A company uses Amazon S3 and AWS Glue Data Catalog to manage a data lake that contains contact information for customers. The company uses PySpark and AWS Glue jobs with a DynamicFrame to run a workflow that processes data within the data lake.
A data engineer notices that the workflow is generating errors as a result of how customer postal codes are stored in the data lake. Some postal codes include unnecessary numbers or invalid characters.
The data engineer needs a solution to address the errors and correct the postal codes in the data lake.

• A. Configure the column.push_down_predicate setting and the catalogPartitionPredicate settings for the postal code column in the DynamicFrame.
• B. Use AWS Glue workflow properties to allow job state sharing. Configure the AWS Glue jobs to read values from the postal code column by using the properties from a previously successful run of the jobs.
• C. Create a schema definition for PySpark that matches the format the processing workflow requires for postal codes. Pass the schema to the DynamicFrame during processing.
• D. Set the DynamicFrame additional_options parameter `useS3ListImplementation' to True.

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A company runs concurrent analytical queries on Amazon Redshift tables multiple times each day. The queries require consistent data views three times each day. The company runs extract, transform, and load (ETL) operations that update dimension tables while the queries run.
The company has noticed that the queries cause table-level locks during the ETL operations. The company's current solution experiences query timeouts and deadlocks during peak processing hours, which affects analytical reporting and on-demand analysis.
Which solution will fix this issue?

• A. Use Amazon Redshift Spectrum to query data in Amazon S3 for analytical workloads. Maintain ETL operations on Amazon Redshift tables with transaction isolation.
• B. Deploy separate Amazon Redshift clusters for ETL and analytics workloads. Use cross-database queries and data sharing to maintain data consistency.
• C. Configure Amazon Redshift federated queries to access source data directly. Use read replicas to isolate analytical workloads from ETL operations.
• D. Use Amazon Redshift materialized views for analytical queries. Schedule ETL operations during off-peak hours to minimize lock contention.

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A data engineer needs to create an Amazon Athena table based on a subset of data from an existing Athena table named cities_world. The cities_world table contains cities that are located around the world. The data engineer must create a new table named cities_us to contain only the cities from cities_world that are located in the US.
Which SQL statement should the data engineer use to meet this requirement?

• A. MOVE city, state FROM cities_world TO cities_usa WHERE country='usa';
• B. UPDATE cities_usa SET (city, state) = (SELECT city, state FROM cities_world WHERE country='usa');
• C. INSERT INTO cities_usa SELECT city, state FROM cities_world WHERE country='usa';
• D. INSERT INTO cities_usa (city,state) SELECT city, state FROM cities_world WHERE country='usa';

A company is setting up a new Amazon SageMaker Unified Studio domain. Each of the company's business units needs isolated control over its own assets, projects, and metadata.
Specific datasets must be shareable with other business units upon approval. The company also requires centralized user authentication and identity mapping. Which solution will meet these requirements?

• A. Configure business units to be represented as separate domains. Establish isolated environments with no shared administrative policies. Configure AWS IAM Identity Center for centralized authentication. Delegate administration at the domain level.
• B. Configure each business unit as a domain unit with delegated ownership and fine-grained permissions policies. Give users the ability to share assets across domain units with explicit access control. Assign API keys to users for authentication to access the domain portal.
• C. Configure each business unit as a separate domain unit to manage permissions on assets, projects, and metadata. Configure AWS IAM Identity Center for centralized authentication. Map user profiles to their respective domain units. Enable cross-business unit sharing through access requests. Instruct domain unit owners to approve or deny the requests.
• D. Configure business units as separate domain units with owner permissions. Restrict projects exclusively to owners to prevent data sharing between domains. Configure AWS IAM Identity Center for centralized authentication. Map user profiles to their respective domain units.

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A company has a data pipeline that processes transaction data in real time. The company needs a notification system that alerts different teams based on the type of processing error without any delay. For security-related errors, the system must immediately notify the security team. For data validation errors, the system must notify the data quality team. For system errors, the system must notify the operations team. Which solution will meet these requirements with the LEAST operational overhead?

• A. Create an Amazon Simple Notification Service (Amazon SNS) topic with an AWS Lambda function subscriber that evaluates the error type and forwards the error to the appropriate email addresses.
• B. Use Amazon Simple Queue Service (Amazon SQS) with message attributes to categorize errors.
  Allow each team to poll their respective SQS queue for relevant errors.
• C. Set up Amazon CloudWatch alarms with different metrics for each error type. Invoke a different Amazon Simple Notification Service (Amazon SNS) notification each time a metrics threshold is crossed.
• D. Configure Amazon EventBridge rules with distinct event patterns for each error type. Route each error type to a dedicated Amazon Simple Notification Service (Amazon SNS) topic for team- specific alerts.

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