QUESTION: 45

Once a month, a company receives a 100 MB .csv le compressed with gzip. The le contains 50,000 property listing records and is stored in Amazon S3 Glacier.
The company needs its data analyst to query a subset of the data for a speci c vendor.
What is the most cost-effective solution?

Load the data into Amazon S3 and query it with Amazon S3 Select.
Query the data from Amazon S3 Glacier directly with Amazon Glacier Select.
Load the data to Amazon S3 and query it with Amazon Athena.
Load the data to Amazon S3 and query it with Amazon Redshift Spectrum.

Answer(s): A

Reference:

https://aws.amazon.com/athena/faqs/

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QUESTION: 46

A retail company is building its data warehouse solution using Amazon Redshift. As a part of that effort, the company is loading hundreds of les into the fact table created in its Amazon Redshift cluster. The company wants the solution to achieve the highest throughput and optimally use cluster resources when loading data into the company's fact table.
How should the company meet these requirements?

Use multiple COPY commands to load the data into the Amazon Redshift cluster.
Use S3DistCp to load multiple les into the Hadoop Distributed File System (HDFS) and use an HDFS connector to ingest the data into the Amazon Redshift cluster.
Use LOAD commands equal to the number of Amazon Redshift cluster nodes and load the data in parallel into each node.
Use a single COPY command to load the data into the Amazon Redshift cluster.

Answer(s): D

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QUESTION: 47

A data analyst is designing a solution to interactively query datasets with SQL using a JDBC connection. Users will join data stored in Amazon S3 in Apache ORC format with data stored in Amazon OpenSearch Service (Amazon Elasticsearch Service) and Amazon Aurora MySQL.
Which solution will provide the MOST up-to-date results?

Use AWS Glue jobs to ETL data from Amazon ES and Aurora MySQL to Amazon S3. Query the data with Amazon Athena.
Use Amazon DMS to stream data from Amazon ES and Aurora MySQL to Amazon Redshift. Query the data with Amazon Redshift.
Query all the datasets in place with Apache Spark SQL running on an AWS Glue developer endpoint.
Query all the datasets in place with Apache Presto running on Amazon EMR.

Answer(s): D

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QUESTION: 48

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?

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.
Create a second Kinesis Data Firehose delivery stream to deliver the log les to Amazon OpenSearch Service (Amazon Elasticsearch Service). Use Amazon ES to perform text-based searches of the logs for ad-hoc analyses and use OpenSearch Dashboards (Kibana) for data visualizations.
Create an AWS Lambda function to convert the logs into .csv format. Then add the function to the Kinesis Data Firehose transformation con guration. Use Amazon Redshift to perform ad-hoc analyses of the logs using SQL queries and use Amazon QuickSight to develop data visualizations.
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.

Answer(s): A

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