A company wants to improve user satisfaction for its smart home system by adding more features to its recommendation engine. Each sensor asynchronously pushes its nested JSON data into Amazon Kinesis Data Streams using the Kinesis Producer Library (KPL) in Java. Statistics from a set of failed sensors showed that, when a sensor is malfunctioning, its recorded data is not always sent to the cloud.
The company needs a solution that offers near-real-time analytics on the data from the most updated sensors. Which solution enables the company to meet these requirements?

1. A. Set the RecordMaxBufferedTime property of the KPL to "1" to disable the buffering on the sensor side.Use Kinesis Data Analytics to enrich the data based on a company-developed anomaly detection SQL scrip
2. B. Push the enriched data to a fleet of Kinesis data streams and enable the data transformation feature to flatten the JSON fil
3. C. Instantiate a dense storage Amazon Redshift cluster and use it as the destination for the Kinesis Data Firehose delivery stream.
4. D. Update the sensors code to use the PutRecord/PutRecords call from the Kinesis Data Streams API with the AWS SDK for Jav
5. E. Use Kinesis Data Analytics to enrich the data based on a company-developed anomaly detection SQL scrip
6. F. Direct the output of KDA application to a Kinesis Data Firehose delivery stream, enable the data transformation feature to flatten the JSON file, and set the Kinesis Data Firehose destination to an Amazon Elasticsearch Service cluster.
7. G. Set the RecordMaxBufferedTime property of the KPL to "0" to disable the buffering on the sensor side.Connect for each stream a dedicated Kinesis Data Firehose delivery stream and enable the data transformation feature to flatten the JSON file before sending it to an Amazon S3 bucke
8. H. Load the S3 data into an Amazon Redshift cluster.
9. I. Update the sensors code to use the PutRecord/PutRecords call from the Kinesis Data Streams API with the AWS SDK for Jav
10. J. Use AWS Glue to fetch and process data from the stream using the Kinesis Client Library (KCL). Instantiate an Amazon Elasticsearch Service cluster and use AWS Lambda to directly push data into it.

Correct Answer: B
https://docs.aws.amazon.com/streams/latest/dev/developing-producers-with-kpl.html
The KPL can incur an additional processing delay of up to RecordMaxBufferedTime within the library (user-configurable). Larger values of RecordMaxBufferedTime results in higher packing efficiencies and better performance. Applications that cannot tolerate this additional delay may need to use the AWS SDK directly.

A transportation company uses IoT sensors attached to trucks to collect vehicle data for its global delivery fleet. The company currently sends the sensor data in small .csv files to Amazon S3. The files are then loaded into a 10-node Amazon Redshift cluster with two slices per node and queried using both Amazon Athena and Amazon Redshift. The company wants to optimize the files to reduce the cost of querying and also improve the speed of data loading into the Amazon Redshift cluster.
Which solution meets these requirements?

1. A. Use AWS Glue to convert all the files from .csv to a single large Apache Parquet fil
2. B. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.
3. C. Use Amazon EMR to convert each .csv file to Apache Avr
4. D. COPY the files into Amazon Redshift and query the file with Athena from Amazon S3.
5. E. Use AWS Glue to convert the files from .csv to a single large Apache ORC fil
6. F. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.
7. G. Use AWS Glue to convert the files from .csv to Apache Parquet to create 20 Parquet file
8. H. COPY the files into Amazon Redshift and query the files with Athena from Amazon S3.

Correct Answer: D

A company is reading data from various customer databases that run on Amazon RDS. The databases contain many inconsistent fields For example, a customer record field that is place_id in one database is location_id in another database. The company wants to link customer records across different databases, even when many customer record fields do not match exactly
Which solution will meet these requirements with the LEAST operational overhead?

1. A. Create an Amazon EMR cluster to process and analyze data in the databases Connect to the Apache Zeppelin notebook, and use the FindMatches transform to find duplicate records in the data.
2. B. Create an AWS Glue crawler to crawl the database
3. C. Use the FindMatches transform to find duplicate records in the data Evaluate and tune the transform by evaluating performance and results of finding matches
4. D. Create an AWS Glue crawler to crawl the data in the databases Use Amazon SageMaker to construct Apache Spark ML pipelines to find duplicate records in the data
5. E. Create an Amazon EMR cluster to process and analyze data in the database
6. F. Connect to the Apache Zeppelin notebook, and use Apache Spark ML to find duplicate records in the dat
7. G. Evaluate and tune the model by evaluating performance and results of finding duplicates

Correct Answer: B

An Amazon Redshift database contains sensitive user data. Logging is necessary to meet compliance requirements. The logs must contain database authentication attempts, connections, and disconnections. The logs must also contain each query run against the database and record which database user ran each query.
Which steps will create the required logs?

1. A. Enable Amazon Redshift Enhanced VPC Routin
2. B. Enable VPC Flow Logs to monitor traffic.
3. C. Allow access to the Amazon Redshift database using AWS IAM onl
4. D. Log access using AWS CloudTrail.
5. E. Enable audit logging for Amazon Redshift using the AWS Management Console or the AWS CLI.
6. F. Enable and download audit reports from AWS Artifact.

Correct Answer: C

A company using Amazon QuickSight Enterprise edition has thousands of dashboards analyses and datasets. The company struggles to manage and assign permissions for granting users access to various items within QuickSight. The company wants to make it easier to implement sharing and permissions management.
Which solution should the company implement to simplify permissions management?

1. A. Use QuickSight folders to organize dashboards, analyses, and datasets Assign individual users permissions to these folders
2. B. Use QuickSight folders to organize dashboards analyses, and datasets Assign group permissions by using these folders.
3. C. Use AWS 1AM resource-based policies to assign group permissions to QuickSight items
4. D. Use QuickSight user management APIs to provision group permissions based on dashboard naming conventions

Correct Answer: C