A company wants to improve the data load time of a sales data dashboard. Data has been collected as .csv files and stored within an Amazon S3 bucket that is partitioned by date. The data is then loaded to an Amazon Redshift data warehouse for frequent analysis. The data volume is up to 500 GB per day.
Which solution will improve the data loading performance?

• A.Load the .csv files in an unsorted key order and vacuum the table in Amazon Redshift.
• B.Split large .csv files, then use a COPY command to load data into Amazon Redshift.
• C.Compress .csv files and use an INSERT statement to ingest data into Amazon Redshift.
• D.Use Amazon Kinesis Data Firehose to ingest data into Amazon Redshift.

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A large retailer has successfully migrated to an Amazon S3 data lake architecture. The company's marketing team is using Amazon Redshift and Amazon QuickSight to analyze data, and derive and visualize insights. To ensure the marketing team has the most up-to-date actionable information, a data analyst implements nightly refreshes of Amazon Redshift using terabytes of updates from the previous day.
After the first nightly refresh, users report that half of the most popular dashboards that had been running correctly before the refresh are now running much slower. Amazon CloudWatch does not show any alerts.
What is the MOST likely cause for the performance degradation?

• A.The dashboards are suffering from inefficient SQL queries.
• B.The cluster is undersized for the queries being run by the dashboards.
• C.The nightly data refreshes are causing a lingering transaction that cannot be automatically closed by Amazon Redshift due to ongoing user workloads.
• D.The nightly data refreshes left the dashboard tables in need of a vacuum operation that could not be automatically performed by Amazon Redshift due to ongoing user workloads.

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A central government organization is collecting events from various internal applications using Amazon Managed Streaming for Apache Kafka (Amazon MSK). The organization has configured a separate Kafka topic for each application to separate the dat a. For security reasons, the Kafka cluster has been configured to only allow TLS encrypted data and it encrypts the data at rest.
A recent application update showed that one of the applications was configured incorrectly, resulting in writing data to a Kafka topic that belongs to another application. This resulted in multiple errors in the analytics pipeline as data from different applications appeared on the same topic. After this incident, the organization wants to prevent applications from writing to a topic different than the one they should write to.
Which solution meets these requirements with the least amount of effort?

• A.Create a different Amazon EC2 security group for each application. Create an Amazon MSK cluster and Kafka topic for each application. Configure each security group to have access to the specific cluster.
• B.Install Kafka Connect on each application instance and configure each Kafka Connect instance to write to a specific topic only.
• C.Create a different Amazon EC2 security group for each application. Configure each security group to have access to a specific topic in the Amazon MSK cluster. Attach the security group to each application based on the topic that the applications should read and write to.
• D.Use Kafka ACLs and configure read and write permissions for each topic. Use the distinguished name of the clients' TLS certificates as the principal of the ACL.

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A bank is building an Amazon S3 data lake. The bank wants a single data repository for customer data needs, such as personalized recommendations. The bank needs to use Amazon Kinesis Data Firehose to ingest customers' personal information, bank accounts, and transactions in near real time from a transactional relational database.
All personally identifiable information (Pll) that is stored in the S3 bucket must be masked. The bank has enabled versioning for the S3 bucket.
Which solution will meet these requirements?

• A.Configure server-side encryption (SSE) for the S3 bucket. Invoke an AWS Lambda function from S3 events to mask the PII.
• B.Use Amazon Macie to scan the S3 bucket. Configure Macie to discover Pll. Invoke an AWS Lambda function from S3 events to mask the Pll.
• C.Invoke an AWS Lambda function from Kinesis Data Firehose to mask the PII before Kinesis Data Firehose delivers the data to the S3 bucket.
• D.Create an AWS Lambda function to read the objects, mask the Pll, and store the objects back with same key. Invoke the Lambda function from S3 events.

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A company is building a data lake and needs to ingest data from a relational database that has time-series data.
The company wants to use managed services to accomplish this. The process needs to be scheduled daily and bring incremental data only from the source into Amazon S3.
What is the MOST cost-effective approach to meet these requirements?

• A.Use AWS Glue to connect to the data source using JDBC Drivers and ingest the full data. Use AWS DataSync to ensure the delta only is written into Amazon S3.
• B.Use AWS Glue to connect to the data source using JDBC Drivers. Ingest incremental records only using job bookmarks.
• C.Use AWS Glue to connect to the data source using JDBC Drivers and ingest the entire dataset. Use appropriate Apache Spark libraries to compare the dataset, and find the delta.
• D.Use AWS Glue to connect to the data source using JDBC Drivers. Store the last updated key in an Amazon DynamoDB table and ingest the data using the updated key as a filter.

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