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Amazon DAS-C01認證考試 & DAS-C01測試題庫 -最新DAS-C01題庫

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下載AWS Certified Data Analytics – Specialty (DAS-C01) Exam考試題庫

NEW QUESTION 45
A company has a marketing department and a finance department. The departments are storing data in Amazon S3 in their own AWS accounts in AWS Organizations. Both departments use AWS Lake Formation to catalog and secure their data. The departments have some databases and tables that share common names.
The marketing department needs to securely access some tables from the finance department.
Which two steps are required for this process? (Choose two.)

  • A. The finance department grants Lake Formation permissions for the tables to the external account for the marketing department.
  • B. The finance department creates cross-account IAM permissions to the table for the marketing department role.
  • C. The marketing department creates an IAM role that has permissions to the Lake Formation tables.

Answer: A,B

Explanation:
Explanation
Granting Lake Formation Permissions
Creating an IAM role (AWS CLI)

 

NEW QUESTION 46
A banking company wants to collect large volumes of transactional data using Amazon Kinesis Data Streams for real-time analytics. The company uses PutRecord to send data to Amazon Kinesis, and has observed network outages during certain times of the day. The company wants to obtain exactly once semantics for the entire processing pipeline.
What should the company do to obtain these characteristics?

  • A. Rely on the exactly one processing semantics of Apache Flink and Apache Spark Streaming included in Amazon EMR.
  • B. Rely on the processing semantics of Amazon Kinesis Data Analytics to avoid duplicate processing of events.
  • C. Design the application so it can remove duplicates during processing be embedding a unique ID in each record.
  • D. Design the data producer so events are not ingested into Kinesis Data Streams multiple times.

Answer: C

 

NEW QUESTION 47
A large retailer has successfully migrated to an Amazon S3 data lake architecture. The company’s marketing team is using Amazon Redshift and Amazon QuickSight to analyze data, and derive and visualize insights. To ensure the marketing team has the most up-to-date actionable information, a data analyst implements nightly refreshes of Amazon Redshift using terabytes of updates from the previous day.
After the first nightly refresh, users report that half of the most popular dashboards that had been running correctly before the refresh are now running much slower. Amazon CloudWatch does not show any alerts.
What is the MOST likely cause for the performance degradation?

  • A. The nightly data refreshes left the dashboard tables in need of a vacuum operation that could not be automatically performed by Amazon Redshift due to ongoing user workloads.
  • B. The dashboards are suffering from inefficient SQL queries.
  • C. The cluster is undersized for the queries being run by the dashboards.
  • D. The nightly data refreshes are causing a lingering transaction that cannot be automatically closed by Amazon Redshift due to ongoing user workloads.

Answer: A

Explanation:
https://github.com/awsdocs/amazon-redshift-developer-guide/issues/21

 

NEW QUESTION 48
An operations team notices that a few AWS Glue jobs for a given ETL application are failing. The AWS Glue jobs read a large number of small JSON files from an Amazon S3 bucket and write the data to a different S3 bucket in Apache Parquet format with no major transformations. Upon initial investigation, a data engineer notices the following error message in the History tab on the AWS Glue console: “Command Failed with Exit Code 1.” Upon further investigation, the data engineer notices that the driver memory profile of the failed jobs crosses the safe threshold of 50% usage quickly and reaches 90-95% soon after. The average memory usage across all executors continues to be less than 4%.
The data engineer also notices the following error while examining the related Amazon CloudWatch Logs.
What should the data engineer do to solve the failure in the MOST cost-effective way?

  • A. Modify the AWS Glue ETL code to use the ‘groupFiles’: ‘inPartition’ feature.
  • B. Change the worker type from Standard to G.2X.
  • C. Modify maximum capacity to increase the total maximum data processing units (DPUs) used.
  • D. Increase the fetch size setting by using AWS Glue dynamics frame.

Answer: A

Explanation:
https://docs.aws.amazon.com/glue/latest/dg/monitor-profile-debug-oom-abnormalities.html#monitor-debug-oom-fix

 

NEW QUESTION 49
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. Create an external table using Amazon Redshift Spectrum for the call center data and perform the join with Amazon Redshift.
  • B. Export the call center data from Amazon Redshift to Amazon EMR using Apache Sqoop. Perform the join with Apache Hive.
  • 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.

Answer: A

Explanation:
Explanation
https://docs.aws.amazon.com/redshift/latest/dg/c-spectrum-external-tables.html

 

NEW QUESTION 50
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