DAS-C01 Free Practice Test

A large ride-sharing company has thousands of drivers globally serving millions of unique customers every day. The company has decided to migrate an existing data mart to Amazon Redshift. The existing schema includes the following tables.
A trips fact table for information on completed rides. A drivers dimension table for driver profiles. A customers fact table holding customer profile information.
The company analyzes trip details by date and destination to examine profitability by region. The drivers data rarely changes. The customers data frequently changes.
What table design provides optimal query performance?

1. A. Use DISTSTYLE KEY (destination) for the trips table and sort by dat
2. B. Use DISTSTYLE ALL for the drivers and customers tables.
3. C. Use DISTSTYLE EVEN for the trips table and sort by dat
4. D. Use DISTSTYLE ALL for the drivers table.Use DISTSTYLE EVEN for the customers table.
5. E. Use DISTSTYLE KEY (destination) for the trips table and sort by dat
6. F. Use DISTSTYLE ALL for the drivers tabl
7. G. Use DISTSTYLE EVEN for the customers table.
8. H. Use DISTSTYLE EVEN for the drivers table and sort by dat
9. I. Use DISTSTYLE ALL for both fact tables.

Correct Answer: C
https://www.matillion.com/resources/blog/aws-redshift-performance-choosing-the-right-distribution-styles/#:~:t https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-best-dist-key.html

An airline has .csv-formatted data stored in Amazon S3 with an AWS Glue Data Catalog. Data analysts want to join this data with call center data stored in Amazon Redshift as part of a dally batch process. The Amazon Redshift cluster is already under a heavy load. The solution must be managed, serverless, well-functioning, and minimize the load on the existing Amazon Redshift cluster. The solution should also require minimal effort and development activity.
Which solution meets these requirements?

1. A. Unload the call center data from Amazon Redshift to Amazon S3 using an AWS Lambda function.Perform the join with AWS Glue ETL scripts.
2. B. Export the call center data from Amazon Redshift using a Python shell in AWS Glu
3. C. Perform the join with AWS Glue ETL scripts.
4. D. Create an external table using Amazon Redshift Spectrum for the call center data and perform the join with Amazon Redshift.
5. E. Export the call center data from Amazon Redshift to Amazon EMR using Apache Sqoo
6. F. Perform the join with Apache Hive.

Correct Answer: C
https://docs.aws.amazon.com/redshift/latest/dg/c-spectrum-external-tables.html

A technology company is creating a dashboard that will visualize and analyze time-sensitive data. The data will come in through Amazon Kinesis Data Firehose with the butter interval set to 60 seconds. The dashboard must support near-real-time data.
Which visualization solution will meet these requirements?

1. A. Select Amazon Elasticsearch Service (Amazon ES) as the endpoint for Kinesis Data Firehos
2. B. Set up a Kibana dashboard using the data in Amazon ES with the desired analyses and visualizations.
3. C. Select Amazon S3 as the endpoint for Kinesis Data Firehos
4. D. Read data into an Amazon SageMaker Jupyter notebook and carry out the desired analyses and visualizations.
5. E. Select Amazon Redshift as the endpoint for Kinesis Data Firehos
6. F. Connect Amazon QuickSight with SPICE to Amazon Redshift to create the desired analyses and visualizations.
7. G. Select Amazon S3 as the endpoint for Kinesis Data Firehos
8. H. Use AWS Glue to catalog the data and Amazon Athena to query i
9. I. Connect Amazon QuickSight with SPICE to Athena to create the desired analyses and visualizations.

Correct Answer: A

A regional energy company collects voltage data from sensors attached to buildings. To address any known dangerous conditions, the company wants to be alerted when a sequence of two voltage drops is detected within 10 minutes of a voltage spike at the same building. It is important to ensure that all messages are delivered as quickly as possible. The system must be fully managed and highly available. The company also needs a solution that will automatically scale up as it covers additional cites with this monitoring feature. The alerting system is subscribed to an Amazon SNS topic for remediation.
Which solution meets these requirements?

1. A. Create an Amazon Managed Streaming for Kafka cluster to ingest the data, and use an Apache Spark Streaming with Apache Kafka consumer API in an automatically scaled Amazon EMR cluster to process the incoming dat
2. B. Use the Spark Streaming application to detect the known event sequence and send the SNS message.
3. C. Create a REST-based web service using Amazon API Gateway in front of an AWS Lambda function.Create an Amazon RDS for PostgreSQL database with sufficient Provisioned IOPS (PIOPS). In the Lambda function, store incoming events in the RDS database and query the latest data to detect the known event sequence and send the SNS message.
4. D. Create an Amazon Kinesis Data Firehose delivery stream to capture the incoming sensor dat
5. E. Use an AWS Lambda transformation function to detect the known event sequence and send the SNS message.
6. F. Create an Amazon Kinesis data stream to capture the incoming sensor data and create another stream for alert message
7. G. Set up AWS Application Auto Scaling on bot
8. H. Create a Kinesis Data Analytics for Java application to detect the known event sequence, and add a message to the message strea
9. I. Configure an AWS Lambda function to poll the message stream and publish to the SNS topic.

Correct Answer: D

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 previouslytrained 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