DAS-C01 Free Practice Test

A company needs to store objects containing log data in JSON format. The objects are generated by eight applications running in AWS. Six of the applications generate a total of 500 KiB of data per second, and two of the applications can generate up to 2 MiB of data per second.
A data engineer wants to implement a scalable solution to capture and store usage data in an Amazon S3
bucket. The usage data objects need to be reformatted, converted to .csv format, and then compressed before they are stored in Amazon S3. The company requires the solution to include the least custom code possible and has authorized the data engineer to request a service quota increase if needed.
Which solution meets these requirements?

1. A. Configure an Amazon Kinesis Data Firehose delivery stream for each applicatio
2. B. Write AWS Lambda functions to read log data objects from the stream for each applicatio
3. C. Have the function perform reformatting and .csv conversio
4. D. Enable compression on all the delivery streams.
5. E. Configure an Amazon Kinesis data stream with one shard per applicatio
6. F. Write an AWS Lambda function to read usage data objects from the shard
7. G. Have the function perform .csv conversion, reformatting, and compression of the dat
8. H. Have the function store the output in Amazon S3.
9. I. Configure an Amazon Kinesis data stream for each applicatio
10. J. Write an AWS Lambda function to read usage data objects from the stream for each applicatio
11. K. Have the function perform .csv conversion, reformatting, and compression of the dat
12. L. Have the function store the output in Amazon S3.
13. M. Store usage data objects in an Amazon DynamoDB tabl
14. N. Configure a DynamoDB stream to copy the objects to an S3 bucke
15. O. Configure an AWS Lambda function to be triggered when objects are written to the S3 bucke
16. P. Have the function convert the objects into .csv format.

Correct Answer: A

A media analytics company consumes a stream of social media posts. The posts are sent to an Amazon Kinesis data stream partitioned on user_id. An AWS Lambda function retrieves the records and validates the content before loading the posts into an Amazon Elasticsearch cluster. The validation process needs to receive the posts for a given user in the order they were received. A data analyst has noticed that, during peak hours, the social media platform posts take more than an hour to appear in the Elasticsearch cluster.
What should the data analyst do reduce this latency?

1. A. Migrate the validation process to Amazon Kinesis Data Firehose.
2. B. Migrate the Lambda consumers from standard data stream iterators to an HTTP/2 stream consumer.
3. C. Increase the number of shards in the stream.
4. D. Configure multiple Lambda functions to process the stream.

Correct Answer: D

A company wants to enrich application logs in near-real-time and use the enriched dataset for further analysis. The application is running on Amazon EC2 instances across multiple Availability Zones and storing its logs using Amazon CloudWatch Logs. The enrichment source is stored in an Amazon DynamoDB table.
Which solution meets the requirements for the event collection and enrichment?

1. A. Use a CloudWatch Logs subscription to send the data to Amazon Kinesis Data Firehos
2. B. Use AWS Lambda to transform the data in the Kinesis Data Firehose delivery stream and enrich it with the data in the DynamoDB tabl
3. C. Configure Amazon S3 as the Kinesis Data Firehose delivery destination.
4. D. Export the raw logs to Amazon S3 on an hourly basis using the AWS CL
5. E. Use AWS Glue crawlers to catalog the log
6. F. Set up an AWS Glue connection for the DynamoDB table and set up an AWS Glue ETL job to enrich the dat
7. G. Store the enriched data in Amazon S3.
8. H. Configure the application to write the logs locally and use Amazon Kinesis Agent to send the data to Amazon Kinesis Data Stream
9. I. Configure a Kinesis Data Analytics SQL application with the Kinesis data stream as the sourc
10. J. Join the SQL application input stream with DynamoDB records, and then store the enriched output stream in Amazon S3 using Amazon Kinesis Data Firehose.
11. K. Export the raw logs to Amazon S3 on an hourly basis using the AWS CL
12. L. Use Apache Spark SQL on Amazon EMR to read the logs from Amazon S3 and enrich the records with the data from DynamoD
13. M. Store the enriched data in Amazon S3.

Correct Answer: A
https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters.html#FirehoseExample

An online retail company is migrating its reporting system to AWS. The company’s legacy system runs data processing on online transactions using a complex series of nested Apache Hive queries. Transactional data is exported from the online system to the reporting system several times a day. Schemas in the files are stable between updates.
A data analyst wants to quickly migrate the data processing to AWS, so any code changes should be minimized. To keep storage costs low, the data analyst decides to store the data in Amazon S3. It is vital that the data from the reports and associated analytics is completely up to date based on the data in Amazon S3.
Which solution meets these requirements?

1. A. Create an AWS Glue Data Catalog to manage the Hive metadat
2. B. Create an AWS Glue crawler over Amazon S3 that runs when data is refreshed to ensure that data changes are update
3. C. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.
4. D. Create an AWS Glue Data Catalog to manage the Hive metadat
5. E. Create an Amazon EMR cluster with consistent view enable
6. F. Run emrfs sync before each analytics step to ensure data changes are update
7. G. Create an EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.
8. H. Create an Amazon Athena table with CREATE TABLE AS SELECT (CTAS) to ensure data is refreshed from underlying queries against the raw datase
9. I. Create an AWS Glue Data Catalog to manage the Hive metadata over the CTAS tabl
10. J. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.
11. K. Use an S3 Select query to ensure that the data is properly update
12. L. Create an AWS Glue Data Catalog to manage the Hive metadata over the S3 Select tabl
13. M. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.

Correct Answer: A

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