Free Amazon DAS-C01 Exam Questions

Question 1

A medical company has a system with sensor devices that read metrics and send them in real time to an Amazon Kinesis data stream. The Kinesis data stream has multiple shards. The company needs to calculate the average value of a numeric metric every second and set an alarm for whenever the value is above one threshold or below another threshold. The alarm must be sent to Amazon Simple Notification Service (Amazon SNS) in less than 30 seconds.
Which architecture meets these requirements?

A : an Amazon Kinesis Data Firehose delivery stream to read the data from the Kinesis data stream with an AWS Lambda transformation function that calculates the average per second and sends the alarm to Amazon SNS.

B : an AWS Lambda function to read from the Kinesis data stream to calculate the average per second and sent the alarm to Amazon SNS.

C : an Amazon Kinesis Data Firehose deliver stream to read the data from the Kinesis data stream and store it on Amazon S3. Have Amazon S3 trigger an AWS Lambda function that calculates the average per second and sends the alarm to Amazon SNS.

D : an Amazon Kinesis Data Analytics application to read from the Kinesis data stream and calculate the average per second. Send the results to an AWS Lambda function that sends the alarm to Amazon SNS.

Answer: D

Question 2

An IOT company is collecting data from multiple sensors and is streaming the data to Amazon Managed Streaming for Apache Kafka (Amazon MSK). Each sensor type has its own topic, and each topic has the same number of partitions. The company is planning to turn on more sensors. However, the company wants to evaluate which sensor types are producing the most data sothat the company can scale accordingly. The company needs to know which sensor types have the largest values for the following metrics: ByteslnPerSec and MessageslnPerSec. Which level of monitoring for Amazon MSK will meet these requirements?

A : DEFAULT level

B : PER TOPIC PER BROKER level

C : PER BROKER level

D : PER TOPIC level

Answer: B

Question 3

A company has a marketing department and a finance department. The departments are storing data in Amazon S3 in their own AWS accounts in AWS Organizations. Both departments use AWS Lake Formation to catalog and secure their dat
a. The departments have some databases and tables that share common names.
The marketing department needs to securely access some tables from the finance department.
Which two steps are required for this process? (Choose two.)

A : finance department grants Lake Formation permissions for the tables to the external account for the marketing department.

B : finance department creates cross-account IAM permissions to the table for the marketing department role.

C : marketing department creates an IAM role that has permissions to the Lake Formation tables.

Answer: A,B

Question 4

A data analyst is designing an Amazon QuickSight dashboard using centralized sales data that resides in Amazon Redshift. The dashboard must be restricted so that a salesperson in Sydney. Australia, can see only the Australia view and that a salesperson in New York can see only United States (US) data.

What should the data analyst do to ensure the appropriate data security is in place?

A : ace the data sources for Australia and the US into separate SPICE capacity pools.

B : t up an Amazon Redshift VPC security group for Australia and the US.

C : ploy QuickSight Enterprise edition to implement row-level security (RLS) to the sales table.

D : ploy QuickSight Enterprise edition and set up different VPC security groups for Australia and the US.

Answer: D

Question 5

A company wants to collect and process events data from different departments in near-real time. Before storing the data in Amazon S3, the company needs to clean the data by standardizing the format of the address and timestamp columns. The data varies in size based on the overall load at each particular point in time. A single data record can be 100 KB-10 MB.
How should a data analytics specialist design the solution for data ingestion?

A : Amazon Kinesis Data Streams. Configure a stream for the raw data. Use a Kinesis Agent to write data to the stream. Create an Amazon Kinesis Data Analytics application that reads data from the raw stream, cleanses it, and stores the output to Amazon S3.

B : Amazon Kinesis Data Firehose. Configure a Firehose delivery stream with a preprocessing AWS Lambda function for data cleansing. Use a Kinesis Agent to write data to the delivery stream. Configure Kinesis Data Firehose to deliver the data to Amazon S3.

C : Amazon Managed Streaming for Apache Kafka. Configure a topic for the raw data. Use a Kafka producer to write data to the topic. Create an application on Amazon EC2 that reads data from the topic by using the Apache Kafka consumer API, cleanses the data, and writes to Amazon S3.

D : Amazon Simple Queue Service (Amazon SQS). Configure an AWS Lambda function to read events from the SQS queue and upload the events to Amazon S3.

Answer: B