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NEW QUESTION 39
A team of data scientists plans to analyze market trend data for their company’s new investment strategy. The trend data comes from five different data sources in large volumes. The team wants to utilize Amazon Kinesis to support their use case. The team uses SQL-like queries to analyze trends and wants to send notifications based on certain significant patterns in the trends. Additionally, the data scientists want to save the data to Amazon S3 for archival and historical re-processing, and use AWS managed services wherever possible. The team wants to implement the lowest-cost solution.
Which solution meets these requirements?

  • A. Publish data to one Kinesis data stream. Deploy Kinesis Data Analytic to the stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SNS.
    Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.
  • B. Publish data to two Kinesis data streams. Deploy a custom application using the Kinesis Client Library (KCL) to the first stream for analyzing trends, and send notifications using Amazon SNS. Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.
  • C. Publish data to one Kinesis data stream. Deploy a custom application using the Kinesis Client Library (KCL) for analyzing trends, and send notifications using Amazon SNS. Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.
  • D. Publish data to two Kinesis data streams. Deploy Kinesis Data Analytics to the first stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SNS.
    Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.

Answer: A

 

NEW QUESTION 40
A financial company hosts a data lake in Amazon S3 and a data warehouse on an Amazon Redshift cluster.
The company uses Amazon QuickSight to build dashboards and wants to secure access from its on-premises Active Directory to Amazon QuickSight.
How should the data be secured?

  • A. Establish a secure connection by creating an S3 endpoint to connect Amazon QuickSight and a VPC endpoint to connect to Amazon Redshift.
  • B. Use an Active Directory connector and single sign-on (SSO) in a corporate network environment.
  • C. Use a VPC endpoint to connect to Amazon S3 from Amazon QuickSight and an IAM role to authenticate Amazon Redshift.
  • D. Place Amazon QuickSight and Amazon Redshift in the security group and use an Amazon S3 endpoint to connect Amazon QuickSight to Amazon S3.

Answer: C

 

NEW QUESTION 41
A company is building a data lake and needs to ingest data from a relational database that has time-series dat a. The company wants to use managed services to accomplish this. The process needs to be scheduled daily and bring incremental data only from the source into Amazon S3.
What is the MOST cost-effective approach to meet these requirements?

  • A. Use AWS Glue to connect to the data source using JDBC Drivers and ingest the full data. Use AWS DataSync to ensure the delta only is written into Amazon S3.
  • B. Use AWS Glue to connect to the data source using JDBC Drivers and ingest the entire dataset. Use appropriate Apache Spark libraries to compare the dataset, and find the delta.
  • C. Use AWS Glue to connect to the data source using JDBC Drivers. Store the last updated key in an Amazon DynamoDB table and ingest the data using the updated key as a filter.
  • D. Use AWS Glue to connect to the data source using JDBC Drivers. Ingest incremental records only using job bookmarks.

Answer: D

Explanation:
https://docs.aws.amazon.com/glue/latest/dg/monitor-continuations.html

 

NEW QUESTION 42
A media company wants to perform machine learning and analytics on the data residing in its Amazon S3 data lake. There are two data transformation requirements that will enable the consumers within the company to create reports:
Daily transformations of 300 GB of data with different file formats landing in Amazon S3 at a scheduled time.
One-time transformations of terabytes of archived data residing in the S3 data lake.
Which combination of solutions cost-effectively meets the company’s requirements for transforming the data? (Choose three.)

  • A. For daily incoming data, use AWS Glue workflows with AWS Glue jobs to perform transformations.
  • B. For daily incoming data, use Amazon Redshift to perform transformations.
  • C. For daily incoming data, use Amazon Athena to scan and identify the schema.
  • D. For archived data, use Amazon EMR to perform data transformations.
  • E. For daily incoming data, use AWS Glue crawlers to scan and identify the schema.
  • F. For archived data, use Amazon SageMaker to perform data transformations.

Answer: A,D,E

 

NEW QUESTION 43
A smart home automation company must efficiently ingest and process messages from various connected devices and sensors. The majority of these messages are comprised of a large number of small files. These messages are ingested using Amazon Kinesis Data Streams and sent to Amazon S3 using a Kinesis data stream consumer application. The Amazon S3 message data is then passed through a processing pipeline built on Amazon EMR running scheduled PySpark jobs.
The data platform team manages data processing and is concerned about the efficiency and cost of downstream data processing. They want to continue to use PySpark.
Which solution improves the efficiency of the data processing jobs and is well architected?

  • A. Launch an Amazon Redshift cluster. Copy the collected data from Amazon S3 to Amazon Redshift and move the data processing jobs from Amazon EMR to Amazon Redshift.
  • B. Send the sensor and devices data directly to a Kinesis Data Firehose delivery stream to send the data to Amazon S3 with Apache Parquet record format conversion enabled. Use Amazon EMR running PySpark to process the data in Amazon S3.
  • C. Set up an AWS Lambda function with a Python runtime environment. Process individual Kinesis data stream messages from the connected devices and sensors using Lambda.
  • D. Set up AWS Glue Python jobs to merge the small data files in Amazon S3 into larger files and transform them to Apache Parquet format. Migrate the downstream PySpark jobs from Amazon EMR to AWS Glue.

Answer: D

Explanation:
https://aws.amazon.com/it/about-aws/whats-new/2020/04/aws-glue-now-supports-serverless-streaming-etl/

 

NEW QUESTION 44
……

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