Question 1

A company leverages Amazon Athena for ad-hoc queries against data stored in Amazon S3. The company wants to implement additional controls to separate query execution and query history among users, teams, or applications running in the same AWS account to comply with internal security policies.
Which solution meets these requirements?

Options :

A : Create an S3 bucket for each given use case, create an S3 bucket policy that grants permissions to appropriate individual IAM users. and apply the S3 bucket policy to the S3 bucket.

B : Create an Athena workgroup for each given use case, apply tags to the workgroup, and create an IAM policy using the tags to apply appropriate permissions to the workgroup.

C : Create an IAM role for each given use case, assign appropriate permissions to the role for the given use case, and add the role to associate the role with Athena.

D : Create an AWS Glue Data Catalog resource policy for each given use case that grants permissions to appropriate individual IAM users, and apply the resource policy to the specific tables used by Athena.

Answer: B

Question 2

An advertising company has a data lake that is built on Amazon S3. The company uses AWS Glue Data Catalog to maintain the metadat
a. The data lake is several years old and its overall size has increased exponentially as additional data sources and metadata are stored in the data lake. The data lake administrator wants to implement a mechanism to simplify permissions management between Amazon S3 and the Data Catalog to keep them in sync
Which solution will simplify permissions management with minimal development effort?

Options :

A : Set AWS Identity and Access Management (1AM) permissions tor AWS Glue

B : Use AWS Lake Formation permissions

C : Manage AWS Glue and S3 permissions by using bucket policies

D : Use Amazon Cognito user pools.

Answer: B

Question 3

A company with a video streaming website wants to analyze user behavior to make recommendations to users in real time Clickstream data is being sent to Amazon Kinesis Data Streams and reference data is stored in Amazon S3 The company wants a solution that can use standard SQL quenes The solution must also provide a way to look up pre-calculated reference data while making recommendations
Which solution meets these requirements?

Options :

A : Use an AWS Glue Python shell job to process incoming data from Kinesis Data Streams Use the Boto3 library to write data to Amazon Redshift

B : Use AWS Glue streaming and Scale to process incoming data from Kinesis Data Streams Use the AWS Glue connector to write data to Amazon Redshift

C : Use Amazon Kinesis Data Analytics to create an in-application table based upon the reference data Process incoming data from Kinesis Data Streams Use a data stream to write results to Amazon Redshift

D : Use Amazon Kinesis Data Analytics to create an in-application table based upon the reference data Process incoming data from Kinesis Data Streams Use an Amazon Kinesis Data Firehose delivery stream to write results to Amazon Redshift

Answer: D

Question 4

An online retail company with millions of users around the globe wants to improve its ecommerce analytics capabilities. Currently, clickstream data is uploaded directly to Amazon S3 as compressed files. Several times each day, an application running on Amazon EC2 processes the data and makes search options and reports available for visualization by editors and marketers. The company wants to make website clicks and aggregated data available to editors and marketers in minutes to enable them to connect with users more effectively.
Which options will help meet these requirements in the MOST efficient way? (Choose two.)

Options :

A : Use Amazon Kinesis Data Firehose to upload compressed and batched clickstream records to Amazon Elasticsearch Service.

B : Upload clickstream records to Amazon S3 as compressed files. Then use AWS Lambda to send data to Amazon Elasticsearch Service from Amazon S3.

C : Use Amazon Elasticsearch Service deployed on Amazon EC2 to aggregate, filter, and process the data. Refresh content performance dashboards in near-real time.

D : Use Kibana to aggregate, filter, and visualize the data stored in Amazon Elasticsearch Service. Refresh content performance dashboards in near-real time.

E : Upload clickstream records from Amazon S3 to Amazon Kinesis Data Streams and use a Kinesis Data Streams consumer to send records to Amazon Elasticsearch Service.

Answer: A,D

Question 5

A manufacturing company is storing data from its operational systems in Amazon S3. The company's business analysts need to perform one-time queries of the data in Amazon S3 with Amazon Athena. The company needs to access the Athena service from the on-premises network by using a JDBC connection. The company has created a VPC. Security policies mandate that requests to AWS services cannot traverse the internet. Which combination of steps should a data analytics specialist take to meet these requirements? (Select TWO.)

Options :

A : Establish an AWS Direct Connect connection between the on-premises network and the VPC.

B : Configure the JDBC connection to connect to Athena through Amazon API Gateway.

C : Configure the JDBC connection to use a gateway VPC endpoint for Amazon S3.

D : Configure the JDBC connection to use an interface VPC endpoint for Athena.

E : Deploy Athena within a private subnet.

Answer: A,D

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