is used to build a web app named App1 and deploy App1 to VMSS1. You need to ensure that an email alert is generated whenever VMSS1 scales in or out. Solution: From Azure DevOps, configure the Service hooks settings for Project1. Does this meet the goal? A.Yes B.No Answer: B QUESTION 216 Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You have an Azure DevOps organization named Contoso and an Azure subscription. The subscription contains an Azure virtual machine scale set named VMSS1 that is configured for autoscaling. You have a project in Azure DevOps named Project1. Project1 is used to build a web app named App1 and deploy App1 to VMSS1. Solution: From Azure Monitor, configure the autoscale settings. Does this meet the goal? A.Yes B.No Answer: B QUESTION 217 You have an Azure solution that contains a build pipeline in Azure Pipelines. You experience intermittent delays before the build pipeline starts. You need to reduce the time it takes to start the build pipeline. What should you do? A.Split the build pipeline into multiple stages. B.Purchase an additional parallel job. C.Create a new agent pool. D.Enable self-hosted build agents. Answer: C QUESTION 218 You are evaluating the use of code review assignments in GitHub. Which two requirements can be met by using code review assignments' Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point A.Automatically choose and assign reviewers based on a list of available personnel B.Automatically choose and assign reviewers based on who has the most completed review requests. C.Ensure that each team member reviews an equal number of pull requests during any 30-day period. D.Automatically choose and assign reviewers based on who received the least recent review requests. Answer: AC QUESTION 219 You haw an Azure subscription that contains multiple Azure services. You need to send an SMS alert when scheduled maintenance is planned for the Azure services. Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. A.Create an Azure Service Health alert. B.Enable Azure Security Center. C.Create and configure an action group D.Create and configure an Azure Monitor alert rule Answer: AD QUESTION 220 You have a project m Azure DevOps that has a release pipeline. You need to integrate work item tracking and an Agile project management system to meet the following requirements: - Ensure that developers can track whether their commits are deployed to production. - Report the deployment status. - Minimize integration effort. Which system should you use? A.Trello B.Jira C.Basecamp D.Asana Answer: B QUESTION 221 You have several Azure Active Directory (Azure AD) accounts. You need to ensure that users use multi-factor authentication (MFA) to access Azure apps from untrusted networks. What should you configure in Azure AD? A.access reviews B.managed identities C.entitlement management D.conditional access Answer: D QUESTION 222 You configure Azure Application Insights and the shared service plan tier for a web app. You enable Smart Detection. You confirm that standard metrics are visible in the logs, but when you test a failure, you do not receive a Smart Detection notification What prevents the Smart Detection notification from being sent? A.You must restart the web app before Smart Detection is enabled. B.Smart Detection uses the first 24 hours to establish the normal behavior of the web app. C.You must enable the Snapshot Debugger for the web app. D.The web app is configured to use the shared service plan tier. Answer: B QUESTION 223 Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You have an Azure DevOps organization named Contoso and an Azure subscription. The subscription contains an Azure virtual machine scale set named VMSS1 that is configured for autoscaling. You have a project in Azure DevOps named Project1. Project1 is used to build a web app named App1 and deploy App1 to VMSS1. You need to ensure that an email alert is generated whenever VMSS1 scales in or out. Solution: From Azure DevOps, configure the Notifications settings for Project1. Does this meet the goal? A.Yes B.No Answer: B 2021 Latest Braindump2go AZ-400 PDF and AZ-400 VCE Dumps Free Share: https://drive.google.com/drive/folders/1kLhX5N_Pt_noAKZD50xUpnSEA5Tt62TZ?usp=sharing
[October-2021]New Braindump2go MLS-C01 PDF and VCE Dumps[Q158-Q171]
QUESTION 158 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 Answer: A QUESTION 159 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? A.Extract text from receipt images by using Amazon Textract. Use the Amazon SageMaker 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 AWS Marketplace. Use the NER deep learning model to extract entities. C.Extract text from receipt images by using Amazon Textract. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection. D.Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection. Answer: C QUESTION 160 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? A.Create an IAM role that has permissions to create Amazon SageMaker Processing jobs, S3 read and write access to the relevant S3 bucket, and appropriate KMS and ECR permissions. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job from the notebook. B.Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job with an IAM role that has read and write permissions to the relevant S3 bucket, and appropriate KMS and ECR permissions. C.Create an IAM role that has permissions to create Amazon SageMaker Processing jobs and to access Amazon ECR. Attach the role to the SageMaker notebook instance. Set up both an S3 endpoint and a KMS endpoint in the default VPC. Create Amazon SageMaker Processing jobs from the notebook. D.Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. Set up an S3 endpoint in the default VPC. Create Amazon SageMaker Processing jobs with the access key and secret key of the IAM user with appropriate KMS and ECR permissions. Answer: D QUESTION 161 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? A.Call the CreateNotebookInstanceLifecycleConfig API operation B.Create a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance C.Stop and then restart the SageMaker notebook instance D.Call the UpdateNotebookInstanceLifecycleConfig API operation Answer: C QUESTION 162 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? A.Enable server-side encryption on the S3 bucket. Submit an AWS Support ticket to opt out of allowing images to be used for improving the service, and follow the process provided by AWS Support. B.Switch to using an Amazon Rekognition collection to store the images. Use the IndexFaces and SearchFacesByImage API operations instead of the CompareFaces API operation. C.Switch to using the AWS GovCloud (US) Region for Amazon S3 to store images and for Amazon Rekognition to compare faces. Set up a VPN connection and only call the Amazon Rekognition API operations through the VPN. D.Enable client-side encryption on the S3 bucket. Set up a VPN connection and only call the Amazon Rekognition API operations through the VPN. Answer: B QUESTION 163 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? A.Install cameras compatible with Amazon Kinesis Video Streams to stream the data to AWS over the restaurant's existing internet connection. Write an AWS Lambda function to take an image and send it to Amazon Rekognition to count the number of faces in the image. Send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long. B.Deploy AWS DeepLens cameras in the restaurant to capture video. Enable Amazon Rekognition on the AWS DeepLens device, and use it to trigger a local AWS Lambda function when a person is recognized. Use the Lambda function to send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long. C.Build a custom model in Amazon SageMaker to recognize the number of people in an image. Install cameras compatible with Amazon Kinesis Video Streams in the restaurant. Write an AWS Lambda function to take an image. Use the SageMaker endpoint to call the model to count people. Send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long. D.Build a custom model in Amazon SageMaker to recognize the number of people in an image. Deploy AWS DeepLens cameras in the restaurant. Deploy the model to the cameras. Deploy an AWS Lambda function to the cameras to use the model to count people and send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long. Answer: A QUESTION 164 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? A.Decrease the cooldown period for the scale-in activity. Increase the configured maximum 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 inference endpoint. D.Increase the cooldown period for the scale-out activity. Answer: A QUESTION 165 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? A.Update the ProductionVariant data type with the new version of the model by using the CreateEndpointConfig operation with the InitialVariantWeight parameter set to 0. Specify the TargetVariant parameter for InvokeEndpoint calls for users who subscribed to the preview feature. When the new version of the model is ready for release, gradually increase InitialVariantWeight until all users have the updated version. B.Configure two SageMaker hosted endpoints that serve the different versions of the model. Create an Application Load Balancer (ALB) to route traffic to both endpoints based on the TargetVariant query string parameter. Reconfigure the app to send the TargetVariant query string parameter for users who subscribed to the preview feature. When the new version of the model is ready for release, change the ALB's routing algorithm to weighted until all users have the updated version. C.Update the DesiredWeightsAndCapacity data type with the new version of the model by using the UpdateEndpointWeightsAndCapacities operation with the DesiredWeight parameter set to 0. Specify the TargetVariant parameter for InvokeEndpoint calls for users who subscribed to the preview feature. When the new version of the model is ready for release, gradually increase DesiredWeight until all users have the updated version. D.Configure two SageMaker hosted endpoints that serve the different versions of the model. Create an Amazon Route 53 record that is configured with a simple routing policy and that points to the current version of the model. Configure the mobile app to use the endpoint URL for users who subscribed to the preview feature and to use the Route 53 record for other users. When the new version of the model is ready for release, add a new model version endpoint to Route 53, and switch the policy to weighted until all users have the updated version. Answer: D QUESTION 166 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? A.Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the factorization machines (FM) algorithm. B.Use Amazon SageMaker to train a model using the IP Insights algorithm. Schedule updates and retraining of the model using new log data nightly. C.Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the IP Insights algorithm. D.Use Amazon SageMaker to train a model using the Object2Vec algorithm. Schedule updates and retraining of the model using new log data nightly. Answer: C QUESTION 167 A retail company wants to combine its customer orders with the product description data from its product catalog. The structure and format of the records in each dataset is different. A data analyst tried to use a spreadsheet to combine the datasets, but the effort resulted in duplicate records and records that were not properly combined. The company needs a solution that it can use to combine similar records from the two datasets and remove any duplicates. Which solution will meet these requirements? A.Use an AWS Lambda function to process the data. Use two arrays to compare equal strings 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. Call the AWS Glue SearchTables API operation to perform a fuzzy-matching search on the two datasets, and cleanse the data accordingly. C.Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Use the FindMatches transform to cleanse the data. D.Create an AWS Lake Formation custom transform. Run a transformation for matching products from the Lake Formation console to cleanse the data automatically. Answer: D QUESTION 168 A company provisions Amazon SageMaker notebook instances for its data science team and creates Amazon VPC interface endpoints to ensure communication between the VPC and the notebook instances. All connections to the Amazon SageMaker API are contained entirely and securely using the AWS network. However, the data science team realizes that individuals 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 ranges of the VPC. Apply this security group to all of the notebook instances' VPC interfaces. B.Create an IAM policy that allows the sagemaker:CreatePresignedNotebooklnstanceUrl and sagemaker:DescribeNotebooklnstance actions from only the VPC endpoints. Apply this 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 Amazon SageMaker notebook instances are hosted to private subnets. Stop and start all of the notebook instances to reassign only private IP addresses. D.Change the network ACL of the subnet the notebook is hosted in to restrict access to anyone outside the VPC. Answer: B QUESTION 169 A company will use Amazon SageMaker to train and host a machine learning (ML) model for a marketing campaign. The majority of data is sensitive customer data. The data must be encrypted at rest. The company wants AWS to maintain the root of trust for the master keys and wants encryption key usage to be logged. Which implementation will meet these requirements? A.Use encryption keys that are stored in AWS Cloud HSM to encrypt the ML data volumes, and to encrypt the model artifacts and data in Amazon S3. B.Use SageMaker built-in transient keys to encrypt the ML data volumes. Enable default encryption for new Amazon Elastic Block Store (Amazon EBS) volumes. C.Use customer managed keys in AWS Key Management Service (AWS KMS) to encrypt the ML data volumes, and to encrypt the model artifacts and data in Amazon S3. D.Use AWS Security Token Service (AWS STS) to create temporary tokens to encrypt the ML storage volumes, and to encrypt the model artifacts and data in Amazon S3. Answer: C QUESTION 170 A machine learning specialist stores IoT soil sensor data in Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size. The specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker. Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead? A.Launch an Amazon EMR cluster. Create an Apache Hive external table for the DynamoDB table and S3 data. Join the Hive tables and write the results out to Amazon S3. B.Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output to an Amazon Redshift cluster. C.Enable Amazon DynamoDB Streams on the sensor table. Write an AWS Lambda function that consumes the stream and appends the results to the existing weather files in Amazon S3. D.Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3. Answer: C QUESTION 171 A company sells thousands of products on a public website and wants to automatically identify products with potential durability problems. The company has 1.000 reviews with date, star rating, review text, review summary, and customer email fields, but many reviews are incomplete and have empty fields. Each review has already been labeled with the correct durability result. A machine learning specialist must train a model to identify reviews expressing concerns over product durability. The first model needs to be trained and ready to review in 2 days. What is the MOST direct approach to solve this problem within 2 days? A.Train a custom classifier by using Amazon Comprehend. B.Build a recurrent neural network (RNN) in Amazon SageMaker by using Gluon and Apache MXNet. C.Train a built-in BlazingText model using Word2Vec mode in Amazon SageMaker. D.Use a built-in seq2seq model in Amazon SageMaker. 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