DAS-C01 Exam: Absolutely Free Online Practice

Question 1
- A marketing company has an application that stores event data in an Amazon RDS database. The company is replicating this data to Amazon Redshift for reporting and business intelligence (BI) purposes. New event data is continuously generated and ingested into the RDS database throughout the day and captured by a change data capture (CDC) replication task in AWS Database Migration Service (AWS DMS). The company requires that the new data be replicated to Amazon Redshift in near-real time. Which solution meets these requirements?
  
  A :
  Use Amazon Kinesis Data Streams as the destination of the CDC replication task in AWS DMS. Use an AWS Glue streaming job to read changed records from Kinesis Data Streams and perform an upsert into the Redshift cluster.
  
  B :
  Use Amazon S3 as the destination of the CDC replication task in AWS DMS. Use the COPY command to load data into the Redshift cluster.
  
  C :
  Use Amazon DynamoDB as the destination of the CDC replication task in AWS DMS. Use the COPY command to load data into the Redshift cluster.
  
  D :
  Use Amazon Kinesis Data Firehose as the destination of the CDC replication task in AWS DMS. Use an AWS Glue streaming job to read changed records from Kinesis Data Firehose and perform an upsert into the Redshift cluster.
  
  Answer: A
Question 2
- 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.
  
  Answer: D
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?
  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
- A company wants to enrich application logs in near-real-time and use the enriched dataset for further analysis. The application is running on Amazon EC2 instances across multiple Availability Zones and storing its logs using Amazon CloudWatch Logs. The enrichment source is stored in an Amazon DynamoDB table.
  Which solution meets the requirements for the event collection and enrichment?
  A : Use a CloudWatch Logs subscription to send the data to Amazon Kinesis Data Firehose. Use AWS Lambda to transform the data in the Kinesis Data Firehose delivery stream and enrich it with the data in the DynamoDB table. Configure Amazon S3 as the Kinesis Data Firehose delivery destination.
  
  B : Export the raw logs to Amazon S3 on an hourly basis using the AWS CLI. Use AWS Glue crawlers to catalog the logs. Set up an AWS Glue connection for the DynamoDB table and set up an AWS Glue ETL job to enrich the data. Store the enriched data in Amazon S3.
  
  C : Configure the application to write the logs locally and use Amazon Kinesis Agent to send the data to Amazon Kinesis Data Streams. Configure a Kinesis Data Analytics SQL application with the Kinesis data stream as the source. Join the SQL application input stream with DynamoDB records, and then store the enriched output stream in Amazon S3 using Amazon Kinesis Data Firehose.
  
  D : Export the raw logs to Amazon S3 on an hourly basis using the AWS CLI. Use Apache Spark SQL on Amazon EMR to read the logs from Amazon S3 and enrich the records with the data from DynamoDB. Store the enriched data in Amazon S3.
  
  Answer: A
Question 5
- A data analyst is designing an Amazon QuickSight dashboard using centralized sales data that resides in Amazon Redshift. The dashboard must be restricted so that a salesperson in Sydney. Australia, can see only the Australia view and that a salesperson in New York can see only United States (US) data.
  What should the data analyst do to ensure the appropriate data security is in place?
  
  A : Place the data sources for Australia and the US into separate SPICE capacity pools.
  
  B : Set up an Amazon Redshift VPC security group for Australia and the US.
  
  C : Deploy QuickSight Enterprise edition to implement row-level security (RLS) to the sales table.
  
  D : Deploy QuickSight Enterprise edition and set up different VPC security groups for Australia and the US.
  
  Answer: D

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