DAS-C01 Exam: Absolutely Free Online Practice

Question 1
- 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 : 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 : port 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 : nfigure 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 : port 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 2
- A company ingests a large set of sensor data in nested JSON format from different sources and stores it in an Amazon S3 bucket. The sensor data must be joined with performance data currently stored in an Amazon Redshift cluster. A business analyst with basic SQL skills must build dashboards and analyze this data in Amazon QuickSight. A data engineer needs to build a solution to prepare the data for use by the business analyst. The data engineer does not know the structure of the JSON file. The company requires a solution with the least possible implementation effort. Which combination of steps will create a solution that meets these requirements? (Select THREE.)
  
  A : Use an AWS Glue ETL job to convert the data into Apache Parquet format and write to Amazon S3.
  
  B : Use an AWS Glue crawler to catalog the data.
  
  C :
  Use an AWS Glue ETL job with the ApplyMapping class to un-nest the data and write to Amazon Redshift tables.
  
  D :
  Use an AWS Glue ETL job with the Regionalize class to un-nest the data and write to Amazon Redshift tables.
  
  E :
  Use QuickSight to create an Amazon Athena data source to read the Apache Parquet files in Amazon S3.
  
  F : Use QuickSight to create an Amazon Redshift data source to read the native Amazon Redshift tables.
  
  Answer: B,D,F
Question 3
- A company has a business unit uploading .csv files to an Amazon S3 bucket. The company's data platform team has set up an AWS Glue crawler to do discovery, and create tables and schemas. An AWS Glue job writes processed data from the created tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creating the Amazon Redshift table appropriately. When the AWS Glue job is rerun for any reason in a day, duplicate records are introduced into the Amazon Redshift table.
  Which solution will update the Redshift table without duplicates when jobs are rerun?
  A : dify the AWS Glue job to copy the rows into a staging table. Add SQL commands to replace the existing rows in the main table as postactions in the DynamicFrameWriter class.
  
  B : ad the previously inserted data into a MySQL database in the AWS Glue job. Perform an upsert operation in MySQL, and copy the results to the Amazon Redshift table.
  
  C : Apache Spark's DataFrame dropDuplicates() API to eliminate duplicates and then write the data to Amazon Redshift.
  
  D : the AWS Glue ResolveChoice built-in transform to select the most recent value of the column.
  
  Answer: A
Question 4
- 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 : eate 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 : eate 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 : eate 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 : eate 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 5
- An IOT company is collecting data from multiple sensors and is streaming the data to Amazon Managed Streaming for Apache Kafka (Amazon MSK). Each sensor type has its own topic, and each topic has the same number of partitions. The company is planning to turn on more sensors. However, the company wants to evaluate which sensor types are producing the most data sothat the company can scale accordingly. The company needs to know which sensor types have the largest values for the following metrics: ByteslnPerSec and MessageslnPerSec. Which level of monitoring for Amazon MSK will meet these requirements?
  
  A : DEFAULT level
  
  B : PER TOPIC PER BROKER level
  
  C : PER BROKER level
  
  D : PER TOPIC level
  
  Answer: B

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