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Amazon AWS Certified Data Analytics – Specialty (DAS-C01) Exam Sample Questions (Q51-Q56):
NEW QUESTION # 51
A data analytics specialist is setting up workload management in manual mode for an Amazon Redshift environment. The data analytics specialist is defining query monitoring rules to manage system performance and user experience of an Amazon Redshift cluster.
Which elements must each query monitoring rule include?
- A. A unique rule name, a query runtime condition, and an AWS Lambda function to resubmit any failed queries in off hours
- B. A unique rule name, one to three predicates, and an action
- C. A workload name, a unique rule name, and a query runtime-based condition
- D. A queue name, a unique rule name, and a predicate-based stop condition
Answer: B
NEW QUESTION # 52
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. Unload the call center data from Amazon Redshift to Amazon S3 using an AWS Lambda function.
Perform the join with AWS Glue ETL scripts. - 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 to Amazon EMR using Apache Sqoop. Perform the join with Apache Hive.
- D. Export the call center data from Amazon Redshift using a Python shell in AWS Glue. Perform the join with AWS Glue ETL scripts.
Answer: B
Explanation:
Explanation
https://docs.aws.amazon.com/redshift/latest/dg/c-spectrum-external-tables.html
NEW QUESTION # 53
A company developed a new elections 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 low-cost option to perform this infrequent data analysis with visualizations of logs in a way that requires minimal development effort.
Which solution meets these requirements?
- A. Create an AWS Lambda function to convert the logs into .csv format. Then add the function to the Kinesis Data Firehose transformation configuration. Use Amazon Redshift to perform ad-hoc analyses of the logs using SQL queries and use Amazon QuickSight to develop data visualizations.
- B. Use an AWS Glue crawler to create and update a table in the Glue data catalog from the logs. Use Athena to perform ad-hoc analyses and use Amazon QuickSight to develop data visualizations.
- C. Create an Amazon EMR cluster and use Amazon S3 as the data source. Create an Apache Spark job to perform ad-hoc analyses and use Amazon QuickSight to develop data visualizations.
- D. Create a second Kinesis Data Firehose delivery stream to deliver the log files to Amazon Elasticsearch Service (Amazon ES). Use Amazon ES to perform text-based searches of the logs for ad-hoc analyses and use Kibana for data visualizations.
Answer: B
Explanation:
https://aws.amazon.com/blogs/big-data/analyzing-aws-waf-logs-with-amazon-es-amazon-athena-and-amazon-quicksight/
NEW QUESTION # 54
A company owns facilities with IoT devices installed across the world. The company is using Amazon Kinesis Data Streams to stream data from the devices to Amazon S3. The company’s operations team wants to get insights from the IoT data to monitor data quality at ingestion. The insights need to be derived in near-real time, and the output must be logged to Amazon DynamoDB for further analysis.
Which solution meets these requirements?
- A. Connect Amazon Kinesis Data Analytics to analyze the stream data. Save the output to DynamoDB by using the default output from Kinesis Data Analytics.
- B. Connect Amazon Kinesis Data Firehose to analyze the stream data by using an AWS Lambda function. Save the output to DynamoDB by using the default output from Kinesis Data Firehose.
- C. Connect Amazon Kinesis Data Analytics to analyze the stream data. Save the output to DynamoDB by using an AWS Lambda function.
- D. Connect Amazon Kinesis Data Firehose to analyze the stream data by using an AWS Lambda function. Save the data to Amazon S3. Then run an AWS Glue job on schedule to ingest the data into DynamoDB.
Answer: B
NEW QUESTION # 55
A data analyst is using Amazon QuickSight for data visualization across multiple datasets generated by applications. Each application stores files within a separate Amazon S3 bucket. AWS Glue Data Catalog is used as a central catalog across all application data in Amazon S3. A new application stores its data within a separate S3 bucket. After updating the catalog to include the new application data source, the data analyst created a new Amazon QuickSight data source from an Amazon Athena table, but the import into SPICE failed.
How should the data analyst resolve the issue?
- A. Edit the permissions for the AWS Glue Data Catalog from within the Amazon QuickSight console.
- B. Edit the permissions for the AWS Glue Data Catalog from within the AWS Glue console.
- C. Edit the permissions for the new S3 bucket from within the Amazon QuickSight console.
- D. Edit the permissions for the new S3 bucket from within the S3 console.
Answer: C
NEW QUESTION # 56
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