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Question 61

A company wants to use an automatic machine learning (ML) Random Cut Forest (RCF) algorithm to visualize complex real-world scenarios, such as detecting seasonality and trends, excluding outers, and imputing missing values.
The team working on this project is non-technical and is looking for an out-of-the-box solution that will require the LEAST amount of management overhead.
Which solution will meet these requirements?

A.Use calculated fields to create a new forecast and then use Amazon QuickSight to visualize the data.
B.Use a pre-build ML AMI from the AWS Marketplace to create forecasts and then use Amazon QuickSight to visualize the data.
C.Use Amazon QuickSight to visualize the data and then use ML-powered forecasting to forecast the key business metrics.
D.Use an AWS Glue ML transform to create a forecast and then use Amazon QuickSight to visualize the data.

Question 62

A streaming application is reading data from Amazon Kinesis Data Streams and immediately writing the data to an Amazon S3 bucket every 10 seconds. The application is reading data from hundreds of shards. The batch interval cannot be changed due to a separate requirement. The data is being accessed by Amazon Athen a. Users are seeing degradation in query performance as time progresses.
Which action can help improve query performance?

A.Merge the files in Amazon S3 to form larger files.
B.Increase the number of shards in Kinesis Data Streams.
C.Add more memory and CPU capacity to the streaming application.
D.Write the files to multiple S3 buckets.

Question 63

An airline has .csv-formatted data stored in Amazon S3 with an AWS Glue Data Catalog. Data analysts want to join this data with call center data stored in Amazon Redshift as part of a dally batch process. The Amazon Redshift cluster is already under a heavy load. The solution must be managed, serverless, well-functioning, and minimize the load on the existing Amazon Redshift cluster. The solution should also require minimal effort and development activity.
Which solution meets these requirements?

A.Unload the call center data from Amazon Redshift to Amazon S3 using an AWS Lambda function.
Perform the join with AWS Glue ETL scripts.
B.Export the call center data from Amazon Redshift using a Python shell in AWS Glue. Perform the join with AWS Glue ETL scripts.
C.Create an external table using Amazon Redshift Spectrum for the call center data and perform the join with Amazon Redshift.
D.Export the call center data from Amazon Redshift to Amazon EMR using Apache Sqoop. Perform the join with Apache Hive.

Question 64

A data engineering team within a shared workspace company wants to build a centralized logging system for all weblogs generated by the space reservation system. The company has a fleet of Amazon EC2 instances that process requests for shared space reservations on its website. The data engineering team wants to ingest all weblogs into a service that will provide a near-real-time search engine. The team does not want to manage the maintenance and operation of the logging system.
Which solution allows the data engineering team to efficiently set up the web logging system within AWS?

A.Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis data stream to CloudWatch. Choose Amazon Elasticsearch Service as the end destination of the weblogs.
B.Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis Data Firehose delivery stream to CloudWatch. Choose Amazon Elasticsearch Service as the end destination of the weblogs.
C.Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis data stream to CloudWatch. Configure Splunk as the end destination of the weblogs.
D.Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis Firehose delivery stream to CloudWatch. Configure Amazon DynamoDB as the end destination of the weblogs.

Question 65

A manufacturing company uses Amazon S3 to store its data. The company wants to use AWS Lake Formation to provide granular-level security on those data assets. The data is in Apache Parquet format. The company has set a deadline for a consultant to build a data lake.
How should the consultant create the MOST cost-effective solution that meets these requirements?

A.Run Lake Formation blueprints to move the data to Lake Formation. Once Lake Formation has the data, apply permissions on Lake Formation.
B.To create the data catalog, run an AWS Glue crawler on the existing Parquet data. Register the Amazon S3 path and then apply permissions through Lake Formation to provide granular-level security.
C.Install Apache Ranger on an Amazon EC2 instance and integrate with Amazon EMR. Using Ranger policies, create role-based access control for the existing data assets in Amazon S3.
D.Create multiple IAM roles for different users and groups. Assign IAM roles to different data assets in Amazon S3 to create table-based and column-based access controls.