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 Amazon EMR to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.
• B.Load all the data files in parallel to Amazon Aurora, and run an AWS Glue job to load the data into Amazon Redshift.
• C.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.
• D.Create a manifest file that contains the data file locations and issue a COPY command to load the data into Amazon Redshift.

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A company has 1 million scanned documents stored as image files in Amazon S3. The documents contain typewritten application forms with information including the applicant first name, applicant last name, application date, application type, and application text. The company has developed a machine learning algorithm to extract the metadata values from the scanned documents. The company wants to allow internal data analysts to analyze and find applications using the applicant name, application date, or application text.
The original images should also be downloadable. Cost control is secondary to query performance.
Which solution organizes the images and metadata to drive insights while meeting the requirements?

• A.Store the metadata and the Amazon S3 location of the image file in an Amazon Redshift table. Allow the data analysts to run ad-hoc queries on the table.
• B.For each image, use object tags to add the metadata. Use Amazon S3 Select to retrieve the files based on the applicant name and application date.
• C.Index the metadata and the Amazon S3 location of the image file in Amazon Elasticsearch Service.
  Allow the data analysts to use Kibana to submit queries to the Elasticsearch cluster.
• D.Store the metadata and the Amazon S3 location of the image files in an Apache Parquet file in Amazon S3, and define a table in the AWS Glue Data Catalog. Allow data analysts to use Amazon Athena to submit custom queries.

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A hospital is building a research data lake to ingest data from electronic health records (EHR) systems from multiple hospitals and clinics. The EHR systems are independent of each other and do not have a common patient identifier. The data engineering team is not experienced in machine learning (ML) and has been asked to generate a unique patient identifier for the ingested records.
Which solution will accomplish this task?

• A.An AWS Glue ETL job with the FindMatches transform
• B.Amazon Kendra
• C.Amazon SageMaker Ground Truth
• D.An AWS Glue ETL job with the ResolveChoice transform

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A data analytics specialist is building an automated ETL ingestion pipeline using AWS Glue to ingest compressed files that have been uploaded to an Amazon S3 bucket. The ingestion pipeline should support incremental data processing.
Which AWS Glue feature should the data analytics specialist use to meet this requirement?

• A.Job bookmarks
• B.Triggers
• C.Workflows
• D.Classifiers

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