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NEW QUESTION 45
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 Amazon Redshift Spectrum to query files from Amazon S3.
- B. Use temporary staging tables during the loading process.
- C. Use S3DistCp to load files into Amazon Redshift.
- D. Use the UNLOAD command to upload data into Amazon Redshift.
- E. Use the COPY command with the manifest file to load data into Amazon Redshift.
NEW QUESTION 46
A manufacturing company has been collecting IoT sensor data from devices on its factory floor for a year and is storing the data in Amazon Redshift for daily analysis. A data analyst has determined that, at an expected ingestion rate of about 2 TB per day, the cluster will be undersized in less than 4 months. A long-term solution is needed. The data analyst has indicated that most queries only reference the most recent 13 months of data, yet there are also quarterly reports that need to query all the data generated from the past 7 years. The chief technology officer (CTO) is concerned about the costs, administrative effort, and performance of a long-term solution.
Which solution should the data analyst use to meet these requirements?
- A. Unload all the tables in Amazon Redshift to an Amazon S3 bucket using S3 Intelligent-Tiering. Use AWS Glue to crawl the S3 bucket location to create external tables in an AWS Glue Data Catalog.
Create an Amazon EMR cluster using Auto Scaling for any daily analytics needs, and use Amazon Athena for the quarterly reports, with both using the same AWS Glue Data Catalog.
- B. Create a daily job in AWS Glue to UNLOAD records older than 13 months to Amazon S3 and delete those records from Amazon Redshift. Create an external table in Amazon Redshift to point to the S3 location. Use Amazon Redshift Spectrum to join to data that is older than 13 months.
- C. Execute a CREATE TABLE AS SELECT (CTAS) statement to move records that are older than 13 months to quarterly partitioned data in Amazon Redshift Spectrum backed by Amazon S3.
- D. Take a snapshot of the Amazon Redshift cluster. Restore the cluster to a new cluster using dense storage nodes with additional storage capacity.
NEW QUESTION 47
A company has developed several AWS Glue jobs to validate and transform its data from Amazon S3 and load it into Amazon RDS for MySQL in batches once every day. The ETL jobs read the S3 data using a DynamicFrame. Currently, the ETL developers are experiencing challenges in processing only the incremental data on every run, as the AWS Glue job processes all the S3 input data on each run.
Which approach would allow the developers to solve the issue with minimal coding effort?
- A. Enable job bookmarks on the AWS Glue jobs.
- B. Create custom logic on the ETL jobs to track the processed S3 objects.
- C. Have the ETL jobs delete the processed objects or data from Amazon S3 after each run.
- D. Have the ETL jobs read the data from Amazon S3 using a DataFrame.
NEW QUESTION 48
Once a month, a company receives a 100 MB .csv file compressed with gzip. The file 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 specific vendor.
What is the most cost-effective solution?
- A. Load the data into Amazon S3 and query it with Amazon S3 Select.
- B. Query the data from Amazon S3 Glacier directly with Amazon Glacier Select.
- C. Load the data to Amazon S3 and query it with Amazon Athena.
- D. Load the data to Amazon S3 and query it with Amazon Redshift Spectrum.
NEW QUESTION 49
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