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?

• A.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.
• B.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.
• C.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.
• 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.

Comment: *

Name: *

Email: *

Verification: *

A financial company uses Amazon S3 as its data lake and has set up a data warehouse using a multi-node Amazon Redshift cluster. The data files in the data lake are organized in folders based on the data source of each data file. All the data files are loaded to one table in the Amazon Redshift cluster using a separate COPY command for each data file location. With this approach, loading all the data files into Amazon Redshift takes a long time to complete. Users want a faster solution with little or no increase in cost while maintaining the segregation of the data files in the S3 data lake.
Which solution meets these requirements?

• A.Use Amazon EMR to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.
• B.Load all the data files in parallel to Amazon Aurora, and run an AWS Glue job to load the data into Amazon Redshift.
• C.Use an AWS Glue job to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.
• D.Create a manifest file that contains the data file locations and issue a COPY command to load the data into Amazon Redshift.

Comment: *

Name: *

Email: *

Verification: *

A mobile gaming company wants to capture data from its gaming app and make the data available for analysis immediately. The data record size will be approximately 20 KB. The company is concerned about achieving optimal throughput from each device. Additionally, the company wants to develop a data stream processing application with dedicated throughput for each consumer.
Which solution would achieve this goal?

• A.Have the app call the PutRecordBatch API to send data to Amazon Kinesis Data Firehose. Submit a support case to enable dedicated throughput on the account.
• B.Have the app call the PutRecords API to send data to Amazon Kinesis Data Streams. Use the enhanced fan-out feature while consuming the data.
• C.Have the app use Amazon Kinesis Producer Library (KPL) to send data to Kinesis Data Firehose. Use the enhanced fan-out feature while consuming the data.
• D.Have the app call the PutRecords API to send data to Amazon Kinesis Data Streams. Host the stream- processing application on Amazon EC2 with Auto Scaling.

Comment: *

Name: *

Email: *

Verification: *

A financial company uses Apache Hive on Amazon EMR for ad-hoc queries. Users are complaining of sluggish performance.
A data analyst notes the following:
* Approximately 90% of queries are submitted 1 hour after the market opens.
* Hadoop Distributed File System (HDFS) utilization never exceeds 10%.
Which solution would help address the performance issues?

• A.Create instance fleet configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metric. Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch CapacityRemainingGB metric.
• B.Create instance fleet configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metric.
  Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch YARNMemoryAvailablePercentage metric.
• C.Create instance group configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metric. Create an automatic scaling policy to scale in the instance groups based on the CloudWatch CapacityRemainingGB metric.
• D.Create instance group configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metric.
  Create an automatic scaling policy to scale in the instance groups based on the CloudWatch YARNMemoryAvailablePercentage metric.

Comment: *

Name: *

Email: *

Verification: *

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.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.
• B.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.
• C.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.
• 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.

Comment: *

Name: *

Email: *

Verification: *