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NEW QUESTION 20
A company's marketing team has asked for help in identifying a high performing long-term storage service for their data based on the following requirements:
* The data size is approximately 32 TB uncompressed.
* There is a low volume of single-row inserts each day.
* There is a high volume of aggregation queries each day.
* Multiple complex joins are performed.
* The queries typically involve a small subset of the columns in a table.
Which storage service will provide the MOST performant solution?

A. Amazon NeptuneB. Amazon Aurora MySQLC. Amazon ElasticsearchD. Amazon Redshift

Answer: D

 

NEW QUESTION 21
A company has collected more than 100 TB of log files in the last 24 months. The files are stored as raw text in a dedicated Amazon S3 bucket. Each object has a key of the form year-month-day_log_HHmmss.txt where HHmmss represents the time the log file was initially created. A table was created in Amazon Athena that points to the S3 bucket. One-time queries are run against a subset of columns in the table several times an hour.
A data analyst must make changes to reduce the cost of running these queries. Management wants a solution with minimal maintenance overhead.
Which combination of steps should the data analyst take to meet these requirements? (Choose three.)

A. Add a key prefix of the form date=year-month-day/ to the S3 objects to partition the data.B. Drop and recreate the table with the PARTITIONED BY clause. Run the MSCK REPAIR TABLE statement.C. Convert the log files to Apache Parquet format.D. Drop and recreate the table with the PARTITIONED BY clause. Run the ALTER TABLE ADD PARTITION statement.E. Add a key prefix of the form year-month-day/ to the S3 objects to partition the data.F. Convert the log files to Apace Avro format.

Answer: A,B,C

 

NEW QUESTION 22
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 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.B. Use Amazon EMR to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.C. Load all the data files in parallel to Amazon Aurora, and run an AWS Glue job 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.

Answer: D

Explanation:
Explanation
https://docs.aws.amazon.com/redshift/latest/dg/loading-data-files-using-manifest.html "You can use a manifest to ensure that the COPY command loads all of the required files, and only the required files, for a data load"

 

NEW QUESTION 23
A large financial company is running its ETL process. Part of this process is to move data from Amazon S3 into an Amazon Redshift cluster. The company wants to use the most cost-efficient method to load the dataset into Amazon Redshift.
Which combination of steps would meet these requirements? (Choose two.)

A. Use S3DistCp to load files into Amazon Redshift.B. Use temporary staging tables during the loading process.C. Use the COPY command with the manifest file to load data into Amazon Redshift.D. Use the UNLOAD command to upload data into Amazon Redshift.E. Use Amazon Redshift Spectrum to query files from Amazon S3.

Answer: B,E

 

NEW QUESTION 24
A media company wants to perform machine learning and analytics on the data residing in its Amazon S3 data lake. There are two data transformation requirements that will enable the consumers within the company to create reports:
* Daily transformations of 300 GB of data with different file formats landing in Amazon S3 at a scheduled time.
* One-time transformations of terabytes of archived data residing in the S3 data lake.
Which combination of solutions cost-effectively meets the company's requirements for transforming the data?
(Choose three.)

A. For daily incoming data, use AWS Glue crawlers to scan and identify the schema.B. For archived data, use Amazon EMR to perform data transformations.C. For daily incoming data, use Amazon Athena to scan and identify the schema.D. For daily incoming data, use Amazon Redshift to perform transformations.E. For archived data, use Amazon SageMaker to perform data transformations.F. For daily incoming data, use AWS Glue workflows with AWS Glue jobs to perform transformations.

Answer: C,D,F

 

NEW QUESTION 25
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