A company has an application that ingests streaming data. The company needs to analyze this stream over a 5-minute timeframe to evaluate the stream for anomalies with Random Cut Forest (RCF) and summarize the current count of status codes. The source and summarized data should be persisted for future use.
Which approach would enable the desired outcome while keeping data persistence costs low?

1. A. Ingest the data stream with Amazon Kinesis Data Stream
2. B. Have an AWS Lambda consumer evaluate the stream, collect the number status codes, and evaluate the data against a previously trained RCF mode
3. C. Persist the source and results as a time series to Amazon DynamoDB.
4. D. Ingest the data stream with Amazon Kinesis Data Stream
5. E. Have a Kinesis Data Analytics application evaluate the stream over a 5-minute window using the RCF function and summarize the count of status code
6. F. Persist the source and results to Amazon S3 through output delivery to Kinesis Data Firehouse.
7. G. Ingest the data stream with Amazon Kinesis Data Firehose with a delivery frequency of 1 minute or 1 MB in Amazon S3. Ensure Amazon S3 triggers an event to invoke an AWS Lambda consumer that evaluates the batch data, collects the number status codes, and evaluates the data against a previously trained RCF mode
8. H. Persist the source and results as a time series to Amazon DynamoDB.
9. I. Ingest the data stream with Amazon Kinesis Data Firehose with a delivery frequency of 5 minutes or 1 MB into Amazon S3. Have a Kinesis Data Analytics application evaluate the stream over a 1-minute window using the RCF function and summarize the count of status code
10. J. Persist the results to Amazon S3 through a Kinesis Data Analytics output to an AWS Lambda integration.

Correct Answer: B

An advertising company has a data lake that is built on Amazon S3. The company uses AWS Glue Data Catalog to maintain the metadata. The data lake is several years old and its overall size has increased exponentially as additional data sources and metadata are stored in the data lake. The data lake administrator wants to implement a mechanism to simplify permissions management between Amazon S3 and the Data Catalog to keep them in sync
Which solution will simplify permissions management with minimal development effort?

1. A. Set AWS Identity and Access Management (1AM) permissions tor AWS Glue
2. B. Use AWS Lake Formation permissions
3. C. Manage AWS Glue and S3 permissions by using bucket policies
4. D. Use Amazon Cognito user pools.

Correct Answer: B

An ecommerce company ingests a large set of clickstream data in JSON format and stores the data in Amazon S3. Business analysts from multiple product divisions need to use Amazon Athena to analyze the data. The company's analytics team must design a solution to monitor the daily data usage for Athena by each product division. The solution also must produce a warning when a divisions exceeds its quota
Which solution will meet these requirements with the LEAST operational overhead?

1. A. Use a CREATE TABLE AS SELECT (CTAS) statement to create separate tables for each product division Use AWS Budgets to track Athena usage Configure a threshold for the budget Use Amazon Simple Notification Service (Amazon SNS) to send notifications when thresholds are breached.
2. B. Create an AWS account for each division Provide cross-account access to an AWS Glue Data Catalog to all the account
3. C. Set an Amazon CloudWatch alarm to monitor Athena usag
4. D. Use Amazon Simple Notification Service (Amazon SNS) to send notifications.
5. E. Create an Athena workgroup for each division Configure a data usage control for each workgroup and a time period of 1 day Configure an action to send notifications to an Amazon Simple Notification Service(Amazon SNS) topic
6. F. Create an AWS account for each division Configure an AWS Glue Data Catalog in each account Set an Amazon CloudWatch alarm to monitor Athena usage Use Amazon Simple Notification Service (Amazon SNS) to send notifications.

Correct Answer: C

A large energy company is using Amazon QuickSight to build dashboards and report the historical usage data of its customers This data is hosted in Amazon Redshift The reports need access to all the fact tables' billions ot records to create aggregation in real time grouping by multiple dimensions
A data analyst created the dataset in QuickSight by using a SQL query and not SPICE Business users have noted that the response time is not fast enough to meet their needs
Which action would speed up the response time for the reports with the LEAST implementation effort?

1. A. Use QuickSight to modify the current dataset to use SPICE
2. B. Use AWS Glue to create an Apache Spark job that joins the fact table with the dimension
3. C. Load the data into a new table
4. D. Use Amazon Redshift to create a materialized view that joins the fact table with the dimensions
5. E. Use Amazon Redshift to create a stored procedure that joins the fact table with the dimensions Load the data into a new table

Correct Answer: A

A media company wants to perform machine learning and analytics on the data residing in its Amazon S3 data lake. There are two data transformation requirements that will enable the consumers within the company to create reports:
Daily transformations of 300 GB of data with different file formats landing in Amazon S3 at a scheduled time.
One-time transformations of terabytes of archived data residing in the S3 data lake.
Which combination of solutions cost-effectively meets the company’s requirements for transforming the data? (Choose three.)

1. A. For daily incoming data, use AWS Glue crawlers to scan and identify the schema.
2. B. For daily incoming data, use Amazon Athena to scan and identify the schema.
3. C. For daily incoming data, use Amazon Redshift to perform transformations.
4. D. For daily incoming data, use AWS Glue workflows with AWS Glue jobs to perform transformations.
5. E. For archived data, use Amazon EMR to perform data transformations.
6. F. For archived data, use Amazon SageMaker to perform data transformations.

Correct Answer: ADE