A utility company wants to visualize data for energy usage on a daily basis in Amazon QuickSight A data analytics specialist at the company has built a data pipeline to collect and ingest the data into Amazon S3 Each day the data is stored in an individual csv file in an S3 bucket This is an example of the naming structure 20210707_datacsv 20210708_datacsv
To allow for data querying in QuickSight through Amazon Athena the specialist used an AWS Glue crawler to create a table with the path "s3 //powertransformer/20210707_data csv" However when the data is queried, it returns zero rows
How can this issue be resolved?

1. A. Modify the IAM policy for the AWS Glue crawler to access Amazon S3.
2. B. Ingest the files again.
3. C. Store the files in Apache Parquet format.
4. D. Update the table path to "s3://powertransformer/".

Correct Answer: D

A retail company wants to use Amazon QuickSight to generate dashboards for web and in-store sales. A group of 50 business intelligence professionals will develop and use the dashboards. Once ready, the dashboards will be shared with a group of 1,000 users.
The sales data comes from different stores and is uploaded to Amazon S3 every 24 hours. The data is partitioned by year and month, and is stored in Apache Parquet format. The company is using the AWS Glue Data Catalog as its main data catalog and Amazon Athena for querying. The total size of the uncompressed data that the dashboards query from at any point is 200 GB.
Which configuration will provide the MOST cost-effective solution that meets these requirements?

1. A. Load the data into an Amazon Redshift cluster by using the COPY comman
2. B. Configure 50 author users and 1,000 reader user
3. C. Use QuickSight Enterprise editio
4. D. Configure an Amazon Redshift data source with a direct query option.
5. E. Use QuickSight Standard editio
6. F. Configure 50 author users and 1,000 reader user
7. G. Configure an Athena data source with a direct query option.
8. H. Use QuickSight Enterprise editio
9. I. Configure 50 author users and 1,000 reader user
10. J. Configure an Athena data source and import the data into SPIC
11. K. Automatically refresh every 24 hours.
12. L. Use QuickSight Enterprise editio
13. M. Configure 1 administrator and 1,000 reader user
14. N. Configure an S3 data source and import the data into SPIC
15. O. Automatically refresh every 24 hours.

Correct Answer: C

An airline has been collecting metrics on flight activities for analytics. A recently completed proof of concept demonstrates how the company provides insights to data analysts to improve on-time departures. The proof of concept used objects in Amazon S3, which contained the metrics in .csv format, and used Amazon Athena for querying the data. As the amount of data increases, the data analyst wants to optimize the storage solution to improve query performance.
Which options should the data analyst use to improve performance as the data lake grows? (Choose three.)

1. A. Add a randomized string to the beginning of the keys in S3 to get more throughput across partitions.
2. B. Use an S3 bucket in the same account as Athena.
3. C. Compress the objects to reduce the data transfer I/O.
4. D. Use an S3 bucket in the same Region as Athena.
5. E. Preprocess the .csv data to JSON to reduce I/O by fetching only the document keys needed by the query.
6. F. Preprocess the .csv data to Apache Parquet to reduce I/O by fetching only the data blocks needed forpredicate

Correct Answer: CDF
https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/

A marketing company is storing its campaign response data in Amazon S3. A consistent set of sources has generated the data for each campaign. The data is saved into Amazon S3 as .csv files. A business analyst will use Amazon Athena to analyze each campaign’s data. The company needs the cost of ongoing data analysis with Athena to be minimized.
Which combination of actions should a data analytics specialist take to meet these requirements? (Choose two.)

1. A. Convert the .csv files to Apache Parquet.
2. B. Convert the .csv files to Apache Avro.
3. C. Partition the data by campaign.
4. D. Partition the data by source.
5. E. Compress the .csv files.

Correct Answer: AC
https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/

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"