Instant Access Amazon.DAS-C01.v2024-08-29.q108 Actual Practice Test Engine for Free (Page 19)

40%off

DAS-C01 Premium Dumps

Latest DAS-C01 Exam Premium Dumps provide by TrainingQuiz.com to help you Passing DAS-C01 Exam! TrainingQuiz.com offers the updated DAS-C01 exam dumps, the TrainingQuiz.com DAS-C01 exam questions has been updated to correct Answer. Get the latest TrainingQuiz.com DAS-C01 pdf dumps with Exam Engine here:

(209 Q&As Dumps, 40%OFF Special Discount: DumpsDB)

Question 86

A company developed a new voting results reporting website that uses Amazon Kinesis Data Firehose to deliver full logs from AWS WAF to an Amazon S3 bucket. The company is now seeking a solution to perform this infrequent data analysis with data visualization capabilities in a way that requires minimal development effort.
Which solution MOST cost-effectively meets these requirements?

A.Use an AWS Glue crawler to create and update a table in the AWS Glue data catalog from the logs. Use Amazon Athena to perform ad-hoc analyses. Develop data visualizations by using Amazon QuickSight.
B.Configure Kinesis Data Firehose to deliver the logs to an Amazon OpenSearch Service cluster. Use OpenSearch Service REST APIs to analyze the data. Visualize the data by building an OpenSearch Service dashboard.
C.Create an AWS Lambda function to convert the logs to CSV format. Add the Lambda function to the Kinesis Data Firehose transformation configuration. Use Amazon Redshift to perform a one-time analysis of the logs by using SQL queries. Develop data visualizations by using Amazon QuickSight.
D.Create an Amazon EMR cluster and use Amazon S3 as the data source. Create an Apache Spark job to perform a one-time analysis of the logs. Develop data visualizations by using Amazon QuickSight.

Question 87

A company has a process that writes two datasets in CSV format to an Amazon S3 bucket every 6 hours. The company needs to join the datasets, convert the data to Apache Parquet, and store the data within another bucket for users to query using Amazon Athen a. The data also needs to be loaded to Amazon Redshift for advanced analytics. The company needs a solution that is resilient to the failure of any individual job component and can be restarted in case of an error.
Which solution meets these requirements with the LEAST amount of operational overhead?

A.Use AWS Step Functions to orchestrate an Amazon EMR cluster running Apache Spark. Use PySpark to generate data frames of the datasets in Amazon S3, transform the data, join the data, write the data back to Amazon S3, and load the data to Amazon Redshift.
B.Create an AWS Glue job using Python Shell that generates dynamic frames of the datasets in Amazon S3, transforms the data, joins the data, writes the data back to Amazon S3, and loads the data to Amazon Redshift. Use an AWS Glue workflow to orchestrate the AWS Glue job at the desired frequency.
C.Use AWS Step Functions to orchestrate the AWS Glue job. Create an AWS Glue job using Python Shell that creates dynamic frames of the datasets in Amazon S3, transforms the data, joins the data, writes the data back to Amazon S3, and loads the data to Amazon Redshift.
D.Create an AWS Glue job using PySpark that creates dynamic frames of the datasets in Amazon S3, transforms the data, joins the data, writes the data back to Amazon S3, and loads the data to Amazon Redshift. Use an AWS Glue workflow to orchestrate the AWS Glue job.

Question 88

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.

Question 89

An airline has .csv-formatted data stored in Amazon S3 with an AWS Glue Data Catalog. Data analysts want to join this data with call center data stored in Amazon Redshift as part of a dally batch process. The Amazon Redshift cluster is already under a heavy load. The solution must be managed, serverless, well-functioning, and minimize the load on the existing Amazon Redshift cluster. The solution should also require minimal effort and development activity.
Which solution meets these requirements?

A.Export the call center data from Amazon Redshift to Amazon EMR using Apache Sqoop. Perform the join with Apache Hive.
B.Create an external table using Amazon Redshift Spectrum for the call center data and perform the join with Amazon Redshift.
C.Export the call center data from Amazon Redshift using a Python shell in AWS Glue. Perform the join with AWS Glue ETL scripts.
D.Unload the call center data from Amazon Redshift to Amazon S3 using an AWS Lambda function.
Perform the join with AWS Glue ETL scripts.

Question 90

A data analyst is using AWS Glue to organize, cleanse, validate, and format a 200 GB dataset. The data analyst triggered the job to run with the Standard worker type. After 3 hours, the AWS Glue job status is still RUNNING. Logs from the job run show no error codes. The data analyst wants to improve the job execution time without overprovisioning.
Which actions should the data analyst take?

A.Enable job metrics in AWS Glue to estimate the number of data processing units (DPUs). Based on the profiled metrics, increase the value of the spark.yarn.executor.memoryOverhead job parameter.
B.Enable job bookmarks in AWS Glue to estimate the number of data processing units (DPUs). Based on the profiled metrics, increase the value of the executor-cores job parameter.
C.Enable job bookmarks in AWS Glue to estimate the number of data processing units (DPUs). Based on the profiled metrics, increase the value of the num-executors job parameter.
D.Enable job metrics in AWS Glue to estimate the number of data processing units (DPUs). Based on the profiled metrics, increase the value of the maximum capacity job parameter.