A data engineer is using AWS Glue ETL jobs to process data at frequent intervals The processed data is then copied into Amazon S3 The ETL jobs run every 15 minutes. The AWS Glue Data Catalog partitions need to be updated automatically after the completion of each job Which solution will meet these requirements MOST cost-effectively?

• A.Use the AWS Glue Data Catalog to manage the data catalog Define an AWS Glue workflow for the ETL process Define a trigger within the workflow that can start the crawler when an ETL job run is complete
• B.Use an Apache Hive metastore to manage the data catalog Update the AWS Glue ETL code to include the enableUpdateCatalog and partitionKeys arguments.
• C.Use the AWS Glue Data Catalog to manage the data catalog Use AWS Glue Studio to manage ETL jobs. Use the AWS Glue Studio feature that supports updates to the AWS Glue Data Catalog during job runs.
• D.Use the AWS Glue Data Catalog to manage the data catalog Update the AWS Glue ETL code to include the enableUpdateCatalog and partitionKeys arguments.

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A large university has adopted a strategic goal of increasing diversity among enrolled students. The data analytics team is creating a dashboard with data visualizations to enable stakeholders to view historical trends. All access must be authenticated using Microsoft Active Directory. All data in transit and at rest must be encrypted.
Which solution meets these requirements?

• A.Amazon QuickSight Enterprise edition configured to perform identity federation using SAML 2.0 and the default encryption settings.
• B.Amazon QuickSight Standard edition configured to perform identity federation using SAML 2.0. and the default encryption settings.
• C.Amazon QuickSight Enterprise edition using AD Connector to authenticate using Active Directory. Configure Amazon QuickSight to use customer-provided keys imported into AWS KMS.
• D.Amazon QuckSight Standard edition using AD Connector to authenticate using Active Directory. Configure Amazon QuickSight to use customer-provided keys imported into AWS KMS.

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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 large company has a central data lake to run analytics across different departments. Each department uses a separate AWS account and stores its data in an Amazon S3 bucket in that account. Each AWS account uses the AWS Glue Data Catalog as its data catalog. There are different data lake access requirements based on roles. Associate analysts should only have read access to their departmental dat a. Senior data analysts can have access in multiple departments including theirs, but for a subset of columns only.
Which solution achieves these required access patterns to minimize costs and administrative tasks?

• A.Consolidate all AWS accounts into one account. Create different S3 buckets for each department and move all the data from every account to the central data lake account. Migrate the individual data catalogs into a central data catalog and apply fine-grained permissions to give to each user the required access to tables and databases in AWS Glue and Amazon S3.
• B.Keep the account structure and the individual AWS Glue catalogs on each account. Add a central data lake account and use AWS Glue to catalog data from various accounts. Configure cross-account access for AWS Glue crawlers to scan the data in each departmental S3 bucket to identify the schema and populate the catalog. Add the senior data analysts into the central account and apply highly detailed access controls in the Data Catalog and Amazon S3.
• C.Set up an individual AWS account for the central data lake. Use AWS Lake Formation to catalog the cross- account locations. On each individual S3 bucket, modify the bucket policy to grant S3 permissions to the Lake Formation service-linked role. Use Lake Formation permissions to add fine-grained access controls to allow senior analysts to view specific tables and columns.
• D.Set up an individual AWS account for the central data lake and configure a central S3 bucket. Use an AWS Lake Formation blueprint to move the data from the various buckets into the central S3 bucket. On each individual bucket, modify the bucket policy to grant S3 permissions to the Lake Formation service-linked role. Use Lake Formation permissions to add fine-grained access controls for both associate and senior analysts to view specific tables and columns.

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A mortgage company has a microservice for accepting payments. This microservice uses the Amazon DynamoDB encryption client with AWS KMS managed keys to encrypt the sensitive data before writing the data to DynamoDB. The finance team should be able to load this data into Amazon Redshift and aggregate the values within the sensitive fields. The Amazon Redshift cluster is shared with other data analysts from different business units.
Which steps should a data analyst take to accomplish this task efficiently and securely?

• A.Create an Amazon EMR cluster. Create Apache Hive tables that reference the data stored in DynamoDB. Insert the output to the restricted Amazon S3 bucket for the finance team. Use the COPY command with the IAM role that has access to the KMS key to load the data from Amazon S3 to the finance table in Amazon Redshift.
• B.Create an Amazon EMR cluster with an EMR_EC2_DefaultRole role that has access to the KMS key.
  Create Apache Hive tables that reference the data stored in DynamoDB and the finance table in Amazon Redshift. In Hive, select the data from DynamoDB and then insert the output to the finance table in Amazon Redshift.
• C.Create an AWS Lambda function to process the DynamoDB stream. Save the output to a restricted S3 bucket for the finance team. Create a finance table in Amazon Redshift that is accessible to the finance team only. Use the COPY command with the IAM role that has access to the KMS key to load the data from S3 to the finance table.
• D.Create an AWS Lambda function to process the DynamoDB stream. Decrypt the sensitive data using the same KMS key. Save the output to a restricted S3 bucket for the finance team. Create a finance table in Amazon Redshift that is accessible to the finance team only. Use the COPY command to load the data from Amazon S3 to the finance table.

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