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

A company recently created a test AWS account to use for a development environment The company also created a production AWS account in another AWS Region As part of its security testing the company wants to send log data from Amazon CloudWatch Logs in its production account to an Amazon Kinesis data stream in its test account
Which solution will allow the company to accomplish this goal?

1. A. Create a subscription filter in the production accounts CloudWatch Logs to target the Kinesis data stream in the test account as its destination In the test account create an 1AM role that grants access to the Kinesis data stream and the CloudWatch Logs resources in the production account
2. B. In the test account create an 1AM role that grants access to the Kinesis data stream and the CloudWatch Logs resources in the production account Create a destination data stream in Kinesis Data Streams in the test account with an 1AM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account
3. C. In the test account, create an 1AM role that grants access to the Kinesis data stream and the CloudWatch Logs resources in the production account Create a destination data stream in Kinesis Data Streams in the test account with an 1AM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account
4. D. Create a destination data stream in Kinesis Data Streams in the test account with an 1AM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account Create a subscription filter in the production accounts CloudWatch Logs to target the Kinesis data stream in the test account as its destination

Correct Answer: D

A large company receives files from external parties in Amazon EC2 throughout the day. At the end of the day, the files are combined into a single file, compressed into a gzip file, and uploaded to Amazon S3. The total size of all the files is close to 100 GB daily. Once the files are uploaded to Amazon S3, an AWS Batch program executes a COPY command to load the files into an Amazon Redshift cluster.
Which program modification will accelerate the COPY process?

1. A. Upload the individual files to Amazon S3 and run the COPY command as soon as the files become available.
2. B. Split the number of files so they are equal to a multiple of the number of slices in the Amazon Redshift cluste
3. C. Gzip and upload the files to Amazon S3. Run the COPY command on the files.
4. D. Split the number of files so they are equal to a multiple of the number of compute nodes in the Amazon Redshift cluste
5. E. Gzip and upload the files to Amazon S3. Run the COPY command on the files.
6. F. Apply sharding by breaking up the files so the distkey columns with the same values go to the same file.Gzip and upload the sharded files to Amazon S3. Run the COPY command on the files.

Correct Answer: B

A company that monitors weather conditions from remote construction sites is setting up a solution to collect temperature data from the following two weather stations.
Station A, which has 10 sensors
Station B, which has five sensors
These weather stations were placed by onsite subject-matter experts.
Each sensor has a unique ID. The data collected from each sensor will be collected using Amazon Kinesis Data Streams.
Based on the total incoming and outgoing data throughput, a single Amazon Kinesis data stream with two shards is created. Two partition keys are created based on the station names. During testing, there is a bottleneck on data coming from Station A, but not from Station B. Upon review, it is confirmed that the total stream throughput is still less than the allocated Kinesis Data Streams throughput.
How can this bottleneck be resolved without increasing the overall cost and complexity of the solution, while retaining the data collection quality requirements?

1. A. Increase the number of shards in Kinesis Data Streams to increase the level of parallelism.
2. B. Create a separate Kinesis data stream for Station A with two shards, and stream Station A sensor data to the new stream.
3. C. Modify the partition key to use the sensor ID instead of the station name.
4. D. Reduce the number of sensors in Station A from 10 to 5 sensors.

Correct Answer: C
https://docs.aws.amazon.com/streams/latest/dev/kinesis-using-sdk-java-resharding.html
"Splitting increases the number of shards in your stream and therefore increases the data capacity of the stream. Because you are charged on a per-shard basis, splitting increases the cost of your stream"

A smart home automation company must efficiently ingest and process messages from various connected devices and sensors. The majority of these messages are comprised of a large number of small files. These messages are ingested using Amazon Kinesis Data Streams and sent to Amazon S3 using a Kinesis data stream consumer application. The Amazon S3 message data is then passed through a processing pipeline built on Amazon EMR running scheduled PySpark jobs.
The data platform team manages data processing and is concerned about the efficiency and cost of downstream data processing. They want to continue to use PySpark.
Which solution improves the efficiency of the data processing jobs and is well architected?

1. A. Send the sensor and devices data directly to a Kinesis Data Firehose delivery stream to send the data to Amazon S3 with Apache Parquet record format conversion enable
2. B. Use Amazon EMR running PySpark to process the data in Amazon S3.
3. C. Set up an AWS Lambda function with a Python runtime environmen
4. D. Process individual Kinesis data stream messages from the connected devices and sensors using Lambda.
5. E. Launch an Amazon Redshift cluste
6. F. Copy the collected data from Amazon S3 to Amazon Redshift and move the data processing jobs from Amazon EMR to Amazon Redshift.
7. G. Set up AWS Glue Python jobs to merge the small data files in Amazon S3 into larger files and transform them to Apache Parquet forma
8. H. Migrate the downstream PySpark jobs from Amazon EMR to AWS Glue.

Correct Answer: D
https://aws.amazon.com/it/about-aws/whats-new/2020/04/aws-glue-now-supports-serverless-streaming-etl/